Dynamic modeling of gearbox faults: A review

NASA Astrophysics Data System (ADS)

Liang, Xihui; Zuo, Ming J.; Feng, Zhipeng

2018-01-01

Gearbox is widely used in industrial and military applications. Due to high service load, harsh operating conditions or inevitable fatigue, faults may develop in gears. If the gear faults cannot be detected early, the health will continue to degrade, perhaps causing heavy economic loss or even catastrophe. Early fault detection and diagnosis allows properly scheduled shutdowns to prevent catastrophic failure and consequently result in a safer operation and higher cost reduction. Recently, many studies have been done to develop gearbox dynamic models with faults aiming to understand gear fault generation mechanism and then develop effective fault detection and diagnosis methods. This paper focuses on dynamics based gearbox fault modeling, detection and diagnosis. State-of-art and challenges are reviewed and discussed. This detailed literature review limits research results to the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and fault diagnosis techniques, gearbox transmission path modeling and method validation. In the end, a summary and some research prospects are presented.

On Identifiability of Bias-Type Actuator-Sensor Faults in Multiple-Model-Based Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2012-01-01

This paper explores a class of multiple-model-based fault detection and identification (FDI) methods for bias-type faults in actuators and sensors. These methods employ banks of Kalman-Bucy filters to detect the faults, determine the fault pattern, and estimate the fault values, wherein each Kalman-Bucy filter is tuned to a different failure pattern. Necessary and sufficient conditions are presented for identifiability of actuator faults, sensor faults, and simultaneous actuator and sensor faults. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have biases.

Space shuttle main engine fault detection using neural networks

NASA Technical Reports Server (NTRS)

Bishop, Thomas; Greenwood, Dan; Shew, Kenneth; Stevenson, Fareed

1991-01-01

A method for on-line Space Shuttle Main Engine (SSME) anomaly detection and fault typing using a feedback neural network is described. The method involves the computation of features representing time-variance of SSME sensor parameters, using historical test case data. The network is trained, using backpropagation, to recognize a set of fault cases. The network is then able to diagnose new fault cases correctly. An essential element of the training technique is the inclusion of randomly generated data along with the real data, in order to span the entire input space of potential non-nominal data.

Modeling of a latent fault detector in a digital system

NASA Technical Reports Server (NTRS)

Nagel, P. M.

1978-01-01

Methods of modeling the detection time or latency period of a hardware fault in a digital system are proposed that explain how a computer detects faults in a computational mode. The objectives were to study how software reacts to a fault, to account for as many variables as possible affecting detection and to forecast a given program's detecting ability prior to computation. A series of experiments were conducted on a small emulated microprocessor with fault injection capability. Results indicate that the detecting capability of a program largely depends on the instruction subset used during computation and the frequency of its use and has little direct dependence on such variables as fault mode, number set, degree of branching and program length. A model is discussed which employs an analog with balls in an urn to explain the rate of which subsequent repetitions of an instruction or instruction set detect a given fault.

A testing-coverage software reliability model considering fault removal efficiency and error generation.

PubMed

Li, Qiuying; Pham, Hoang

2017-01-01

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance.

Software-implemented fault insertion: An FTMP example

NASA Technical Reports Server (NTRS)

Czeck, Edward W.; Siewiorek, Daniel P.; Segall, Zary Z.

1987-01-01

This report presents a model for fault insertion through software; describes its implementation on a fault-tolerant computer, FTMP; presents a summary of fault detection, identification, and reconfiguration data collected with software-implemented fault insertion; and compares the results to hardware fault insertion data. Experimental results show detection time to be a function of time of insertion and system workload. For the fault detection time, there is no correlation between software-inserted faults and hardware-inserted faults; this is because hardware-inserted faults must manifest as errors before detection, whereas software-inserted faults immediately exercise the error detection mechanisms. In summary, the software-implemented fault insertion is able to be used as an evaluation technique for the fault-handling capabilities of a system in fault detection, identification and recovery. Although the software-inserted faults do not map directly to hardware-inserted faults, experiments show software-implemented fault insertion is capable of emulating hardware fault insertion, with greater ease and automation.

A testing-coverage software reliability model considering fault removal efficiency and error generation

PubMed Central

Li, Qiuying; Pham, Hoang

2017-01-01

In this paper, we propose a software reliability model that considers not only error generation but also fault removal efficiency combined with testing coverage information based on a nonhomogeneous Poisson process (NHPP). During the past four decades, many software reliability growth models (SRGMs) based on NHPP have been proposed to estimate the software reliability measures, most of which have the same following agreements: 1) it is a common phenomenon that during the testing phase, the fault detection rate always changes; 2) as a result of imperfect debugging, fault removal has been related to a fault re-introduction rate. But there are few SRGMs in the literature that differentiate between fault detection and fault removal, i.e. they seldom consider the imperfect fault removal efficiency. But in practical software developing process, fault removal efficiency cannot always be perfect, i.e. the failures detected might not be removed completely and the original faults might still exist and new faults might be introduced meanwhile, which is referred to as imperfect debugging phenomenon. In this study, a model aiming to incorporate fault introduction rate, fault removal efficiency and testing coverage into software reliability evaluation is developed, using testing coverage to express the fault detection rate and using fault removal efficiency to consider the fault repair. We compare the performance of the proposed model with several existing NHPP SRGMs using three sets of real failure data based on five criteria. The results exhibit that the model can give a better fitting and predictive performance. PMID:28750091

Fault detection of Tennessee Eastman process based on topological features and SVM

NASA Astrophysics Data System (ADS)

Zhao, Huiyang; Hu, Yanzhu; Ai, Xinbo; Hu, Yu; Meng, Zhen

2018-03-01

Fault detection in industrial process is a popular research topic. Although the distributed control system(DCS) has been introduced to monitor the state of industrial process, it still cannot satisfy all the requirements for fault detection of all the industrial systems. In this paper, we proposed a novel method based on topological features and support vector machine(SVM), for fault detection of industrial process. The proposed method takes global information of measured variables into account by complex network model and predicts whether a system has generated some faults or not by SVM. The proposed method can be divided into four steps, i.e. network construction, network analysis, model training and model testing respectively. Finally, we apply the model to Tennessee Eastman process(TEP). The results show that this method works well and can be a useful supplement for fault detection of industrial process.

Robust Fault Detection for Aircraft Using Mixed Structured Singular Value Theory and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G.

2000-01-01

The purpose of fault detection is to identify when a fault or failure has occurred in a system such as an aircraft or expendable launch vehicle. The faults may occur in sensors, actuators, structural components, etc. One of the primary approaches to model-based fault detection relies on analytical redundancy. That is the output of a computer-based model (actually a state estimator) is compared with the sensor measurements of the actual system to determine when a fault has occurred. Unfortunately, the state estimator is based on an idealized mathematical description of the underlying plant that is never totally accurate. As a result of these modeling errors, false alarms can occur. This research uses mixed structured singular value theory, a relatively recent and powerful robustness analysis tool, to develop robust estimators and demonstrates the use of these estimators in fault detection. To allow qualitative human experience to be effectively incorporated into the detection process fuzzy logic is used to predict the seriousness of the fault that has occurred.

Enhanced data validation strategy of air quality monitoring network.

PubMed

Harkat, Mohamed-Faouzi; Mansouri, Majdi; Nounou, Mohamed; Nounou, Hazem

2018-01-01

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement). Copyright © 2017 Elsevier Inc. All rights reserved.

Effectiveness of back-to-back testing

NASA Technical Reports Server (NTRS)

Vouk, Mladen A.; Mcallister, David F.; Eckhardt, David E.; Caglayan, Alper; Kelly, John P. J.

1987-01-01

Three models of back-to-back testing processes are described. Two models treat the case where there is no intercomponent failure dependence. The third model describes the more realistic case where there is correlation among the failure probabilities of the functionally equivalent components. The theory indicates that back-to-back testing can, under the right conditions, provide a considerable gain in software reliability. The models are used to analyze the data obtained in a fault-tolerant software experiment. It is shown that the expected gain is indeed achieved, and exceeded, provided the intercomponent failure dependence is sufficiently small. However, even with the relatively high correlation the use of several functionally equivalent components coupled with back-to-back testing may provide a considerable reliability gain. Implications of this finding are that the multiversion software development is a feasible and cost effective approach to providing highly reliable software components intended for fault-tolerant software systems, on condition that special attention is directed at early detection and elimination of correlated faults.

Implementation of a model based fault detection and diagnosis for actuation faults of the Space Shuttle main engine

NASA Technical Reports Server (NTRS)

Duyar, A.; Guo, T.-H.; Merrill, W.; Musgrave, J.

1992-01-01

In a previous study, Guo, Merrill and Duyar, 1990, reported a conceptual development of a fault detection and diagnosis system for actuation faults of the space shuttle main engine. This study, which is a continuation of the previous work, implements the developed fault detection and diagnosis scheme for the real time actuation fault diagnosis of the space shuttle main engine. The scheme will be used as an integral part of an intelligent control system demonstration experiment at NASA Lewis. The diagnosis system utilizes a model based method with real time identification and hypothesis testing for actuation, sensor, and performance degradation faults.

Temporal evolution of fault systems in the Upper Jurassic of the Central German Molasse Basin: case study Unterhaching

NASA Astrophysics Data System (ADS)

Budach, Ingmar; Moeck, Inga; Lüschen, Ewald; Wolfgramm, Markus

2018-03-01

The structural evolution of faults in foreland basins is linked to a complex basin history ranging from extension to contraction and inversion tectonics. Faults in the Upper Jurassic of the German Molasse Basin, a Cenozoic Alpine foreland basin, play a significant role for geothermal exploration and are therefore imaged, interpreted and studied by 3D seismic reflection data. Beyond this applied aspect, the analysis of these seismic data help to better understand the temporal evolution of faults and respective stress fields. In 2009, a 27 km2 3D seismic reflection survey was conducted around the Unterhaching Gt 2 well, south of Munich. The main focus of this study is an in-depth analysis of a prominent v-shaped fault block structure located at the center of the 3D seismic survey. Two methods were used to study the periodic fault activity and its relative age of the detected faults: (1) horizon flattening and (2) analysis of incremental fault throws. Slip and dilation tendency analyses were conducted afterwards to determine the stresses resolved on the faults in the current stress field. Two possible kinematic models explain the structural evolution: One model assumes a left-lateral strike slip fault in a transpressional regime resulting in a positive flower structure. The other model incorporates crossing conjugate normal faults within a transtensional regime. The interpreted successive fault formation prefers the latter model. The episodic fault activity may enhance fault zone permeability hence reservoir productivity implying that the analysis of periodically active faults represents an important part in successfully targeting geothermal wells.

Fault Detection for Automotive Shock Absorber

NASA Astrophysics Data System (ADS)

Hernandez-Alcantara, Diana; Morales-Menendez, Ruben; Amezquita-Brooks, Luis

2015-11-01

Fault detection for automotive semi-active shock absorbers is a challenge due to the non-linear dynamics and the strong influence of the disturbances such as the road profile. First obstacle for this task, is the modeling of the fault, which has been shown to be of multiplicative nature. Many of the most widespread fault detection schemes consider additive faults. Two model-based fault algorithms for semiactive shock absorber are compared: an observer-based approach and a parameter identification approach. The performance of these schemes is validated and compared using a commercial vehicle model that was experimentally validated. Early results shows that a parameter identification approach is more accurate, whereas an observer-based approach is less sensible to parametric uncertainty.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids.

PubMed

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-04-28

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability.

Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids

PubMed Central

Shao, Sujie; Guo, Shaoyong; Qiu, Xuesong

2017-01-01

Due to the increasingly important role in monitoring and data collection that sensors play, accurate and timely fault detection is a key issue for wireless sensor networks (WSNs) in smart grids. This paper presents a novel distributed fault detection mechanism for WSNs based on credibility and cooperation. Firstly, a reasonable credibility model of a sensor is established to identify any suspicious status of the sensor according to its own temporal data correlation. Based on the credibility model, the suspicious sensor is then chosen to launch fault diagnosis requests. Secondly, the sending time of fault diagnosis request is discussed to avoid the transmission overhead brought about by unnecessary diagnosis requests and improve the efficiency of fault detection based on neighbor cooperation. The diagnosis reply of a neighbor sensor is analyzed according to its own status. Finally, to further improve the accuracy of fault detection, the diagnosis results of neighbors are divided into several classifications to judge the fault status of the sensors which launch the fault diagnosis requests. Simulation results show that this novel mechanism can achieve high fault detection ratio with a small number of fault diagnoses and low data congestion probability. PMID:28452925

Hideen Markov Models and Neural Networks for Fault Detection in Dynamic Systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

None given. (From conclusion): Neural networks plus Hidden Markov Models(HMM)can provide excellene detection and false alarm rate performance in fault detection applications. Modified models allow for novelty detection. Also covers some key contributions of neural network model, and application status.

Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme

NASA Technical Reports Server (NTRS)

Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong

2011-01-01

A method detects and isolates in-flight sensor, actuator, and component faults for advanced propulsion systems. In sharp contrast to many conventional methods, which deal with either sensor fault or component fault, but not both, this method considers sensor fault, actuator fault, and component fault under one systemic and unified framework. The proposed solution consists of two main components: a bank of real-time, nonlinear adaptive fault diagnostic estimators for residual generation, and a residual evaluation module that includes adaptive thresholds and a Transferable Belief Model (TBM)-based residual evaluation scheme. By employing a nonlinear adaptive learning architecture, the developed approach is capable of directly dealing with nonlinear engine models and nonlinear faults without the need of linearization. Software modules have been developed and evaluated with the NASA C-MAPSS engine model. Several typical engine-fault modes, including a subset of sensor/actuator/components faults, were tested with a mild transient operation scenario. The simulation results demonstrated that the algorithm was able to successfully detect and isolate all simulated faults as long as the fault magnitudes were larger than the minimum detectable/isolable sizes, and no misdiagnosis occurred

A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults.

PubMed

Sun, Rui; Cheng, Qi; Wang, Guanyu; Ochieng, Washington Yotto

2017-09-29

The use of Unmanned Aerial Vehicles (UAVs) has increased significantly in recent years. On-board integrated navigation sensors are a key component of UAVs' flight control systems and are essential for flight safety. In order to ensure flight safety, timely and effective navigation sensor fault detection capability is required. In this paper, a novel data-driven Adaptive Neuron Fuzzy Inference System (ANFIS)-based approach is presented for the detection of on-board navigation sensor faults in UAVs. Contrary to the classic UAV sensor fault detection algorithms, based on predefined or modelled faults, the proposed algorithm combines an online data training mechanism with the ANFIS-based decision system. The main advantages of this algorithm are that it allows real-time model-free residual analysis from Kalman Filter (KF) estimates and the ANFIS to build a reliable fault detection system. In addition, it allows fast and accurate detection of faults, which makes it suitable for real-time applications. Experimental results have demonstrated the effectiveness of the proposed fault detection method in terms of accuracy and misdetection rate.

PV Systems Reliability Final Technical Report: Ground Fault Detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lavrova, Olga; Flicker, Jack David; Johnson, Jay

We have examined ground faults in PhotoVoltaic (PV) arrays and the efficacy of fuse, current detection (RCD), current sense monitoring/relays (CSM), isolation/insulation (Riso) monitoring, and Ground Fault Detection and Isolation (GFID) using simulations based on a Simulation Program with Integrated Circuit Emphasis SPICE ground fault circuit model, experimental ground faults installed on real arrays, and theoretical equations.

A Kalman Filter Based Technique for Stator Turn-Fault Detection of the Induction Motors

NASA Astrophysics Data System (ADS)

Ghanbari, Teymoor; Samet, Haidar

2017-11-01

Monitoring of the Induction Motors (IMs) through stator current for different faults diagnosis has considerable economic and technical advantages in comparison with the other techniques in this content. Among different faults of an IM, stator and bearing faults are more probable types, which can be detected by analyzing signatures of the stator currents. One of the most reliable indicators for fault detection of IMs is lower sidebands of power frequency in the stator currents. This paper deals with a novel simple technique for detecting stator turn-fault of the IMs. Frequencies of the lower sidebands are determined using the motor specifications and their amplitudes are estimated by a Kalman Filter (KF). Instantaneous Total Harmonic Distortion (ITHD) of these harmonics is calculated. Since variation of the ITHD for the three-phase currents is considerable in case of stator turn-fault, the fault can be detected using this criterion, confidently. Different simulation results verify high performance of the proposed method. The performance of the method is also confirmed using some experiments.

Minimum entropy deconvolution optimized sinusoidal synthesis and its application to vibration based fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; Zhao, Qing

2017-03-01

In this paper, a minimum entropy deconvolution based sinusoidal synthesis (MEDSS) filter is proposed to improve the fault detection performance of the regular sinusoidal synthesis (SS) method. The SS filter is an efficient linear predictor that exploits the frequency properties during model construction. The phase information of the harmonic components is not used in the regular SS filter. However, the phase relationships are important in differentiating noise from characteristic impulsive fault signatures. Therefore, in this work, the minimum entropy deconvolution (MED) technique is used to optimize the SS filter during the model construction process. A time-weighted-error Kalman filter is used to estimate the MEDSS model parameters adaptively. Three simulation examples and a practical application case study are provided to illustrate the effectiveness of the proposed method. The regular SS method and the autoregressive MED (ARMED) method are also implemented for comparison. The MEDSS model has demonstrated superior performance compared to the regular SS method and it also shows comparable or better performance with much less computational intensity than the ARMED method.

Detection and Modeling of High-Dimensional Thresholds for Fault Detection and Diagnosis

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

Many Fault Detection and Diagnosis (FDD) systems use discrete models for detection and reasoning. To obtain categorical values like oil pressure too high, analog sensor values need to be discretized using a suitablethreshold. Time series of analog and discrete sensor readings are processed and discretized as they come in. This task isusually performed by the wrapper code'' of the FDD system, together with signal preprocessing and filtering. In practice,selecting the right threshold is very difficult, because it heavily influences the quality of diagnosis. If a threshold causesthe alarm trigger even in nominal situations, false alarms will be the consequence. On the other hand, if threshold settingdoes not trigger in case of an off-nominal condition, important alarms might be missed, potentially causing hazardoussituations. In this paper, we will in detail describe the underlying statistical modeling techniques and algorithm as well as the Bayesian method for selecting the most likely shape and its parameters. Our approach will be illustrated by several examples from the Aerospace domain.

Robust Fault Detection and Isolation for Stochastic Systems

NASA Technical Reports Server (NTRS)

George, Jemin; Gregory, Irene M.

2010-01-01

This paper outlines the formulation of a robust fault detection and isolation scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves precise reconstruction of sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the robust fault detection and isolation system.

Robust fault detection of wind energy conversion systems based on dynamic neural networks.

PubMed

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

PubMed Central

Talebi, Nasser; Sadrnia, Mohammad Ali; Darabi, Ahmad

2014-01-01

Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate. PMID:24744774

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, Howard; Braun, James E.

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment inmore » the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.« less

Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm: October 1, 2014 -- May 5, 2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheung, Howard; Braun, James E.

2015-12-31

This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms. The aim of the project is to develop fault models of typical heating, ventilating and air conditioning (HVAC) equipment inmore » the United States, and the fault models in this report are grouped as control faults, sensor faults, packaged and split air conditioner faults, water-cooled chiller faults, and other uncategorized faults. The control fault models simulate impacts of inappropriate thermostat control schemes such as an incorrect thermostat set point in unoccupied hours and manual changes of thermostat set point due to extreme outside temperature. Sensor fault models focus on the modeling of sensor biases including economizer relative humidity sensor bias, supply air temperature sensor bias, and water circuit temperature sensor bias. Packaged and split air conditioner fault models simulate refrigerant undercharging, condenser fouling, condenser fan motor efficiency degradation, non-condensable entrainment in refrigerant, and liquid line restriction. Other fault models that are uncategorized include duct fouling, excessive infiltration into the building, and blower and pump motor degradation.« less

Detection of CMOS bridging faults using minimal stuck-at fault test sets

NASA Technical Reports Server (NTRS)

Ijaz, Nabeel; Frenzel, James F.

1993-01-01

The performance of minimal stuck-at fault test sets at detecting bridging faults are evaluated. New functional models of circuit primitives are presented which allow accurate representation of bridging faults under switch-level simulation. The effectiveness of the patterns is evaluated using both voltage and current testing.

Simplified Interval Observer Scheme: A New Approach for Fault Diagnosis in Instruments

PubMed Central

Martínez-Sibaja, Albino; Astorga-Zaragoza, Carlos M.; Alvarado-Lassman, Alejandro; Posada-Gómez, Rubén; Aguila-Rodríguez, Gerardo; Rodríguez-Jarquin, José P.; Adam-Medina, Manuel

2011-01-01

There are different schemes based on observers to detect and isolate faults in dynamic processes. In the case of fault diagnosis in instruments (FDI) there are different diagnosis schemes based on the number of observers: the Simplified Observer Scheme (SOS) only requires one observer, uses all the inputs and only one output, detecting faults in one detector; the Dedicated Observer Scheme (DOS), which again uses all the inputs and just one output, but this time there is a bank of observers capable of locating multiple faults in sensors, and the Generalized Observer Scheme (GOS) which involves a reduced bank of observers, where each observer uses all the inputs and m-1 outputs, and allows the localization of unique faults. This work proposes a new scheme named Simplified Interval Observer SIOS-FDI, which does not requires the measurement of any input and just with just one output allows the detection of unique faults in sensors and because it does not require any input, it simplifies in an important way the diagnosis of faults in processes in which it is difficult to measure all the inputs, as in the case of biologic reactors. PMID:22346593

Simulation of Co-Seismic Off-Fault Stress Effects: Influence of Fault Roughness and Pore Pressure Coupling

NASA Astrophysics Data System (ADS)

Fälth, B.; Lund, B.; Hökmark, H.

2017-12-01

Aiming at improved safety assessment of geological nuclear waste repositories, we use dynamic 3D earthquake simulations to estimate the potential for co-seismic off-fault distributed fracture slip. Our model comprises a 12.5 x 8.5 km strike-slip fault embedded in a full space continuum where we apply a homogeneous initial stress field. In the reference case (Case 1) the fault is planar and oriented optimally for slip, given the assumed stress field. To examine the potential impact of fault roughness, we also study cases where the fault surface has undulations with self-similar fractal properties. In both the planar and the undulated cases the fault has homogeneous frictional properties. In a set of ten rough fault models (Case 2), the fault friction is equal to that of Case 1, meaning that these models generate lower seismic moments than Case 1. In another set of ten rough fault models (Case 3), the fault dynamic friction is adjusted such that seismic moments on par with that of Case 1 are generated. For the propagation of the earthquake rupture we adopt the linear slip-weakening law and obtain Mw 6.4 in Case 1 and Case 3, and Mw 6.3 in Case 2 (35 % lower moment than Case 1). During rupture we monitor the off-fault stress evolution along the fault plane at 250 m distance and calculate the corresponding evolution of the Coulomb Failure Stress (CFS) on optimally oriented hypothetical fracture planes. For the stress-pore pressure coupling, we assume Skempton's coefficient B = 0.5 as a base case value, but also examine the sensitivity to variations of B. We observe the following: (I) The CFS values, and thus the potential for fracture slip, tend to increase with the distance from the hypocenter. This is in accordance with results by other authors. (II) The highest CFS values are generated by quasi-static stress concentrations around fault edges and around large scale fault bends, where we obtain values of the order of 10 MPa. (III) Locally, fault roughness may have a significant impact. The ratios (max CFS in Case 2) / (max CFS in Case 1) = 1.1 and (max CFS in Case 3) / (max CFS in Case 1) = 1.2 indicate a minor impact. However, at specific locations, CFS in Case 2 and Case 3 may be more than 5 times higher than in Case 1. (IV) The sensitivity to variations of B is modest; (max CFS in Case 1 with B = 0) / (max CFS in Case 1 with B = 1) = 1.15.

Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger

NASA Astrophysics Data System (ADS)

Habbi, Hacene; Kidouche, Madjid; Kinnaert, Michel; Zelmat, Mimoun

2011-04-01

This article addresses the design and real-time implementation of a fuzzy model-based fault detection and diagnosis (FDD) system for a pilot co-current heat exchanger. The design method is based on a three-step procedure which involves the identification of data-driven fuzzy rule-based models, the design of a fuzzy residual generator and the evaluation of the residuals for fault diagnosis using statistical tests. The fuzzy FDD mechanism has been implemented and validated on the real co-current heat exchanger, and has been proven to be efficient in detecting and isolating process, sensor and actuator faults.

Fault detection of helicopter gearboxes using the multi-valued influence matrix method

NASA Technical Reports Server (NTRS)

Chin, Hsinyung; Danai, Kourosh; Lewicki, David G.

1993-01-01

In this paper we investigate the effectiveness of a pattern classifying fault detection system that is designed to cope with the variability of fault signatures inherent in helicopter gearboxes. For detection, the measurements are monitored on-line and flagged upon the detection of abnormalities, so that they can be attributed to a faulty or normal case. As such, the detection system is composed of two components, a quantization matrix to flag the measurements, and a multi-valued influence matrix (MVIM) that represents the behavior of measurements during normal operation and at fault instances. Both the quantization matrix and influence matrix are tuned during a training session so as to minimize the error in detection. To demonstrate the effectiveness of this detection system, it was applied to vibration measurements collected from a helicopter gearbox during normal operation and at various fault instances. The results indicate that the MVIM method provides excellent results when the full range of faults effects on the measurements are included in the training set.

Model-Based Fault Tolerant Control

NASA Technical Reports Server (NTRS)

Kumar, Aditya; Viassolo, Daniel

2008-01-01

The Model Based Fault Tolerant Control (MBFTC) task was conducted under the NASA Aviation Safety and Security Program. The goal of MBFTC is to develop and demonstrate real-time strategies to diagnose and accommodate anomalous aircraft engine events such as sensor faults, actuator faults, or turbine gas-path component damage that can lead to in-flight shutdowns, aborted take offs, asymmetric thrust/loss of thrust control, or engine surge/stall events. A suite of model-based fault detection algorithms were developed and evaluated. Based on the performance and maturity of the developed algorithms two approaches were selected for further analysis: (i) multiple-hypothesis testing, and (ii) neural networks; both used residuals from an Extended Kalman Filter to detect the occurrence of the selected faults. A simple fusion algorithm was implemented to combine the results from each algorithm to obtain an overall estimate of the identified fault type and magnitude. The identification of the fault type and magnitude enabled the use of an online fault accommodation strategy to correct for the adverse impact of these faults on engine operability thereby enabling continued engine operation in the presence of these faults. The performance of the fault detection and accommodation algorithm was extensively tested in a simulation environment.

Latent component-based gear tooth fault detection filter using advanced parametric modeling

NASA Astrophysics Data System (ADS)

Ettefagh, M. M.; Sadeghi, M. H.; Rezaee, M.; Chitsaz, S.

2009-10-01

In this paper, a new parametric model-based filter is proposed for gear tooth fault detection. The designing of the filter consists of identifying the most proper latent component (LC) of the undamaged gearbox signal by analyzing the instant modules (IMs) and instant frequencies (IFs) and then using the component with lowest IM as the proposed filter output for detecting fault of the gearbox. The filter parameters are estimated by using the LC theory in which an advanced parametric modeling method has been implemented. The proposed method is applied on the signals, extracted from simulated gearbox for detection of the simulated gear faults. In addition, the method is used for quality inspection of the produced Nissan-Junior vehicle gearbox by gear profile error detection in an industrial test bed. For evaluation purpose, the proposed method is compared with the previous parametric TAR/AR-based filters in which the parametric model residual is considered as the filter output and also Yule-Walker and Kalman filter are implemented for estimating the parameters. The results confirm the high performance of the new proposed fault detection method.

Implementation of an Integrated On-Board Aircraft Engine Diagnostic Architecture

NASA Technical Reports Server (NTRS)

Armstrong, Jeffrey B.; Simon, Donald L.

2012-01-01

An on-board diagnostic architecture for aircraft turbofan engine performance trending, parameter estimation, and gas-path fault detection and isolation has been developed and evaluated in a simulation environment. The architecture incorporates two independent models: a realtime self-tuning performance model providing parameter estimates and a performance baseline model for diagnostic purposes reflecting long-term engine degradation trends. This architecture was evaluated using flight profiles generated from a nonlinear model with realistic fleet engine health degradation distributions and sensor noise. The architecture was found to produce acceptable estimates of engine health and unmeasured parameters, and the integrated diagnostic algorithms were able to perform correct fault isolation in approximately 70 percent of the tested cases

Structural heritage, reactivation and distribution of fault and fracture network in a rifting context: Case study of the western shoulder of the Upper Rhine Graben

NASA Astrophysics Data System (ADS)

Bertrand, Lionel; Jusseaume, Jessie; Géraud, Yves; Diraison, Marc; Damy, Pierre-Clément; Navelot, Vivien; Haffen, Sébastien

2018-03-01

In fractured reservoirs in the basement of extensional basins, fault and fracture parameters like density, spacing and length distribution are key properties for modelling and prediction of reservoir properties and fluids flow. As only large faults are detectable using basin-scale geophysical investigations, these fine-scale parameters need to be inferred from faults and fractures in analogous rocks at the outcrop. In this study, we use the western shoulder of the Upper Rhine Graben as an outcropping analogue of several deep borehole projects in the basement of the graben. Geological regional data, DTM (Digital Terrain Model) mapping and outcrop studies with scanlines are used to determine the spatial arrangement of the faults from the regional to the reservoir scale. The data shows that: 1) The fault network can be hierarchized in three different orders of scale and structural blocks with a characteristic structuration. This is consistent with other basement rocks studies in other rifting system allowing the extrapolation of the important parameters for modelling. 2) In the structural blocks, the fracture network linked to the faults is linked to the interplay between rock facies variation linked to the rock emplacement and the rifting event.

Switch failure diagnosis based on inductor current observation for boost converters

NASA Astrophysics Data System (ADS)

Jamshidpour, E.; Poure, P.; Saadate, S.

2016-09-01

Face to the growing number of applications using DC-DC power converters, the improvement of their reliability is subject to an increasing number of studies. Especially in safety critical applications, designing fault-tolerant converters is becoming mandatory. In this paper, a switch fault-tolerant DC-DC converter is studied. First, some of the fastest Fault Detection Algorithms (FDAs) are recalled. Then, a fast switch FDA is proposed which can detect both types of failures; open circuit fault as well as short circuit fault can be detected in less than one switching period. Second, a fault-tolerant converter which can be reconfigured under those types of fault is introduced. Hardware-In-the-Loop (HIL) results and experimental validations are given to verify the validity of the proposed switch fault-tolerant approach in the case of a single switch DC-DC boost converter with one redundant switch.

Gas Path On-line Fault Diagnostics Using a Nonlinear Integrated Model for Gas Turbine Engines

NASA Astrophysics Data System (ADS)

Lu, Feng; Huang, Jin-quan; Ji, Chun-sheng; Zhang, Dong-dong; Jiao, Hua-bin

2014-08-01

Gas turbine engine gas path fault diagnosis is closely related technology that assists operators in managing the engine units. However, the performance gradual degradation is inevitable due to the usage, and it result in the model mismatch and then misdiagnosis by the popular model-based approach. In this paper, an on-line integrated architecture based on nonlinear model is developed for gas turbine engine anomaly detection and fault diagnosis over the course of the engine's life. These two engine models have different performance parameter update rate. One is the nonlinear real-time adaptive performance model with the spherical square-root unscented Kalman filter (SSR-UKF) producing performance estimates, and the other is a nonlinear baseline model for the measurement estimates. The fault detection and diagnosis logic is designed to discriminate sensor fault and component fault. This integration architecture is not only aware of long-term engine health degradation but also effective to detect gas path performance anomaly shifts while the engine continues to degrade. Compared to the existing architecture, the proposed approach has its benefit investigated in the experiment and analysis.

Sensor Fault Detection and Diagnosis Simulation of a Helicopter Engine in an Intelligent Control Framework

NASA Technical Reports Server (NTRS)

Litt, Jonathan; Kurtkaya, Mehmet; Duyar, Ahmet

1994-01-01

This paper presents an application of a fault detection and diagnosis scheme for the sensor faults of a helicopter engine. The scheme utilizes a model-based approach with real time identification and hypothesis testing which can provide early detection, isolation, and diagnosis of failures. It is an integral part of a proposed intelligent control system with health monitoring capabilities. The intelligent control system will allow for accommodation of faults, reduce maintenance cost, and increase system availability. The scheme compares the measured outputs of the engine with the expected outputs of an engine whose sensor suite is functioning normally. If the differences between the real and expected outputs exceed threshold values, a fault is detected. The isolation of sensor failures is accomplished through a fault parameter isolation technique where parameters which model the faulty process are calculated on-line with a real-time multivariable parameter estimation algorithm. The fault parameters and their patterns can then be analyzed for diagnostic and accommodation purposes. The scheme is applied to the detection and diagnosis of sensor faults of a T700 turboshaft engine. Sensor failures are induced in a T700 nonlinear performance simulation and data obtained are used with the scheme to detect, isolate, and estimate the magnitude of the faults.

Structural Health and Prognostics Management for Offshore Wind Turbines: Sensitivity Analysis of Rotor Fault and Blade Damage with O&M Cost Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Myrent, Noah J.; Barrett, Natalie C.; Adams, Douglas E.

2014-07-01

Operations and maintenance costs for offshore wind plants are significantly higher than the current costs for land-based (onshore) wind plants. One way to reduce these costs would be to implement a structural health and prognostic management (SHPM) system as part of a condition based maintenance paradigm with smart load management and utilize a state-based cost model to assess the economics associated with use of the SHPM system. To facilitate the development of such a system a multi-scale modeling and simulation approach developed in prior work is used to identify how the underlying physics of the system are affected by themore » presence of damage and faults, and how these changes manifest themselves in the operational response of a full turbine. This methodology was used to investigate two case studies: (1) the effects of rotor imbalance due to pitch error (aerodynamic imbalance) and mass imbalance and (2) disbond of the shear web; both on a 5-MW offshore wind turbine in the present report. Sensitivity analyses were carried out for the detection strategies of rotor imbalance and shear web disbond developed in prior work by evaluating the robustness of key measurement parameters in the presence of varying wind speeds, horizontal shear, and turbulence. Detection strategies were refined for these fault mechanisms and probabilities of detection were calculated. For all three fault mechanisms, the probability of detection was 96% or higher for the optimized wind speed ranges of the laminar, 30% horizontal shear, and 60% horizontal shear wind profiles. The revised cost model provided insight into the estimated savings in operations and maintenance costs as they relate to the characteristics of the SHPM system. The integration of the health monitoring information and O&M cost versus damage/fault severity information provides the initial steps to identify processes to reduce operations and maintenance costs for an offshore wind farm while increasing turbine availability, revenue, and overall profit.« less

Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

PubMed Central

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient. PMID:23112645

Fault diagnostics for turbo-shaft engine sensors based on a simplified on-board model.

PubMed

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

Robust fault detection of turbofan engines subject to adaptive controllers via a Total Measurable Fault Information Residual (ToMFIR) technique.

PubMed

Chen, Wen; Chowdhury, Fahmida N; Djuric, Ana; Yeh, Chih-Ping

2014-09-01

This paper provides a new design of robust fault detection for turbofan engines with adaptive controllers. The critical issue is that the adaptive controllers can depress the faulty effects such that the actual system outputs remain the pre-specified values, making it difficult to detect faults/failures. To solve this problem, a Total Measurable Fault Information Residual (ToMFIR) technique with the aid of system transformation is adopted to detect faults in turbofan engines with adaptive controllers. This design is a ToMFIR-redundancy-based robust fault detection. The ToMFIR is first introduced and existing results are also summarized. The Detailed design process of the ToMFIRs is presented and a turbofan engine model is simulated to verify the effectiveness of the proposed ToMFIR-based fault-detection strategy. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

An architecture for object-oriented intelligent control of power systems in space

NASA Technical Reports Server (NTRS)

Holmquist, Sven G.; Jayaram, Prakash; Jansen, Ben H.

1993-01-01

A control system for autonomous distribution and control of electrical power during space missions is being developed. This system should free the astronauts from localizing faults and reconfiguring loads if problems with the power distribution and generation components occur. The control system uses an object-oriented simulation model of the power system and first principle knowledge to detect, identify, and isolate faults. Each power system component is represented as a separate object with knowledge of its normal behavior. The reasoning process takes place at three different levels of abstraction: the Physical Component Model (PCM) level, the Electrical Equivalent Model (EEM) level, and the Functional System Model (FSM) level, with the PCM the lowest level of abstraction and the FSM the highest. At the EEM level the power system components are reasoned about as their electrical equivalents, e.g, a resistive load is thought of as a resistor. However, at the PCM level detailed knowledge about the component's specific characteristics is taken into account. The FSM level models the system at the subsystem level, a level appropriate for reconfiguration and scheduling. The control system operates in two modes, a reactive and a proactive mode, simultaneously. In the reactive mode the control system receives measurement data from the power system and compares these values with values determined through simulation to detect the existence of a fault. The nature of the fault is then identified through a model-based reasoning process using mainly the EEM. Compound component models are constructed at the EEM level and used in the fault identification process. In the proactive mode the reasoning takes place at the PCM level. Individual components determine their future health status using a physical model and measured historical data. In case changes in the health status seem imminent the component warns the control system about its impending failure. The fault isolation process uses the FSM level for its reasoning base.

Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation

NASA Technical Reports Server (NTRS)

Ferrell, Bob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Brown, Barbara

2010-01-01

The purpose of this paper is to present the model development process used to create a Functional Fault Model (FFM) of a liquid hydrogen (L H2) system that will be used for realtime fault isolation in a Fault Detection, Isolation and Recover (FDIR) system. The paper explains th e steps in the model development process and the data products required at each step, including examples of how the steps were performed fo r the LH2 system. It also shows the relationship between the FDIR req uirements and steps in the model development process. The paper concl udes with a description of a demonstration of the LH2 model developed using the process and future steps for integrating the model in a live operational environment.

Cage-rotor induction motor inter-turn short circuit fault detection with and without saturation effect by MEC model.

PubMed

Naderi, Peyman

2016-09-01

The inter-turn short fault for the Cage-Rotor-Induction-Machine (CRIM) is studied in this paper and its local saturation is taken into account. However, in order to observe the exact behavior of machine, the Magnetic-Equivalent-Circuit (MEC) and nonlinear B-H curve are proposed to provide an insight into the machine model and saturation effect respectively. The electrical machines are generally operated near to their saturation zone due to some design necessities. Hence, when the machine is exposed to a fault such as short circuit or eccentricities, it is operated within its saturation zone and thus, time and space harmonics are integrated and as a result, current and torque harmonics are generated which the phenomenon cannot be explored when saturation is dismissed. Nonetheless, inter-turn short circuit may lead to local saturation and this occurrence is studied in this paper using MEC model. In order to achieve the mentioned objectives, two and also four-pole machines are modeled as two samples and the machines performances are analyzed in healthy and faulty cases with and without saturation effect. A novel strategy is proposed to precisely detect inter-turn short circuit fault according to the stator׳s lines current signatures and the accuracy of the proposed method is verified by experimental results. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Development of an On-board Failure Diagnostics and Prognostics System for Solid Rocket Booster

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadim N.; Luchinsky, Dmitry G.; Osipov, Vyatcheslav V.; Timucin, Dogan A.; Uckun, Serdar

2009-01-01

We develop a case breach model for the on-board fault diagnostics and prognostics system for subscale solid-rocket boosters (SRBs). The model development was motivated by recent ground firing tests, in which a deviation of measured time-traces from the predicted time-series was observed. A modified model takes into account the nozzle ablation, including the effect of roughness of the nozzle surface, the geometry of the fault, and erosion and burning of the walls of the hole in the metal case. The derived low-dimensional performance model (LDPM) of the fault can reproduce the observed time-series data very well. To verify the performance of the LDPM we build a FLUENT model of the case breach fault and demonstrate a good agreement between theoretical predictions based on the analytical solution of the model equations and the results of the FLUENT simulations. We then incorporate the derived LDPM into an inferential Bayesian framework and verify performance of the Bayesian algorithm for the diagnostics and prognostics of the case breach fault. It is shown that the obtained LDPM allows one to track parameters of the SRB during the flight in real time, to diagnose case breach fault, and to predict its values in the future. The application of the method to fault diagnostics and prognostics (FD&P) of other SRB faults modes is discussed.

Building a 3D faulted a priori model for stratigraphic inversion: Illustration of a new methodology applied on a North Sea field case study

NASA Astrophysics Data System (ADS)

Rainaud, Jean-François; Clochard, Vincent; Delépine, Nicolas; Crabié, Thomas; Poudret, Mathieu; Perrin, Michel; Klein, Emmanuel

2018-07-01

Accurate reservoir characterization is needed all along the development of an oil and gas field study. It helps building 3D numerical reservoir simulation models for estimating the original oil and gas volumes in place and for simulating fluid flow behaviors. At a later stage of the field development, reservoir characterization can also help deciding which recovery techniques need to be used for fluids extraction. In complex media, such as faulted reservoirs, flow behavior predictions within volumes close to faults can be a very challenging issue. During the development plan, it is necessary to determine which types of communication exist between faults or which potential barriers exist for fluid flows. The solving of these issues rests on accurate fault characterization. In most cases, faults are not preserved along reservoir characterization workflows. The memory of the interpreted faults from seismic is not kept during seismic inversion and further interpretation of the result. The goal of our study is at first to integrate a 3D fault network as a priori information into a model-based stratigraphic inversion procedure. Secondly, we apply our methodology on a well-known oil and gas case study over a typical North Sea field (UK Northern North Sea) in order to demonstrate its added value for determining reservoir properties. More precisely, the a priori model is composed of several geological units populated by physical attributes, they are extrapolated from well log data following the deposition mode, but usually a priori model building methods respect neither the 3D fault geometry nor the stratification dips on the fault sides. We address this difficulty by applying an efficient flattening method for each stratigraphic unit in our workflow. Even before seismic inversion, the obtained stratigraphic model has been directly used to model synthetic seismic on our case study. Comparisons between synthetic seismic obtained from our 3D fault network model give much lower residuals than with a "basic" stratigraphic model. Finally, we apply our model-based inversion considering both faulted and non-faulted a priori models. By comparing the rock impedances results obtain in the two cases, we can see a better delineation of the Brent-reservoir compartments by using the 3D faulted a priori model built with our method.

Vibration signal models for fault diagnosis of planet bearings

NASA Astrophysics Data System (ADS)

Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.

2016-05-01

Rolling element bearings are key components of planetary gearboxes. Among them, the motion of planet bearings is very complex, encompassing spinning and revolution. Therefore, planet bearing vibrations are highly intricate and their fault characteristics are completely different from those of fixed-axis case, making planet bearing fault diagnosis a difficult topic. In order to address this issue, we derive the explicit equations for calculating the characteristic frequency of outer race, rolling element and inner race fault, considering the complex motion of planet bearings. We also develop the planet bearing vibration signal model for each fault case, considering the modulation effects of load zone passing, time-varying angle between the gear pair mesh and fault induced impact force, as well as the time-varying vibration transfer path. Based on the developed signal models, we derive the explicit equations of Fourier spectrum in each fault case, and summarize the vibration spectral characteristics respectively. The theoretical derivations are illustrated by numerical simulation, and further validated experimentally and all the three fault cases (i.e. outer race, rolling element and inner race localized fault) are diagnosed.

SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

NASA Astrophysics Data System (ADS)

Golafshan, Reza; Yuce Sanliturk, Kenan

2016-03-01

Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

Hybrid Model-Based and Data-Driven Fault Detection and Diagnostics for Commercial Buildings: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frank, Stephen; Heaney, Michael; Jin, Xin

Commercial buildings often experience faults that produce undesirable behavior in building systems. Building faults waste energy, decrease occupants' comfort, and increase operating costs. Automated fault detection and diagnosis (FDD) tools for buildings help building owners discover and identify the root causes of faults in building systems, equipment, and controls. Proper implementation of FDD has the potential to simultaneously improve comfort, reduce energy use, and narrow the gap between actual and optimal building performance. However, conventional rule-based FDD requires expensive instrumentation and valuable engineering labor, which limit deployment opportunities. This paper presents a hybrid, automated FDD approach that combines building energymore » models and statistical learning tools to detect and diagnose faults noninvasively, using minimal sensors, with little customization. We compare and contrast the performance of several hybrid FDD algorithms for a small security building. Our results indicate that the algorithms can detect and diagnose several common faults, but more work is required to reduce false positive rates and improve diagnosis accuracy.« less

The development of efficient numerical time-domain modeling methods for geophysical wave propagation

NASA Astrophysics Data System (ADS)

Zhu, Lieyuan

This Ph.D. dissertation focuses on the numerical simulation of geophysical wave propagation in the time domain including elastic waves in solid media, the acoustic waves in fluid media, and the electromagnetic waves in dielectric media. This thesis shows that a linear system model can describe accurately the physical processes of those geophysical waves' propagation and can be used as a sound basis for modeling geophysical wave propagation phenomena. The generalized stability condition for numerical modeling of wave propagation is therefore discussed in the context of linear system theory. The efficiency of a series of different numerical algorithms in the time-domain for modeling geophysical wave propagation are discussed and compared. These algorithms include the finite-difference time-domain method, pseudospectral time domain method, alternating directional implicit (ADI) finite-difference time domain method. The advantages and disadvantages of these numerical methods are discussed and the specific stability condition for each modeling scheme is carefully derived in the context of the linear system theory. Based on the review and discussion of these existing approaches, the split step, ADI pseudospectral time domain (SS-ADI-PSTD) method is developed and tested for several cases. Moreover, the state-of-the-art stretched-coordinate perfect matched layer (SCPML) has also been implemented in SS-ADI-PSTD algorithm as the absorbing boundary condition for truncating the computational domain and absorbing the artificial reflection from the domain boundaries. After algorithmic development, a few case studies serve as the real-world examples to verify the capacities of the numerical algorithms and understand the capabilities and limitations of geophysical methods for detection of subsurface contamination. The first case is a study using ground penetrating radar (GPR) amplitude variation with offset (AVO) for subsurface non-aqueous-liquid (NAPL) contamination. The numerical AVO study reveals that the normalized residual polarization (NRP) variation with offset does not respond to subsurface NAPL existence when the offset is close to or larger than its critical value (which corresponds to critical incident angle) because the air and head waves dominate the recorded wave field and severely interfere with reflected waves in the TEz wave field. Thus it can be concluded that the NRP AVO/GPR method is invalid when source-receiver angle offset is close to or greater than its critical value due to incomplete and severely distorted reflection information. In other words, AVO is not a promising technique for detection of the subsurface NAPL, as claimed by some researchers. In addition, the robustness of the newly developed numerical algorithms is also verified by the AVO study for randomly-arranged layered media. Meanwhile, this case study also demonstrates again that the full-wave numerical modeling algorithms are superior to ray tracing method. The second case study focuses on the effect of the existence of a near-surface fault on the vertically incident P- and S- plane waves. The modeling results show that both P-wave vertical incidence and S-wave vertical incidence cases are qualified fault indicators. For the plane S-wave vertical incidence case, the horizontal location of the upper tip of the fault (the footwall side) can be identified without much effort, because all the recorded parameters on the surface including the maximum velocities and the maximum accelerations, and even their ratios H/V, have shown dramatic changes when crossing the upper tip of the fault. The centers of the transition zone of the all the curves of parameters are almost directly above the fault tip (roughly the horizontal center of the model). Compared with the case of the vertically incident P-wave source, it has been found that the S-wave vertical source is a better indicator for fault location, because the horizontal location of the tip of that fault cannot be clearly identified with the ratio of the horizontal to vertical velocity for the P-wave incident case.

Advanced information processing system: Fault injection study and results

NASA Technical Reports Server (NTRS)

Burkhardt, Laura F.; Masotto, Thomas K.; Lala, Jaynarayan H.

1992-01-01

The objective of the AIPS program is to achieve a validated fault tolerant distributed computer system. The goals of the AIPS fault injection study were: (1) to present the fault injection study components addressing the AIPS validation objective; (2) to obtain feedback for fault removal from the design implementation; (3) to obtain statistical data regarding fault detection, isolation, and reconfiguration responses; and (4) to obtain data regarding the effects of faults on system performance. The parameters are described that must be varied to create a comprehensive set of fault injection tests, the subset of test cases selected, the test case measurements, and the test case execution. Both pin level hardware faults using a hardware fault injector and software injected memory mutations were used to test the system. An overview is provided of the hardware fault injector and the associated software used to carry out the experiments. Detailed specifications are given of fault and test results for the I/O Network and the AIPS Fault Tolerant Processor, respectively. The results are summarized and conclusions are given.

Development of a morphological convolution operator for bearing fault detection

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Liu, Weiwei; Wang, Yan

2018-05-01

This paper presents a novel signal processing scheme, namely morphological convolution operator (MCO) lifted morphological undecimated wavelet (MUDW), for rolling element bearing fault detection. In this scheme, a MCO is first designed to fully utilize the advantage of the closing & opening gradient operator and the closing-opening & opening-closing gradient operator for feature extraction as well as the merit of excellent denoising characteristics of the convolution operator. The MCO is then introduced into MUDW for the purpose of improving the fault detection ability of the reported MUDWs. Experimental vibration signals collected from a train wheelset test rig and the bearing data center of Case Western Reserve University are employed to evaluate the effectiveness of the proposed MCO lifted MUDW on fault detection of rolling element bearings. The results show that the proposed approach has a superior performance in extracting fault features of defective rolling element bearings. In addition, comparisons are performed between two reported MUDWs and the proposed MCO lifted MUDW. The MCO lifted MUDW outperforms both of them in detection of outer race faults and inner race faults of rolling element bearings.

Data-driven fault detection, isolation and estimation of aircraft gas turbine engine actuator and sensors

NASA Astrophysics Data System (ADS)

Naderi, E.; Khorasani, K.

2018-02-01

In this work, a data-driven fault detection, isolation, and estimation (FDI&E) methodology is proposed and developed specifically for monitoring the aircraft gas turbine engine actuator and sensors. The proposed FDI&E filters are directly constructed by using only the available system I/O data at each operating point of the engine. The healthy gas turbine engine is stimulated by a sinusoidal input containing a limited number of frequencies. First, the associated system Markov parameters are estimated by using the FFT of the input and output signals to obtain the frequency response of the gas turbine engine. These data are then used for direct design and realization of the fault detection, isolation and estimation filters. Our proposed scheme therefore does not require any a priori knowledge of the system linear model or its number of poles and zeros at each operating point. We have investigated the effects of the size of the frequency response data on the performance of our proposed schemes. We have shown through comprehensive case studies simulations that desirable fault detection, isolation and estimation performance metrics defined in terms of the confusion matrix criterion can be achieved by having access to only the frequency response of the system at only a limited number of frequencies.

Stress induced near fault-zone breakout rotation: Two case studies in TCDP and JFAST

NASA Astrophysics Data System (ADS)

Wu, H. Y.; Brodsky, E. E.; Moe, K.; Kinoshita, M.

2014-12-01

Within the past decade, two successful rapid-response drilling projects have measured breakouts within the nearfault of a recently ruptured fault. Breakout observation is the direct way to detect the far and near filed stress orientation in drilling. Here we compare those data. In 2006, ICDP performed an inland drilling project to penetrate Chelungpu fault plane in central of Taiwan, which had recently slipped in 1999 Mw 7.6 Chi-Chi earthquake. This drilling project succeeded in full coring and collecting comprehensive logging data in the borehole. The resistivity images run by Formation Micro Imager (FMI) indicated that a breakout rotation in the vicinity of the fault (1111mbf). Leak-off tests on site constrained the magnitude of minimum horizontal principal stress. Here we use these data to determine the stress variation in the fault plane in our breakout dislocation model. Based on the amount of breakout azimuth, rotation and fault geometry, the stress drop can be estimated in this model. In 2012, IODP initiated a rapid drilling project after the 2011 Mw9.0 Tohoku earthquake in Japan Trench. Due to the deep-water depth, only a real-time resistivity image recorded by Logging While Drilling (LWD) and few core samples are recovered by this expedition. However, the breakout azimuth occurred near the plate boundary (820mbsf) represents the stress disturbance after the dramatic slip comparing to TCDP case. In this research, we are attempting to discuss the possible effect factors and reconstruct the geo-mechanical models to interpret the breakout distribution observed from logging data and the stress state after these huge earthquakes.

Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV.

PubMed

Abbaspour, Alireza; Aboutalebi, Payam; Yen, Kang K; Sargolzaei, Arman

2017-03-01

A new online detection strategy is developed to detect faults in sensors and actuators of unmanned aerial vehicle (UAV) systems. In this design, the weighting parameters of the Neural Network (NN) are updated by using the Extended Kalman Filter (EKF). Online adaptation of these weighting parameters helps to detect abrupt, intermittent, and incipient faults accurately. We apply the proposed fault detection system to a nonlinear dynamic model of the WVU YF-22 unmanned aircraft for its evaluation. The simulation results show that the new method has better performance in comparison with conventional recurrent neural network-based fault detection strategies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications

NASA Technical Reports Server (NTRS)

Balaban, Edward; Saxena, Abhinav; Bansal, Prasun; Goebel, Kai F.; Curran, Simon

2009-01-01

Sensor faults continue to be a major hurdle for systems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally difficult to seed particular sensor faults. Therefore, research is underway to better understand the different fault modes seen in sensors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper illustrates the work with data collected from an electro-mechanical actuator in an aerospace setting, equipped with temperature, vibration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network based classifier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and disambiguation efficacy to severity of fault conditions.

Fault Analysis and Detection in Microgrids with High PV Penetration

DOE Office of Scientific and Technical Information (OSTI.GOV)

El Khatib, Mohamed; Hernandez Alvidrez, Javier; Ellis, Abraham

In this report we focus on analyzing current-controlled PV inverters behaviour under faults in order to develop fault detection schemes for microgrids with high PV penetration. Inverter model suitable for steady state fault studies is presented and the impact of PV inverters on two protection elements is analyzed. The studied protection elements are superimposed quantities based directional element and negative sequence directional element. Additionally, several non-overcurrent fault detection schemes are discussed in this report for microgrids with high PV penetration. A detailed time-domain simulation study is presented to assess the performance of the presented fault detection schemes under different microgridmore » modes of operation.« less

Saturating time-delay transformer for overcurrent protection. [Patent application

DOEpatents

Praeg, W.F.

1975-12-18

Electrical loads connected to dc supplies are protected from damage by overcurrent in the case of a load fault by connecting in series with the load a saturating transformer that detects a load fault and limits the fault current to a safe level for a period long enough to correct the fault or else disconnect the power supply.

Saturating time-delay transformer for overcurrent protection

DOEpatents

Praeg, Walter F.

1977-01-01

Electrical loads connected to d-c supplies are protected from damage by overcurrent in the case of a load fault by connecting in series with the load a saturating transformer that detects a load fault and limits the fault current to a safe level for a period long enough to correct the fault or else disconnect the power supply.

Flight elements: Fault detection and fault management

NASA Technical Reports Server (NTRS)

Lum, H.; Patterson-Hine, A.; Edge, J. T.; Lawler, D.

1990-01-01

Fault management for an intelligent computational system must be developed using a top down integrated engineering approach. An approach proposed includes integrating the overall environment involving sensors and their associated data; design knowledge capture; operations; fault detection, identification, and reconfiguration; testability; causal models including digraph matrix analysis; and overall performance impacts on the hardware and software architecture. Implementation of the concept to achieve a real time intelligent fault detection and management system will be accomplished via the implementation of several objectives, which are: Development of fault tolerant/FDIR requirement and specification from a systems level which will carry through from conceptual design through implementation and mission operations; Implementation of monitoring, diagnosis, and reconfiguration at all system levels providing fault isolation and system integration; Optimize system operations to manage degraded system performance through system integration; and Lower development and operations costs through the implementation of an intelligent real time fault detection and fault management system and an information management system.

Measurement of fault latency in a digital avionic miniprocessor

NASA Technical Reports Server (NTRS)

Mcgough, J. G.; Swern, F. L.

1981-01-01

The results of fault injection experiments utilizing a gate-level emulation of the central processor unit of the Bendix BDX-930 digital computer are presented. The failure detection coverage of comparison-monitoring and a typical avionics CPU self-test program was determined. The specific tasks and experiments included: (1) inject randomly selected gate-level and pin-level faults and emulate six software programs using comparison-monitoring to detect the faults; (2) based upon the derived empirical data develop and validate a model of fault latency that will forecast a software program's detecting ability; (3) given a typical avionics self-test program, inject randomly selected faults at both the gate-level and pin-level and determine the proportion of faults detected; (4) determine why faults were undetected; (5) recommend how the emulation can be extended to multiprocessor systems such as SIFT; and (6) determine the proportion of faults detected by a uniprocessor BIT (built-in-test) irrespective of self-test.

Back analysis of fault-slip in burst prone environment

NASA Astrophysics Data System (ADS)

Sainoki, Atsushi; Mitri, Hani S.

2016-11-01

In deep underground mines, stress re-distribution induced by mining activities could cause fault-slip. Seismic waves arising from fault-slip occasionally induce rock ejection when hitting the boundary of mine openings, and as a result, severe damage could be inflicted. In general, it is difficult to estimate fault-slip-induced ground motion in the vicinity of mine openings because of the complexity of the dynamic response of faults and the presence of geological structures. In this paper, a case study is conducted for a Canadian underground mine, herein called "Mine-A", which is known for its seismic activities. Using a microseismic database collected from the mine, a back analysis of fault-slip is carried out with mine-wide 3-dimensional numerical modeling. A back analysis is conducted to estimate the physical and mechanical properties of the causative fracture or shear zones. One large seismic event has been selected for the back analysis to detect a fault-slip related seismic event. In the back analysis, the shear zone properties are estimated with respect to moment magnitude of the seismic event and peak particle velocity (PPV) recorded by a strong ground motion sensor. The estimated properties are then validated through comparison with peak ground acceleration recorded by accelerometers. Lastly, ground motion in active mining areas is estimated by conducting dynamic analysis with the estimated values. The present study implies that it would be possible to estimate the magnitude of seismic events that might occur in the near future by applying the estimated properties to the numerical model. Although the case study is conducted for a specific mine, the developed methodology can be equally applied to other mines suffering from fault-slip related seismic events.

Potential fault region detection in TFDS images based on convolutional neural network

NASA Astrophysics Data System (ADS)

Sun, Junhua; Xiao, Zhongwen

2016-10-01

In recent years, more than 300 sets of Trouble of Running Freight Train Detection System (TFDS) have been installed on railway to monitor the safety of running freight trains in China. However, TFDS is simply responsible for capturing, transmitting, and storing images, and fails to recognize faults automatically due to some difficulties such as such as the diversity and complexity of faults and some low quality images. To improve the performance of automatic fault recognition, it is of great importance to locate the potential fault areas. In this paper, we first introduce a convolutional neural network (CNN) model to TFDS and propose a potential fault region detection system (PFRDS) for simultaneously detecting four typical types of potential fault regions (PFRs). The experimental results show that this system has a higher performance of image detection to PFRs in TFDS. An average detection recall of 98.95% and precision of 100% are obtained, demonstrating the high detection ability and robustness against various poor imaging situations.

Fault compaction and overpressured faults: results from a 3-D model of a ductile fault zone

NASA Astrophysics Data System (ADS)

Fitzenz, D. D.; Miller, S. A.

2003-10-01

A model of a ductile fault zone is incorporated into a forward 3-D earthquake model to better constrain fault-zone hydraulics. The conceptual framework of the model fault zone was chosen such that two distinct parts are recognized. The fault core, characterized by a relatively low permeability, is composed of a coseismic fault surface embedded in a visco-elastic volume that can creep and compact. The fault core is surrounded by, and mostly sealed from, a high permeability damaged zone. The model fault properties correspond explicitly to those of the coseismic fault core. Porosity and pore pressure evolve to account for the viscous compaction of the fault core, while stresses evolve in response to the applied tectonic loading and to shear creep of the fault itself. A small diffusive leakage is allowed in and out of the fault zone. Coseismically, porosity is created to account for frictional dilatancy. We show in the case of a 3-D fault model with no in-plane flow and constant fluid compressibility, pore pressures do not drop to hydrostatic levels after a seismic rupture, leading to an overpressured weak fault. Since pore pressure plays a key role in the fault behaviour, we investigate coseismic hydraulic property changes. In the full 3-D model, pore pressures vary instantaneously by the poroelastic effect during the propagation of the rupture. Once the stress state stabilizes, pore pressures are incrementally redistributed in the failed patch. We show that the significant effect of pressure-dependent fluid compressibility in the no in-plane flow case becomes a secondary effect when the other spatial dimensions are considered because in-plane flow with a near-lithostatically pressured neighbourhood equilibrates at a pressure much higher than hydrostatic levels, forming persistent high-pressure fluid compartments. If the observed faults are not all overpressured and weak, other mechanisms, not included in this model, must be at work in nature, which need to be investigated. Significant leakage perpendicular to the fault strike (in the case of a young fault), or cracks hydraulically linking the fault core to the damaged zone (for a mature fault) are probable mechanisms for keeping the faults strong and might play a significant role in modulating fault pore pressures. Therefore, fault-normal hydraulic properties of fault zones should be a future focus of field and numerical experiments.

Solar Photovoltaic (PV) Distributed Generation Systems - Control and Protection

NASA Astrophysics Data System (ADS)

Yi, Zhehan

This dissertation proposes a comprehensive control, power management, and fault detection strategy for solar photovoltaic (PV) distribution generations. Battery storages are typically employed in PV systems to mitigate the power fluctuation caused by unstable solar irradiance. With AC and DC loads, a PV-battery system can be treated as a hybrid microgrid which contains both DC and AC power resources and buses. In this thesis, a control power and management system (CAPMS) for PV-battery hybrid microgrid is proposed, which provides 1) the DC and AC bus voltage and AC frequency regulating scheme and controllers designed to track set points; 2) a power flow management strategy in the hybrid microgrid to achieve system generation and demand balance in both grid-connected and islanded modes; 3) smooth transition control during grid reconnection by frequency and phase synchronization control between the main grid and microgrid. Due to the increasing demands for PV power, scales of PV systems are getting larger and fault detection in PV arrays becomes challenging. High-impedance faults, low-mismatch faults, and faults occurred in low irradiance conditions tend to be hidden due to low fault currents, particularly, when a PV maximum power point tracking (MPPT) algorithm is in-service. If remain undetected, these faults can considerably lower the output energy of solar systems, damage the panels, and potentially cause fire hazards. In this dissertation, fault detection challenges in PV arrays are analyzed in depth, considering the crossing relations among the characteristics of PV, interactions with MPPT algorithms, and the nature of solar irradiance. Two fault detection schemes are then designed as attempts to address these technical issues, which detect faults inside PV arrays accurately even under challenging circumstances, e.g., faults in low irradiance conditions or high-impedance faults. Taking advantage of multi-resolution signal decomposition (MSD), a powerful signal processing technique based on discrete wavelet transformation (DWT), the first attempt is devised, which extracts the features of both line-to-line (L-L) and line-to-ground (L-G) faults and employs a fuzzy inference system (FIS) for the decision-making stage of fault detection. This scheme is then improved as the second attempt by further studying the system's behaviors during L-L faults, extracting more efficient fault features, and devising a more advanced decision-making stage: the two-stage support vector machine (SVM). For the first time, the two-stage SVM method is proposed in this dissertation to detect L-L faults in PV system with satisfactory accuracies. Numerous simulation and experimental case studies are carried out to verify the proposed control and protection strategies. Simulation environment is set up using the PSCAD/EMTDC and Matlab/Simulink software packages. Experimental case studies are conducted in a PV-battery hybrid microgrid using the dSPACE real-time controller to demonstrate the ease of hardware implementation and the controller performance. Another small-scale grid-connected PV system is set up to verify both fault detection algorithms which demonstrate promising performances and fault detecting accuracies.

Technology transfer by means of fault tree synthesis

NASA Astrophysics Data System (ADS)

Batzias, Dimitris F.

2012-12-01

Since Fault Tree Analysis (FTA) attempts to model and analyze failure processes of engineering, it forms a common technique for good industrial practice. On the contrary, fault tree synthesis (FTS) refers to the methodology of constructing complex trees either from dentritic modules built ad hoc or from fault tress already used and stored in a Knowledge Base. In both cases, technology transfer takes place in a quasi-inductive mode, from partial to holistic knowledge. In this work, an algorithmic procedure, including 9 activity steps and 3 decision nodes is developed for performing effectively this transfer when the fault under investigation occurs within one of the latter stages of an industrial procedure with several stages in series. The main parts of the algorithmic procedure are: (i) the construction of a local fault tree within the corresponding production stage, where the fault has been detected, (ii) the formation of an interface made of input faults that might occur upstream, (iii) the fuzzy (to count for uncertainty) multicriteria ranking of these faults according to their significance, and (iv) the synthesis of an extended fault tree based on the construction of part (i) and on the local fault tree of the first-ranked fault in part (iii). An implementation is presented, referring to 'uneven sealing of Al anodic film', thus proving the functionality of the developed methodology.

A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang Yumin; Lum, Kai-Yew; Wang Qingguo

In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus,more » the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.« less

A H-infinity Fault Detection and Diagnosis Scheme for Discrete Nonlinear System Using Output Probability Density Estimation

NASA Astrophysics Data System (ADS)

Zhang, Yumin; Wang, Qing-Guo; Lum, Kai-Yew

2009-03-01

In this paper, a H-infinity fault detection and diagnosis (FDD) scheme for a class of discrete nonlinear system fault using output probability density estimation is presented. Unlike classical FDD problems, the measured output of the system is viewed as a stochastic process and its square root probability density function (PDF) is modeled with B-spline functions, which leads to a deterministic space-time dynamic model including nonlinearities, uncertainties. A weighting mean value is given as an integral function of the square root PDF along space direction, which leads a function only about time and can be used to construct residual signal. Thus, the classical nonlinear filter approach can be used to detect and diagnose the fault in system. A feasible detection criterion is obtained at first, and a new H-infinity adaptive fault diagnosis algorithm is further investigated to estimate the fault. Simulation example is given to demonstrate the effectiveness of the proposed approaches.

Adaptively Adjusted Event-Triggering Mechanism on Fault Detection for Networked Control Systems.

PubMed

Wang, Yu-Long; Lim, Cheng-Chew; Shi, Peng

2016-12-08

This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.

PubMed

Mansouri, Majdi; Nounou, Mohamed N; Nounou, Hazem N

2017-09-01

In our previous work, we have demonstrated the effectiveness of the linear multiscale principal component analysis (PCA)-based moving window (MW)-generalized likelihood ratio test (GLRT) technique over the classical PCA and multiscale principal component analysis (MSPCA)-based GLRT methods. The developed fault detection algorithm provided optimal properties by maximizing the detection probability for a particular false alarm rate (FAR) with different values of windows, and however, most real systems are nonlinear, which make the linear PCA method not able to tackle the issue of non-linearity to a great extent. Thus, in this paper, first, we apply a nonlinear PCA to obtain an accurate principal component of a set of data and handle a wide range of nonlinearities using the kernel principal component analysis (KPCA) model. The KPCA is among the most popular nonlinear statistical methods. Second, we extend the MW-GLRT technique to one that utilizes exponential weights to residuals in the moving window (instead of equal weightage) as it might be able to further improve fault detection performance by reducing the FAR using exponentially weighed moving average (EWMA). The developed detection method, which is called EWMA-GLRT, provides improved properties, such as smaller missed detection and FARs and smaller average run length. The idea behind the developed EWMA-GLRT is to compute a new GLRT statistic that integrates current and previous data information in a decreasing exponential fashion giving more weight to the more recent data. This provides a more accurate estimation of the GLRT statistic and provides a stronger memory that will enable better decision making with respect to fault detection. Therefore, in this paper, a KPCA-based EWMA-GLRT method is developed and utilized in practice to improve fault detection in biological phenomena modeled by S-systems and to enhance monitoring process mean. The idea behind a KPCA-based EWMA-GLRT fault detection algorithm is to combine the advantages brought forward by the proposed EWMA-GLRT fault detection chart with the KPCA model. Thus, it is used to enhance fault detection of the Cad System in E. coli model through monitoring some of the key variables involved in this model such as enzymes, transport proteins, regulatory proteins, lysine, and cadaverine. The results demonstrate the effectiveness of the proposed KPCA-based EWMA-GLRT method over Q , GLRT, EWMA, Shewhart, and moving window-GLRT methods. The detection performance is assessed and evaluated in terms of FAR, missed detection rates, and average run length (ARL 1 ) values.

A signal-based fault detection and classification method for heavy haul wagons

NASA Astrophysics Data System (ADS)

Li, Chunsheng; Luo, Shihui; Cole, Colin; Spiryagin, Maksym; Sun, Yanquan

2017-12-01

This paper proposes a signal-based fault detection and isolation (FDI) system for heavy haul wagons considering the special requirements of low cost and robustness. The sensor network of the proposed system consists of just two accelerometers mounted on the front left and rear right of the carbody. Seven fault indicators (FIs) are proposed based on the cross-correlation analyses of the sensor-collected acceleration signals. Bolster spring fault conditions are focused on in this paper, including two different levels (small faults and moderate faults) and two locations (faults in the left and right bolster springs of the first bogie). A fully detailed dynamic model of a typical 40t axle load heavy haul wagon is developed to evaluate the deterioration of dynamic behaviour under proposed fault conditions and demonstrate the detectability of the proposed FDI method. Even though the fault conditions considered in this paper did not deteriorate the wagon dynamic behaviour dramatically, the proposed FIs show great sensitivity to the bolster spring faults. The most effective and efficient FIs are chosen for fault detection and classification. Analysis results indicate that it is possible to detect changes in bolster stiffness of ±25% and identify the fault location.

Model-Based Diagnostics for Propellant Loading Systems

NASA Technical Reports Server (NTRS)

Daigle, Matthew John; Foygel, Michael; Smelyanskiy, Vadim N.

2011-01-01

The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly nonequilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.

Surveillance system and method having an operating mode partitioned fault classification model

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor)

2005-01-01

A system and method which partitions a parameter estimation model, a fault detection model, and a fault classification model for a process surveillance scheme into two or more coordinated submodels together providing improved diagnostic decision making for at least one determined operating mode of an asset.

ASCS online fault detection and isolation based on an improved MPCA

NASA Astrophysics Data System (ADS)

Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

2014-09-01

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

Detecting Faults in Southern California using Computer-Vision Techniques and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Interferometry

NASA Astrophysics Data System (ADS)

Barba, M.; Rains, C.; von Dassow, W.; Parker, J. W.; Glasscoe, M. T.

2013-12-01

Knowing the location and behavior of active faults is essential for earthquake hazard assessment and disaster response. In Interferometric Synthetic Aperture Radar (InSAR) images, faults are revealed as linear discontinuities. Currently, interferograms are manually inspected to locate faults. During the summer of 2013, the NASA-JPL DEVELOP California Disasters team contributed to the development of a method to expedite fault detection in California using remote-sensing technology. The team utilized InSAR images created from polarimetric L-band data from NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) project. A computer-vision technique known as 'edge-detection' was used to automate the fault-identification process. We tested and refined an edge-detection algorithm under development through NASA's Earthquake Data Enhanced Cyber-Infrastructure for Disaster Evaluation and Response (E-DECIDER) project. To optimize the algorithm we used both UAVSAR interferograms and synthetic interferograms generated through Disloc, a web-based modeling program available through NASA's QuakeSim project. The edge-detection algorithm detected seismic, aseismic, and co-seismic slip along faults that were identified and compared with databases of known fault systems. Our optimization process was the first step toward integration of the edge-detection code into E-DECIDER to provide decision support for earthquake preparation and disaster management. E-DECIDER partners that will use the edge-detection code include the California Earthquake Clearinghouse and the US Department of Homeland Security through delivery of products using the Unified Incident Command and Decision Support (UICDS) service. Through these partnerships, researchers, earthquake disaster response teams, and policy-makers will be able to use this new methodology to examine the details of ground and fault motions for moderate to large earthquakes. Following an earthquake, the newly discovered faults can be paired with infrastructure overlays, allowing emergency response teams to identify sites that may have been exposed to damage. The faults will also be incorporated into a database for future integration into fault models and earthquake simulations, improving future earthquake hazard assessment. As new faults are mapped, they will further understanding of the complex fault systems and earthquake hazards within the seismically dynamic state of California.

New methods for the condition monitoring of level crossings

NASA Astrophysics Data System (ADS)

García Márquez, Fausto Pedro; Pedregal, Diego J.; Roberts, Clive

2015-04-01

Level crossings represent a high risk for railway systems. This paper demonstrates the potential to improve maintenance management through the use of intelligent condition monitoring coupled with reliability centred maintenance (RCM). RCM combines advanced electronics, control, computing and communication technologies to address the multiple objectives of cost effectiveness, improved quality, reliability and services. RCM collects digital and analogue signals utilising distributed transducers connected to either point-to-point or digital bus communication links. Assets in many industries use data logging capable of providing post-failure diagnostic support, but to date little use has been made of combined qualitative and quantitative fault detection techniques. The research takes the hydraulic railway level crossing barrier (LCB) system as a case study and develops a generic strategy for failure analysis, data acquisition and incipient fault detection. For each barrier the hydraulic characteristics, the motor's current and voltage, hydraulic pressure and the barrier's position are acquired. In order to acquire the data at a central point efficiently, without errors, a distributed single-cable Fieldbus is utilised. This allows the connection of all sensors through the project's proprietary communication nodes to a high-speed bus. The system developed in this paper for the condition monitoring described above detects faults by means of comparing what can be considered a 'normal' or 'expected' shape of a signal with respect to the actual shape observed as new data become available. ARIMA (autoregressive integrated moving average) models were employed for detecting faults. The statistical tests known as Jarque-Bera and Ljung-Box have been considered for testing the model.

The use of microtomography in structural geology: A new methodology to analyse fault faces

NASA Astrophysics Data System (ADS)

Jacques, Patricia D.; Nummer, Alexis Rosa; Heck, Richard J.; Machado, Rômulo

2014-09-01

This paper describes a new methodology to kinematically analyze faults in microscale dimensions (voxel size = 40 μm), using images obtained by X-ray computed microtomography (μCT). The equipment used is a GE MS8x-130 scanner. It was developed using rocks samples from Santa Catarina State, Brazil, and constructing micro Digital Elevation Models (μDEMs) for the fault surface, for analysing microscale brittle structures including striations, roughness and steps. Shaded relief images were created for the μDEMs, which enabled the generation of profiles to classify the secondary structures associated with the main fault surface. In the case of a sample with mineral growth that covers the fault surface, it is possible to detect the kinematic geometry even with the mineral cover. This technique proved to be useful for determining the sense of movement of faults, especially when it is not possible to determine striations in macro or microscopic analysis. When the sample has mineral deposit on the surface (mineral cover) this technique allows a relative chronology and geometric characterization between the faults with and without covering.

A Unified Nonlinear Adaptive Approach for Detection and Isolation of Engine Faults

NASA Technical Reports Server (NTRS)

Tang, Liang; DeCastro, Jonathan A.; Zhang, Xiaodong; Farfan-Ramos, Luis; Simon, Donald L.

2010-01-01

A challenging problem in aircraft engine health management (EHM) system development is to detect and isolate faults in system components (i.e., compressor, turbine), actuators, and sensors. Existing nonlinear EHM methods often deal with component faults, actuator faults, and sensor faults separately, which may potentially lead to incorrect diagnostic decisions and unnecessary maintenance. Therefore, it would be ideal to address sensor faults, actuator faults, and component faults under one unified framework. This paper presents a systematic and unified nonlinear adaptive framework for detecting and isolating sensor faults, actuator faults, and component faults for aircraft engines. The fault detection and isolation (FDI) architecture consists of a parallel bank of nonlinear adaptive estimators. Adaptive thresholds are appropriately designed such that, in the presence of a particular fault, all components of the residual generated by the adaptive estimator corresponding to the actual fault type remain below their thresholds. If the faults are sufficiently different, then at least one component of the residual generated by each remaining adaptive estimator should exceed its threshold. Therefore, based on the specific response of the residuals, sensor faults, actuator faults, and component faults can be isolated. The effectiveness of the approach was evaluated using the NASA C-MAPSS turbofan engine model, and simulation results are presented.

Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

NASA Astrophysics Data System (ADS)

Schlechtingen, Meik; Ferreira Santos, Ilmar

2011-07-01

This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive normal behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three normal behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abnormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.

Artificial Neural Network Based Fault Diagnostics of Rotating Machinery Using Wavelet Transforms as a Preprocessor

NASA Astrophysics Data System (ADS)

Paya, B. A.; Esat, I. I.; Badi, M. N. M.

1997-09-01

The purpose of condition monitoring and fault diagnostics are to detect and distinguish faults occurring in machinery, in order to provide a significant improvement in plant economy, reduce operational and maintenance costs and improve the level of safety. The condition of a model drive-line, consisting of various interconnected rotating parts, including an actual vehicle gearbox, two bearing housings, and an electric motor, all connected via flexible couplings and loaded by a disc brake, was investigated. This model drive-line was run in its normal condition, and then single and multiple faults were introduced intentionally to the gearbox, and to the one of the bearing housings. These single and multiple faults studied on the drive-line were typical bearing and gear faults which may develop during normal and continuous operation of this kind of rotating machinery. This paper presents the investigation carried out in order to study both bearing and gear faults introduced first separately as a single fault and then together as multiple faults to the drive-line. The real time domain vibration signals obtained for the drive-line were preprocessed by wavelet transforms for the neural network to perform fault detection and identify the exact kinds of fault occurring in the model drive-line. It is shown that by using multilayer artificial neural networks on the sets of preprocessed data by wavelet transforms, single and multiple faults were successfully detected and classified into distinct groups.

How Do Normal Faults Grow?

NASA Astrophysics Data System (ADS)

Jackson, C. A. L.; Bell, R. E.; Rotevatn, A.; Tvedt, A. B. M.

2015-12-01

Normal faulting accommodates stretching of the Earth's crust and is one of the fundamental controls on landscape evolution and sediment dispersal in rift basins. Displacement-length scaling relationships compiled from global datasets suggest normal faults grow via a sympathetic increase in these two parameters (the 'isolated fault model'). This model has dominated the structural geology literature for >20 years and underpins the structural and tectono-stratigraphic models developed for active rifts. However, relatively recent analysis of high-quality 3D seismic reflection data suggests faults may grow by rapid establishment of their near-final length prior to significant displacement accumulation (the 'coherent fault model'). The isolated and coherent fault models make very different predictions regarding the tectono-stratigraphic evolution of rift basin, thus assessing their applicability is important. To-date, however, very few studies have explicitly set out to critically test the coherent fault model thus, it may be argued, it has yet to be widely accepted in the structural geology community. Displacement backstripping is a simple graphical technique typically used to determine how faults lengthen and accumulate displacement; this technique should therefore allow us to test the competing fault models. However, in this talk we use several subsurface case studies to show that the most commonly used backstripping methods (the 'original' and 'modified' methods) are, however, of limited value, because application of one over the other requires an a priori assumption of the model most applicable to any given fault; we argue this is illogical given that the style of growth is exactly what the analysis is attempting to determine. We then revisit our case studies and demonstrate that, in the case of seismic-scale growth faults, growth strata thickness patterns and relay zone kinematics, rather than displacement backstripping, should be assessed to directly constrain fault length and thus tip behaviour through time. We conclude that rapid length establishment prior to displacement accumulation may be more common than is typically assumed, thus challenging the well-established, widely cited and perhaps overused, isolated fault model.

Real-Time Fault Detection Approach for Nonlinear Systems and its Asynchronous T-S Fuzzy Observer-Based Implementation.

PubMed

Li, Linlin; Ding, Steven X; Qiu, Jianbin; Yang, Ying

2017-02-01

This paper is concerned with a real-time observer-based fault detection (FD) approach for a general type of nonlinear systems in the presence of external disturbances. To this end, in the first part of this paper, we deal with the definition and the design condition for an L ∞ / L 2 type of nonlinear observer-based FD systems. This analytical framework is fundamental for the development of real-time nonlinear FD systems with the aid of some well-established techniques. In the second part, we address the integrated design of the L ∞ / L 2 observer-based FD systems by applying Takagi-Sugeno (T-S) fuzzy dynamic modeling technique as the solution tool. This fuzzy observer-based FD approach is developed via piecewise Lyapunov functions, and can be applied to the case that the premise variables of the FD system is nonsynchronous with the premise variables of the fuzzy model of the plant. In the end, a case study on the laboratory setup of three-tank system is given to show the efficiency of the proposed results.

Periodic Application of Concurrent Error Detection in Processor Array Architectures. PhD. Thesis -

NASA Technical Reports Server (NTRS)

Chen, Paul Peichuan

1993-01-01

Processor arrays can provide an attractive architecture for some applications. Featuring modularity, regular interconnection and high parallelism, such arrays are well-suited for VLSI/WSI implementations, and applications with high computational requirements, such as real-time signal processing. Preserving the integrity of results can be of paramount importance for certain applications. In these cases, fault tolerance should be used to ensure reliable delivery of a system's service. One aspect of fault tolerance is the detection of errors caused by faults. Concurrent error detection (CED) techniques offer the advantage that transient and intermittent faults may be detected with greater probability than with off-line diagnostic tests. Applying time-redundant CED techniques can reduce hardware redundancy costs. However, most time-redundant CED techniques degrade a system's performance.

Detection of Rooftop Cooling Unit Faults Based on Electrical Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, Peter R.; Laughman, C R.; Leeb, S B.

Non-intrusive load monitoring (NILM) is accomplished by sampling voltage and current at high rates and reducing the resulting start transients or harmonic contents to concise ''signatures''. Changes in these signatures can be used to detect, and in many cases directly diagnose, equipment and component faults associated with roof-top cooling units. Use of the NILM for fault detection and diagnosis (FDD) is important because (1) it complements other FDD schemes that are based on thermo-fluid sensors and analyses and (2) it is minimally intrusive (one measuring point in the relatively protected confines of the control panel) and therefore inherently reliable. Thismore » paper describes changes in the power signatures of fans and compressors that were found, experimentally and theoretically, to be useful for fault detection.« less

Integrated analysis of error detection and recovery

NASA Technical Reports Server (NTRS)

Shin, K. G.; Lee, Y. H.

1985-01-01

An integrated modeling and analysis of error detection and recovery is presented. When fault latency and/or error latency exist, the system may suffer from multiple faults or error propagations which seriously deteriorate the fault-tolerant capability. Several detection models that enable analysis of the effect of detection mechanisms on the subsequent error handling operations and the overall system reliability were developed. Following detection of the faulty unit and reconfiguration of the system, the contaminated processes or tasks have to be recovered. The strategies of error recovery employed depend on the detection mechanisms and the available redundancy. Several recovery methods including the rollback recovery are considered. The recovery overhead is evaluated as an index of the capabilities of the detection and reconfiguration mechanisms.

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

NASA Technical Reports Server (NTRS)

Rinehart, Aidan W.; Simon, Donald L.

2015-01-01

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

An Integrated Architecture for Aircraft Engine Performance Monitoring and Fault Diagnostics: Engine Test Results

NASA Technical Reports Server (NTRS)

Rinehart, Aidan W.; Simon, Donald L.

2014-01-01

This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.

A study of fault prediction and reliability assessment in the SEL environment

NASA Technical Reports Server (NTRS)

Basili, Victor R.; Patnaik, Debabrata

1986-01-01

An empirical study on estimation and prediction of faults, prediction of fault detection and correction effort, and reliability assessment in the Software Engineering Laboratory environment (SEL) is presented. Fault estimation using empirical relationships and fault prediction using curve fitting method are investigated. Relationships between debugging efforts (fault detection and correction effort) in different test phases are provided, in order to make an early estimate of future debugging effort. This study concludes with the fault analysis, application of a reliability model, and analysis of a normalized metric for reliability assessment and reliability monitoring during development of software.

Development of Monitoring and Diagnostic Methods for Robots Used In Remediation of Waste Sites - Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, M.

2000-04-01

This project is the first evaluation of model-based diagnostics to hydraulic robot systems. A greater understanding of fault detection for hydraulic robots has been gained, and a new theoretical fault detection model developed and evaluated.

Fault detection for piecewise affine systems with application to ship propulsion systems.

PubMed

Yang, Ying; Linlin, Li; Ding, Steven X; Qiu, Jianbin; Peng, Kaixiang

2017-09-09

In this paper, the design approach of non-synchronized diagnostic observer-based fault detection (FD) systems is investigated for piecewise affine processes via continuous piecewise Lyapunov functions. Considering that the dynamics of piecewise affine systems in different regions can be considerably different, the weighting matrices are used to weight the residual of each region, so as to optimize the fault detectability. A numerical example and a case study on a ship propulsion system are presented in the end to demonstrate the effectiveness of the proposed results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Development and evaluation of a Fault-Tolerant Multiprocessor (FTMP) computer. Volume 3: FTMP test and evaluation

NASA Technical Reports Server (NTRS)

Lala, J. H.; Smith, T. B., III

1983-01-01

The experimental test and evaluation of the Fault-Tolerant Multiprocessor (FTMP) is described. Major objectives of this exercise include expanding validation envelope, building confidence in the system, revealing any weaknesses in the architectural concepts and in their execution in hardware and software, and in general, stressing the hardware and software. To this end, pin-level faults were injected into one LRU of the FTMP and the FTMP response was measured in terms of fault detection, isolation, and recovery times. A total of 21,055 stuck-at-0, stuck-at-1 and invert-signal faults were injected in the CPU, memory, bus interface circuits, Bus Guardian Units, and voters and error latches. Of these, 17,418 were detected. At least 80 percent of undetected faults are estimated to be on unused pins. The multiprocessor identified all detected faults correctly and recovered successfully in each case. Total recovery time for all faults averaged a little over one second. This can be reduced to half a second by including appropriate self-tests.

Fuzzy Inference System Approach for Locating Series, Shunt, and Simultaneous Series-Shunt Faults in Double Circuit Transmission Lines

PubMed Central

Swetapadma, Aleena; Yadav, Anamika

2015-01-01

Many schemes are reported for shunt fault location estimation, but fault location estimation of series or open conductor faults has not been dealt with so far. The existing numerical relays only detect the open conductor (series) fault and give the indication of the faulty phase(s), but they are unable to locate the series fault. The repair crew needs to patrol the complete line to find the location of series fault. In this paper fuzzy based fault detection/classification and location schemes in time domain are proposed for both series faults, shunt faults, and simultaneous series and shunt faults. The fault simulation studies and fault location algorithm have been developed using Matlab/Simulink. Synchronized phasors of voltage and current signals of both the ends of the line have been used as input to the proposed fuzzy based fault location scheme. Percentage of error in location of series fault is within 1% and shunt fault is 5% for all the tested fault cases. Validation of percentage of error in location estimation is done using Chi square test with both 1% and 5% level of significance. PMID:26413088

Evaluating the performance of a fault detection and diagnostic system for vapor compression equipment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Breuker, M.S.; Braun, J.E.

This paper presents a detailed evaluation of the performance of a statistical, rule-based fault detection and diagnostic (FDD) technique presented by Rossi and Braun (1997). Steady-state and transient tests were performed on a simple rooftop air conditioner over a range of conditions and fault levels. The steady-state data without faults were used to train models that predict outputs for normal operation. The transient data with faults were used to evaluate FDD performance. The effect of a number of design variables on FDD sensitivity for different faults was evaluated and two prototype systems were specified for more complete evaluation. Good performancemore » was achieved in detecting and diagnosing five faults using only six temperatures (2 input and 4 output) and linear models. The performance improved by about a factor of two when ten measurements (three input and seven output) and higher order models were used. This approach for evaluating and optimizing the performance of the statistical, rule-based FDD technique could be used as a design and evaluation tool when applying this FDD method to other packaged air-conditioning systems. Furthermore, the approach could also be modified to evaluate the performance of other FDD methods.« less

Fault detection and diagnosis in an industrial fed-batch cell culture process.

PubMed

Gunther, Jon C; Conner, Jeremy S; Seborg, Dale E

2007-01-01

A flexible process monitoring method was applied to industrial pilot plant cell culture data for the purpose of fault detection and diagnosis. Data from 23 batches, 20 normal operating conditions (NOC) and three abnormal, were available. A principal component analysis (PCA) model was constructed from 19 NOC batches, and the remaining NOC batch was used for model validation. Subsequently, the model was used to successfully detect (both offline and online) abnormal process conditions and to diagnose the root causes. This research demonstrates that data from a relatively small number of batches (approximately 20) can still be used to monitor for a wide range of process faults.

Interpretation of Self-Potential anomalies for investigating fault using the Levenberg-Marquardt method: a study case in Pinggirsari, West Java, Indonesia

NASA Astrophysics Data System (ADS)

Fajriani; Srigutomo, Wahyu; Pratomo, Prihandhanu M.

2017-04-01

Self-Potential (SP) method is frequently used to identify subsurface structures based on electrical properties. For fixed geometry problems, SP method is related to simple geometrical shapes of causative bodies such as a sphere, cylinder, and sheet. This approach is implemented to determine the value of parameters such as shape, depth, polarization angle, and electric dipole moment. In this study, the technique was applied for investigation of fault, where the fault is considered as resembling the shape of a sheet representing dike or fault. The investigated fault is located at Pinggirsari village, Bandung regency, West Java, Indonesia. The observed SP anomalies that were measured allegedly above the fault were inverted to estimate all the fault parameters through inverse modeling scheme using the Levenberg-Marquardt method. The inversion scheme was first tested on a synthetic model, where a close agreement between the test parameters and the calculated parameters was achieved. Finally, the schema was carried out to invert the real observed SP anomalies. The results show that the presence of the fault was detected beneath the surface having electric dipole moment K = 41.5 mV, half-fault dimension a = 34 m, depth of the sheet’s center h = 14.6 m, the location of the fault’s center xo = 478.25 m, and the polarization angle to the horizontal plane θ = 334.52° in a clockwise direction.

Monitoring Wind Turbine Loading Using Power Converter Signals

NASA Astrophysics Data System (ADS)

Rieg, C. A.; Smith, C. J.; Crabtree, C. J.

2016-09-01

The ability to detect faults and predict loads on a wind turbine drivetrain's mechanical components cost-effectively is critical to making the cost of wind energy competitive. In order to investigate whether this is possible using the readily available power converter current signals, an existing permanent magnet synchronous generator based wind energy conversion system computer model was modified to include a grid-side converter (GSC) for an improved converter model and a gearbox. The GSC maintains a constant DC link voltage via vector control. The gearbox was modelled as a 3-mass model to allow faults to be included. Gusts and gearbox faults were introduced to investigate the ability of the machine side converter (MSC) current (I q) to detect and quantify loads on the mechanical components. In this model, gearbox faults were not detectable in the I q signal due to shaft stiffness and damping interaction. However, a model that predicts the load change on mechanical wind turbine components using I q was developed and verified using synthetic and real wind data.

How do normal faults grow?

NASA Astrophysics Data System (ADS)

Jackson, Christopher; Bell, Rebecca; Rotevatn, Atle; Tvedt, Anette

2016-04-01

Normal faulting accommodates stretching of the Earth's crust, and it is arguably the most fundamental tectonic process leading to continent rupture and oceanic crust emplacement. Furthermore, the incremental and finite geometries associated with normal faulting dictate landscape evolution, sediment dispersal and hydrocarbon systems development in rifts. Displacement-length scaling relationships compiled from global datasets suggest normal faults grow via a sympathetic increase in these two parameters (the 'isolated fault model'). This model has dominated the structural geology literature for >20 years and underpins the structural and tectono-stratigraphic models developed for active rifts. However, relatively recent analysis of high-quality 3D seismic reflection data suggests faults may grow by rapid establishment of their near-final length prior to significant displacement accumulation (the 'coherent fault model'). The isolated and coherent fault models make very different predictions regarding the tectono-stratigraphic evolution of rift basin, thus assessing their applicability is important. To-date, however, very few studies have explicitly set out to critically test the coherent fault model thus, it may be argued, it has yet to be widely accepted in the structural geology community. Displacement backstripping is a simple graphical technique typically used to determine how faults lengthen and accumulate displacement; this technique should therefore allow us to test the competing fault models. However, in this talk we use several subsurface case studies to show that the most commonly used backstripping methods (the 'original' and 'modified' methods) are, however, of limited value, because application of one over the other requires an a priori assumption of the model most applicable to any given fault; we argue this is illogical given that the style of growth is exactly what the analysis is attempting to determine. We then revisit our case studies and demonstrate that, in the case of seismic-scale growth faults, growth strata thickness patterns and relay zone kinematics, rather than displacement backstripping, should be assessed to directly constrain fault length and thus tip behaviour through time. We conclude that rapid length establishment prior to displacement accumulation may be more common than is typically assumed, thus challenging the well-established, widely cited and perhaps overused, isolated fault model.

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement

PubMed Central

Hao, Yansong; Song, Liuyang; Tang, Gang; Yuan, Hongfang

2018-01-01

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency. PMID:29597280

A Sparsity-Promoted Method Based on Majorization-Minimization for Weak Fault Feature Enhancement.

PubMed

Ren, Bangyue; Hao, Yansong; Wang, Huaqing; Song, Liuyang; Tang, Gang; Yuan, Hongfang

2018-03-28

Fault transient impulses induced by faulty components in rotating machinery usually contain substantial interference. Fault features are comparatively weak in the initial fault stage, which renders fault diagnosis more difficult. In this case, a sparse representation method based on the Majorzation-Minimization (MM) algorithm is proposed to enhance weak fault features and extract the features from strong background noise. However, the traditional MM algorithm suffers from two issues, which are the choice of sparse basis and complicated calculations. To address these challenges, a modified MM algorithm is proposed in which a sparse optimization objective function is designed firstly. Inspired by the Basis Pursuit (BP) model, the optimization function integrates an impulsive feature-preserving factor and a penalty function factor. Second, a modified Majorization iterative method is applied to address the convex optimization problem of the designed function. A series of sparse coefficients can be achieved through iterating, which only contain transient components. It is noteworthy that there is no need to select the sparse basis in the proposed iterative method because it is fixed as a unit matrix. Then the reconstruction step is omitted, which can significantly increase detection efficiency. Eventually, envelope analysis of the sparse coefficients is performed to extract weak fault features. Simulated and experimental signals including bearings and gearboxes are employed to validate the effectiveness of the proposed method. In addition, comparisons are made to prove that the proposed method outperforms the traditional MM algorithm in terms of detection results and efficiency.

Model-based diagnosis through Structural Analysis and Causal Computation for automotive Polymer Electrolyte Membrane Fuel Cell systems

NASA Astrophysics Data System (ADS)

Polverino, Pierpaolo; Frisk, Erik; Jung, Daniel; Krysander, Mattias; Pianese, Cesare

2017-07-01

The present paper proposes an advanced approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems fault detection and isolation through a model-based diagnostic algorithm. The considered algorithm is developed upon a lumped parameter model simulating a whole PEMFC system oriented towards automotive applications. This model is inspired by other models available in the literature, with further attention to stack thermal dynamics and water management. The developed model is analysed by means of Structural Analysis, to identify the correlations among involved physical variables, defined equations and a set of faults which may occur in the system (related to both auxiliary components malfunctions and stack degradation phenomena). Residual generators are designed by means of Causal Computation analysis and the maximum theoretical fault isolability, achievable with a minimal number of installed sensors, is investigated. The achieved results proved the capability of the algorithm to theoretically detect and isolate almost all faults with the only use of stack voltage and temperature sensors, with significant advantages from an industrial point of view. The effective fault isolability is proved through fault simulations at a specific fault magnitude with an advanced residual evaluation technique, to consider quantitative residual deviations from normal conditions and achieve univocal fault isolation.

Fuzzy model-based observers for fault detection in CSTR.

PubMed

Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

2015-11-01

Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

A KPI-based process monitoring and fault detection framework for large-scale processes.

PubMed

Zhang, Kai; Shardt, Yuri A W; Chen, Zhiwen; Yang, Xu; Ding, Steven X; Peng, Kaixiang

2017-05-01

Large-scale processes, consisting of multiple interconnected subprocesses, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel representation of each subprocess, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer

PubMed Central

Kim, Jong-Myon

2018-01-01

An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing’s vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively. PMID:29642459

Bearing Fault Diagnosis by a Robust Higher-Order Super-Twisting Sliding Mode Observer.

PubMed

Piltan, Farzin; Kim, Jong-Myon

2018-04-07

An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing's vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively.

Ontology-Based Method for Fault Diagnosis of Loaders.

PubMed

Xu, Feixiang; Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei

2018-02-28

This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

Ontology-Based Method for Fault Diagnosis of Loaders

PubMed Central

Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei

2018-01-01

This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study. PMID:29495646

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram.

PubMed

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-09-13

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features.

Immunity-Based Aircraft Fault Detection System

NASA Technical Reports Server (NTRS)

Dasgupta, D.; KrishnaKumar, K.; Wong, D.; Berry, M.

2004-01-01

In the study reported in this paper, we have developed and applied an Artificial Immune System (AIS) algorithm for aircraft fault detection, as an extension to a previous work on intelligent flight control (IFC). Though the prior studies had established the benefits of IFC, one area of weakness that needed to be strengthened was the control dead band induced by commanding a failed surface. Since the IFC approach uses fault accommodation with no detection, the dead band, although it reduces over time due to learning, is present and causes degradation in handling qualities. If the failure can be identified, this dead band can be further A ed to ensure rapid fault accommodation and better handling qualities. The paper describes the application of an immunity-based approach that can detect a broad spectrum of known and unforeseen failures. The approach incorporates the knowledge of the normal operational behavior of the aircraft from sensory data, and probabilistically generates a set of pattern detectors that can detect any abnormalities (including faults) in the behavior pattern indicating unsafe in-flight operation. We developed a tool called MILD (Multi-level Immune Learning Detection) based on a real-valued negative selection algorithm that can generate a small number of specialized detectors (as signatures of known failure conditions) and a larger set of generalized detectors for unknown (or possible) fault conditions. Once the fault is detected and identified, an adaptive control system would use this detection information to stabilize the aircraft by utilizing available resources (control surfaces). We experimented with data sets collected under normal and various simulated failure conditions using a piloted motion-base simulation facility. The reported results are from a collection of test cases that reflect the performance of the proposed immunity-based fault detection algorithm.

Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

PubMed

Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

2014-01-01

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

PubMed Central

Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

2014-01-01

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis.

PubMed

Liu, Jinjun; Leng, Yonggang; Lai, Zhihui; Fan, Shengbo

2018-04-25

Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the fault signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method.

Seismic anisotropy around the Gulf of Corinth, Greece, deduced from three-component seismograms of local earthquakes and its relationship with crustal strain

NASA Astrophysics Data System (ADS)

Bouin, Marie-Paule; TéLlez, Julia; Bernard, Pascal

1996-03-01

Several thousand three-component seismograms from local earthquakes recorded during two field experiments in August 1991 and November 1992 in the Gulf of Corinth have been analyzed to detect shear wave splitting. After a first selection of the events located in the S window of the considered stations, a second very strict selection of the records is applied in order to avoid the effect of scattered or converted phases which can mimic the behavior of shear wave splitting. Two main directions of fast S wave polarization have been detected: one oriented N105°E-N120°E, the other N55°E-N75°E. The first one is perpendicular to the main direction of extension of the Gulf provided by focal mechanism, Global Positioning System measurements, and tectonic studies, and is thus consistent with the extensive-dilatancy anisotropy (EDA) model. The second direction is subparallel to the direction of the active normal fault closest to the sites. This suggests a local control of the anisotropy by these active faults, either by a local rotation of the total stress field, in which case the EDA model may still explain the anisotropy, or by the existence of a specific microstructure or macrostructure generated by the long-term fault activity (set of secondary fault planes parallel to the major one), in which case the anisotropy direction would be significantly rotated from the stress direction (about 50°). The anisotropic signature does not seem to be affected by the geology of the site (pre-Tertiary limestone and Pleistocene sediments), except for a station located on the thick Plio-Quaternary deposits of a delta, where the time delay is significantly larger.

Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals

NASA Astrophysics Data System (ADS)

Chen, Jian; Randall, Robert Bond; Peeters, Bart

2016-06-01

Artificial Neural Networks (ANNs) have the potential to solve the problem of automated diagnostics of piston slap faults, but the critical issue for the successful application of ANN is the training of the network by a large amount of data in various engine conditions (different speed/load conditions in normal condition, and with different locations/levels of faults). On the other hand, the latest simulation technology provides a useful alternative in that the effect of clearance changes may readily be explored without recourse to cutting metal, in order to create enough training data for the ANNs. In this paper, based on some existing simplified models of piston slap, an advanced multi-body dynamic simulation software was used to simulate piston slap faults with different speeds/loads and clearance conditions. Meanwhile, the simulation models were validated and updated by a series of experiments. Three-stage network systems are proposed to diagnose piston faults: fault detection, fault localisation and fault severity identification. Multi Layer Perceptron (MLP) networks were used in the detection stage and severity/prognosis stage and a Probabilistic Neural Network (PNN) was used to identify which cylinder has faults. Finally, it was demonstrated that the networks trained purely on simulated data can efficiently detect piston slap faults in real tests and identify the location and severity of the faults as well.

A Game Theoretic Fault Detection Filter

NASA Technical Reports Server (NTRS)

Chung, Walter H.; Speyer, Jason L.

1995-01-01

The fault detection process is modelled as a disturbance attenuation problem. The solution to this problem is found via differential game theory, leading to an H(sub infinity) filter which bounds the transmission of all exogenous signals save the fault to be detected. For a general class of linear systems which includes some time-varying systems, it is shown that this transmission bound can be taken to zero by simultaneously bringing the sensor noise weighting to zero. Thus, in the limit, a complete transmission block can he achieved, making the game filter into a fault detection filter. When we specialize this result to time-invariant system, it is found that the detection filter attained in the limit is identical to the well known Beard-Jones Fault Detection Filter. That is, all fault inputs other than the one to be detected (the "nuisance faults") are restricted to an invariant subspace which is unobservable to a projection on the output. For time-invariant systems, it is also shown that in the limit, the order of the state-space and the game filter can be reduced by factoring out the invariant subspace. The result is a lower dimensional filter which can observe only the fault to be detected. A reduced-order filter can also he generated for time-varying systems, though the computational overhead may be intensive. An example given at the end of the paper demonstrates the effectiveness of the filter as a tool for fault detection and identification.

Detecting and isolating abrupt changes in linear switching systems

NASA Astrophysics Data System (ADS)

Nazari, Sohail; Zhao, Qing; Huang, Biao

2015-04-01

In this paper, a novel fault detection and isolation (FDI) method for switching linear systems is developed. All input and output signals are assumed to be corrupted with measurement noises. In the proposed method, a 'lifted' linear model named as stochastic hybrid decoupling polynomial (SHDP) is introduced. The SHDP model governs the dynamics of the switching linear system with all different modes, and is independent of the switching sequence. The error-in-variable (EIV) representation of SHDP is derived, and is used for the fault residual generation and isolation following the well-adopted local approach. The proposed FDI method can detect and isolate the fault-induced abrupt changes in switching models' parameters without estimating the switching modes. Furthermore, in this paper, the analytical expressions of the gradient vector and Hessian matrix are obtained based on the EIV SHDP formulation, so that they can be used to implement the online fault detection scheme. The performance of the proposed method is then illustrated by simulation examples.

Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

PubMed Central

Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

2014-01-01

A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, and a signal resampler is invented to eliminate the Doppler effect embedded in the acoustic signal of the recorded bearing. Through the Doppler effect eliminator, the five kinematic model parameters can be identified based on the signal itself. Then, the signal resampler is applied to eliminate the Doppler effect using the identified parameters. With the ability to detect early bearing faults, the transient model analysis method is employed to detect localized bearing faults after the embedded Doppler effect is eliminated. The effectiveness of the proposed fault diagnosis strategy is verified via simulation studies and applications to diagnose locomotive roller bearing defects. PMID:24803197

Analysis of the impact of error detection on computer performance

NASA Technical Reports Server (NTRS)

Shin, K. C.; Lee, Y. H.

1983-01-01

Conventionally, reliability analyses either assume that a fault/error is detected immediately following its occurrence, or neglect damages caused by latent errors. Though unrealistic, this assumption was imposed in order to avoid the difficulty of determining the respective probabilities that a fault induces an error and the error is then detected in a random amount of time after its occurrence. As a remedy for this problem a model is proposed to analyze the impact of error detection on computer performance under moderate assumptions. Error latency, the time interval between occurrence and the moment of detection, is used to measure the effectiveness of a detection mechanism. This model is used to: (1) predict the probability of producing an unreliable result, and (2) estimate the loss of computation due to fault and/or error.

Unbalance detection in rotor systems with active bearings using self-sensing piezoelectric actuators

NASA Astrophysics Data System (ADS)

Ambur, Ramakrishnan; Rinderknecht, Stephan

2018-03-01

Machines which are developed today are highly automated due to increased use of mechatronic systems. To ensure their reliable operation, fault detection and isolation (FDI) is an important feature along with a better control. This research work aims to achieve and integrate both these functions with minimum number of components in a mechatronic system. This article investigates a rotating machine with active bearings equipped with piezoelectric actuators. There is an inherent coupling between their electrical and mechanical properties because of which they can also be used as sensors. Mechanical deflection can be reconstructed from these self-sensing actuators from measured voltage and current signals. These virtual sensor signals are utilised to detect unbalance in a rotor system. Parameters of unbalance such as its magnitude and phase are detected by parametric estimation method in frequency domain. Unbalance location has been identified using hypothesis of localization of faults. Robustness of the estimates against outliers in measurements is improved using weighted least squares method. Unbalances are detected in a real test bench apart from simulation using its model. Experiments are performed in stationary as well as in transient case. As a further step unbalances are estimated during simultaneous actuation of actuators in closed loop with an adaptive algorithm for vibration minimisation. This strategy could be used in systems which aim for both fault detection and control action.

Sliding Mode Fault Tolerant Control with Adaptive Diagnosis for Aircraft Engines

NASA Astrophysics Data System (ADS)

Xiao, Lingfei; Du, Yanbin; Hu, Jixiang; Jiang, Bin

2018-03-01

In this paper, a novel sliding mode fault tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors faults and actuators faults into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the fault diagnosis and fault tolerant controller design. Next, a suitable detection observer is designed to detect the faults effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode fault tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of faults converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed fault tolerant control method.

Deformation of conjugate compliant fault zones induced by the 2013 Mw7.7 Baluchistan (Pakistan) earthquake

NASA Astrophysics Data System (ADS)

Dutta, Rishabh; Wang, Teng; Feng, Guangcai; Harrington, Jonathan; Vasyura-Bathke, Hannes; Jónsson, Sigurjón

2017-04-01

Strain localizations in compliant fault zones (with elastic moduli lower than the surrounding rocks) induced by nearby earthquakes have been detected using geodetic observations in a few cases in the past. Here we observe small-scale changes in interferometric Synthetic Aperture Radar (InSAR) measurements along multiple conjugate faults near the rupture of the 2013 Mw7.7 Baluchistan (Pakistan) earthquake. After removing the main coseismic deformation signal in the interferograms and correcting them for topography-related phase, we observe 2-3 cm signal along several conjugate faults that are 15-30 km from the mainshock fault rupture. These conjugate compliant faults have strikes of N30°E and N45°W. The sense of motion indicates left-lateral deformation across the N30°E faults and right-lateral deformation across the N45°W faults, which suggests the conjugate faults were subjected to extensional coseismic stresses along the WSW-ENE direction. The spacing between the different sets of faults is around 5 to 8 km. We explain the observed strain localizations as an elastic response of the compliant conjugate faults induced by the Baluchistan earthquake. Using 3D Finite Element models (FEM), we impose coseismic static displacements due to the earthquake along the boundaries of the FEM domain to reproduce the coseismic stress changes acting across the compliant faults. The InSAR measurements are used to constrain the geometry and rigidity variations of the compliant faults with respect to the surrounding rocks. The best fitting models show the compliant fault zones to have a width of 0.5 km to 2 km and a reduction of the shear modulus by a factor of 3 to 4. Our study yields similar values as were found for compliant fault zones near the 1992 Landers and the 1999 Hector Mine earthquakes in California, although here the strain localization is occurring on more complex conjugate sets of faults.

Ground Motion Simulation for a Large Active Fault System using Empirical Green's Function Method and the Strong Motion Prediction Recipe - a Case Study of the Noubi Fault Zone -

NASA Astrophysics Data System (ADS)

Kuriyama, M.; Kumamoto, T.; Fujita, M.

2005-12-01

The 1995 Hyogo-ken Nambu Earthquake (1995) near Kobe, Japan, spurred research on strong motion prediction. To mitigate damage caused by large earthquakes, a highly precise method of predicting future strong motion waveforms is required. In this study, we applied empirical Green's function method to forward modeling in order to simulate strong ground motion in the Noubi Fault zone and examine issues related to strong motion prediction for large faults. Source models for the scenario earthquakes were constructed using the recipe of strong motion prediction (Irikura and Miyake, 2001; Irikura et al., 2003). To calculate the asperity area ratio of a large fault zone, the results of a scaling model, a scaling model with 22% asperity by area, and a cascade model were compared, and several rupture points and segmentation parameters were examined for certain cases. A small earthquake (Mw: 4.6) that occurred in northern Fukui Prefecture in 2004 were examined as empirical Green's function, and the source spectrum of this small event was found to agree with the omega-square scaling law. The Nukumi, Neodani, and Umehara segments of the 1891 Noubi Earthquake were targeted in the present study. The positions of the asperity area and rupture starting points were based on the horizontal displacement distributions reported by Matsuda (1974) and the fault branching pattern and rupture direction model proposed by Nakata and Goto (1998). Asymmetry in the damage maps for the Noubi Earthquake was then examined. We compared the maximum horizontal velocities for each case that had a different rupture starting point. In the case, rupture started at the center of the Nukumi Fault, while in another case, rupture started on the southeastern edge of the Umehara Fault; the scaling model showed an approximately 2.1-fold difference between these cases at observation point FKI005 of K-Net. This difference is considered to relate to the directivity effect associated with the direction of rupture propagation. Moreover, it was clarified that the horizontal velocities by assuming the cascade model was underestimated more than one standard deviation of empirical relation by Si and Midorikawa (1999). The scaling and cascade models showed an approximately 6.4-fold difference for the case, in which the rupture started along the southeastern edge of the Umehara Fault at observation point GIF020. This difference is significantly large in comparison with the effect of different rupture starting points, and shows that it is important to base scenario earthquake assumptions on active fault datasets before establishing the source characterization model. The distribution map of seismic intensity for the 1891 Noubi Earthquake also suggests that the synthetic waveforms in the southeastern Noubi Fault zone may be underestimated. Our results indicate that outer fault parameters (e.g., earthquake moment) related to the construction of scenario earthquakes influence strong motion prediction, rather than inner fault parameters such as the rupture starting point. Based on these methods, we will predict strong motion for approximately 140 to 150 km of the Itoigawa-Shizuoka Tectonic Line.

Detection of faults and software reliability analysis

NASA Technical Reports Server (NTRS)

Knight, J. C.

1987-01-01

Specific topics briefly addressed include: the consistent comparison problem in N-version system; analytic models of comparison testing; fault tolerance through data diversity; and the relationship between failures caused by automatically seeded faults.

Automatic Detection of Electric Power Troubles (ADEPT)

NASA Technical Reports Server (NTRS)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint; Brady, Mike; Ford, Donnie

1988-01-01

Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base.

Automatic Detection of Electric Power Troubles (ADEPT)

NASA Astrophysics Data System (ADS)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint; Brady, Mike; Ford, Donnie

1988-11-01

Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base.

A novelty detection diagnostic methodology for gearboxes operating under fluctuating operating conditions using probabilistic techniques

NASA Astrophysics Data System (ADS)

Schmidt, S.; Heyns, P. S.; de Villiers, J. P.

2018-02-01

In this paper, a fault diagnostic methodology is developed which is able to detect, locate and trend gear faults under fluctuating operating conditions when only vibration data from a single transducer, measured on a healthy gearbox are available. A two-phase feature extraction and modelling process is proposed to infer the operating condition and based on the operating condition, to detect changes in the machine condition. Information from optimised machine and operating condition hidden Markov models are statistically combined to generate a discrepancy signal which is post-processed to infer the condition of the gearbox. The discrepancy signal is processed and combined with statistical methods for automatic fault detection and localisation and to perform fault trending over time. The proposed methodology is validated on experimental data and a tacholess order tracking methodology is used to enhance the cost-effectiveness of the diagnostic methodology.

Simultaneous Sensor and Process Fault Diagnostics for Propellant Feed System

NASA Technical Reports Server (NTRS)

Cao, J.; Kwan, C.; Figueroa, F.; Xu, R.

2006-01-01

The main objective of this research is to extract fault features from sensor faults and process faults by using advanced fault detection and isolation (FDI) algorithms. A tank system that has some common characteristics to a NASA testbed at Stennis Space Center was used to verify our proposed algorithms. First, a generic tank system was modeled. Second, a mathematical model suitable for FDI has been derived for the tank system. Third, a new and general FDI procedure has been designed to distinguish process faults and sensor faults. Extensive simulations clearly demonstrated the advantages of the new design.

Modeling of fault reactivation and induced seismicity during hydraulic fracturing of shale-gas reservoirs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rutqvist, Jonny; Rinaldi, Antonio P.; Cappa, Frédéric

2013-07-01

We have conducted numerical simulation studies to assess the potential for injection-induced fault reactivation and notable seismic events associated with shale-gas hydraulic fracturing operations. The modeling is generally tuned towards conditions usually encountered in the Marcellus shale play in the Northeastern US at an approximate depth of 1500 m (~;;4,500 feet). Our modeling simulations indicate that when faults are present, micro-seismic events are possible, the magnitude of which is somewhat larger than the one associated with micro-seismic events originating from regular hydraulic fracturing because of the larger surface area that is available for rupture. The results of our simulations indicatedmore » fault rupture lengths of about 10 to 20 m, which, in rare cases can extend to over 100 m, depending on the fault permeability, the in situ stress field, and the fault strength properties. In addition to a single event rupture length of 10 to 20 m, repeated events and aseismic slip amounted to a total rupture length of 50 m, along with a shear offset displacement of less than 0.01 m. This indicates that the possibility of hydraulically induced fractures at great depth (thousands of meters) causing activation of faults and creation of a new flow path that can reach shallow groundwater resources (or even the surface) is remote. The expected low permeability of faults in producible shale is clearly a limiting factor for the possible rupture length and seismic magnitude. In fact, for a fault that is initially nearly-impermeable, the only possibility of larger fault slip event would be opening by hydraulic fracturing; this would allow pressure to penetrate the matrix along the fault and to reduce the frictional strength over a sufficiently large fault surface patch. However, our simulation results show that if the fault is initially impermeable, hydraulic fracturing along the fault results in numerous small micro-seismic events along with the propagation, effectively preventing larger events from occurring. Nevertheless, care should be taken with continuous monitoring of induced seismicity during the entire injection process to detect any runaway fracturing along faults.« less

Series and parallel arc-fault circuit interrupter tests.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jay Dean; Fresquez, Armando J.; Gudgel, Bob

2013-07-01

While the 2011 National Electrical Codeª (NEC) only requires series arc-fault protection, some arc-fault circuit interrupter (AFCI) manufacturers are designing products to detect and mitigate both series and parallel arc-faults. Sandia National Laboratories (SNL) has extensively investigated the electrical differences of series and parallel arc-faults and has offered possible classification and mitigation solutions. As part of this effort, Sandia National Laboratories has collaborated with MidNite Solar to create and test a 24-string combiner box with an AFCI which detects, differentiates, and de-energizes series and parallel arc-faults. In the case of the MidNite AFCI prototype, series arc-faults are mitigated by openingmore » the PV strings, whereas parallel arc-faults are mitigated by shorting the array. A range of different experimental series and parallel arc-fault tests with the MidNite combiner box were performed at the Distributed Energy Technologies Laboratory (DETL) at SNL in Albuquerque, NM. In all the tests, the prototype de-energized the arc-faults in the time period required by the arc-fault circuit interrupt testing standard, UL 1699B. The experimental tests confirm series and parallel arc-faults can be successfully mitigated with a combiner box-integrated solution.« less

Model Based Inference for Wire Chafe Diagnostics

NASA Technical Reports Server (NTRS)

Schuet, Stefan R.; Wheeler, Kevin R.; Timucin, Dogan A.; Wysocki, Philip F.; Kowalski, Marc Edward

2009-01-01

Presentation for Aging Aircraft conference covering chafing fault diagnostics using Time Domain Reflectometry. Laboratory setup and experimental methods are presented, along with initial results that summarize fault modeling and detection capabilities.

Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis.

PubMed

Talhaoui, Hicham; Menacer, Arezki; Kessal, Abdelhalim; Kechida, Ridha

2014-09-01

This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

PubMed

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-06-17

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

PubMed Central

Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

2016-01-01

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

Hidden Markov models and neural networks for fault detection in dynamic systems

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

Neural networks plus hidden Markov models (HMM) can provide excellent detection and false alarm rate performance in fault detection applications, as shown in this viewgraph presentation. Modified models allow for novelty detection. Key contributions of neural network models are: (1) excellent nonparametric discrimination capability; (2) a good estimator of posterior state probabilities, even in high dimensions, and thus can be embedded within overall probabilistic model (HMM); and (3) simple to implement compared to other nonparametric models. Neural network/HMM monitoring model is currently being integrated with the new Deep Space Network (DSN) antenna controller software and will be on-line monitoring a new DSN 34-m antenna (DSS-24) by July, 1994.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model.

PubMed

Seera, Manjeevan; Lim, Chee Peng; Ishak, Dahaman; Singh, Harapajan

2012-01-01

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Improving Multiple Fault Diagnosability using Possible Conflicts

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Bregon, Anibal; Biswas, Gautam; Koutsoukos, Xenofon; Pulido, Belarmino

2012-01-01

Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.

Italian Case Studies Modelling Complex Earthquake Sources In PSHA

NASA Astrophysics Data System (ADS)

Gee, Robin; Peruzza, Laura; Pagani, Marco

2017-04-01

This study presents two examples of modelling complex seismic sources in Italy, done in the framework of regional probabilistic seismic hazard assessment (PSHA). The first case study is for an area centred around Collalto Stoccaggio, a natural gas storage facility in Northern Italy, located within a system of potentially seismogenic thrust faults in the Venetian Plain. The storage exploits a depleted natural gas reservoir located within an actively growing anticline, which is likely driven by the Montello Fault, the underlying blind thrust. This fault has been well identified by microseismic activity (M<2) detected by a local seismometric network installed in 2012 (http://rete-collalto.crs.inogs.it/). At this time, no correlation can be identified between the gas storage activity and local seismicity, so we proceed with a PSHA that considers only natural seismicity, where the rates of earthquakes are assumed to be time-independent. The source model consists of faults and distributed seismicity to consider earthquakes that cannot be associated to specific structures. All potentially active faults within 50 km of the site are considered, and are modelled as 3D listric surfaces, consistent with the proposed geometry of the Montello Fault. Slip rates are constrained using available geological, geophysical and seismological information. We explore the sensitivity of the hazard results to various parameters affected by epistemic uncertainty, such as ground motions prediction equations with different rupture-to-site distance metrics, fault geometry, and maximum magnitude. The second case is an innovative study, where we perform aftershock probabilistic seismic hazard assessment (APSHA) in Central Italy, following the Amatrice M6.1 earthquake of August 24th, 2016 (298 casualties) and the subsequent earthquakes of Oct 26th and 30th (M6.1 and M6.6 respectively, no deaths). The aftershock hazard is modelled using a fault source with complex geometry, based on literature data and field evidence associated with the August mainshock. Earthquake activity rates during the very first weeks after the deadly earthquake were used to calibrated an Omori-Utsu decay curve, and the magnitude distribution of aftershocks is assumed to follow a Gutenberg-Richter distribution. We apply uniform and non-uniform spatial distribution of the seismicity across the fault source, by modulating the rates as a decreasing function of distance from the mainshock. The hazard results are computed for short-exposure periods (1 month, before the occurrences of October earthquakes) and compared to the background hazard given by law (MPS04), and to observations at some reference sites. We also show the results of disaggregation computed for the city of Amatrice. Finally, we attempt to update the results in light of the new "main" events that occurred afterwards in the region. All source modeling and hazard calculations are performed using the OpenQuake engine. We discuss the novelties of these works, and the benefits and limitations of both analyses, particularly in such different contexts of seismic hazard.

Power plant fault detection using artificial neural network

NASA Astrophysics Data System (ADS)

Thanakodi, Suresh; Nazar, Nazatul Shiema Moh; Joini, Nur Fazriana; Hidzir, Hidzrin Dayana Mohd; Awira, Mohammad Zulfikar Khairul

2018-02-01

The fault that commonly occurs in power plants is due to various factors that affect the system outage. There are many types of faults in power plants such as single line to ground fault, double line to ground fault, and line to line fault. The primary aim of this paper is to diagnose the fault in 14 buses power plants by using an Artificial Neural Network (ANN). The Multilayered Perceptron Network (MLP) that detection trained utilized the offline training methods such as Gradient Descent Backpropagation (GDBP), Levenberg-Marquardt (LM), and Bayesian Regularization (BR). The best method is used to build the Graphical User Interface (GUI). The modelling of 14 buses power plant, network training, and GUI used the MATLAB software.

Verifying Digital Components of Physical Systems: Experimental Evaluation of Test Quality

NASA Astrophysics Data System (ADS)

Laputenko, A. V.; López, J. E.; Yevtushenko, N. V.

2018-03-01

This paper continues the study of high quality test derivation for verifying digital components which are used in various physical systems; those are sensors, data transfer components, etc. We have used logic circuits b01-b010 of the package of ITC'99 benchmarks (Second Release) for experimental evaluation which as stated before, describe digital components of physical systems designed for various applications. Test sequences are derived for detecting the most known faults of the reference logic circuit using three different approaches to test derivation. Three widely used fault types such as stuck-at-faults, bridges, and faults which slightly modify the behavior of one gate are considered as possible faults of the reference behavior. The most interesting test sequences are short test sequences that can provide appropriate guarantees after testing, and thus, we experimentally study various approaches to the derivation of the so-called complete test suites which detect all fault types. In the first series of experiments, we compare two approaches for deriving complete test suites. In the first approach, a shortest test sequence is derived for testing each fault. In the second approach, a test sequence is pseudo-randomly generated by the use of an appropriate software for logic synthesis and verification (ABC system in our study) and thus, can be longer. However, after deleting sequences detecting the same set of faults, a test suite returned by the second approach is shorter. The latter underlines the fact that in many cases it is useless to spend `time and efforts' for deriving a shortest distinguishing sequence; it is better to use the test minimization afterwards. The performed experiments also show that the use of only randomly generated test sequences is not very efficient since such sequences do not detect all the faults of any type. After reaching the fault coverage around 70%, saturation is observed, and the fault coverage cannot be increased anymore. For deriving high quality short test suites, the approach that is the combination of randomly generated sequences together with sequences which are aimed to detect faults not detected by random tests, allows to reach the good fault coverage using shortest test sequences.

Multi-Frequency Signal Detection Based on Frequency Exchange and Re-Scaling Stochastic Resonance and Its Application to Weak Fault Diagnosis

PubMed Central

Leng, Yonggang; Fan, Shengbo

2018-01-01

Mechanical fault diagnosis usually requires not only identification of the fault characteristic frequency, but also detection of its second and/or higher harmonics. However, it is difficult to detect a multi-frequency fault signal through the existing Stochastic Resonance (SR) methods, because the characteristic frequency of the fault signal as well as its second and higher harmonics frequencies tend to be large parameters. To solve the problem, this paper proposes a multi-frequency signal detection method based on Frequency Exchange and Re-scaling Stochastic Resonance (FERSR). In the method, frequency exchange is implemented using filtering technique and Single SideBand (SSB) modulation. This new method can overcome the limitation of "sampling ratio" which is the ratio of the sampling frequency to the frequency of target signal. It also ensures that the multi-frequency target signals can be processed to meet the small-parameter conditions. Simulation results demonstrate that the method shows good performance for detecting a multi-frequency signal with low sampling ratio. Two practical cases are employed to further validate the effectiveness and applicability of this method. PMID:29693577

Weak Fault Feature Extraction of Rolling Bearings Based on an Improved Kurtogram

PubMed Central

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-01-01

Kurtograms have been verified to be an efficient tool in bearing fault detection and diagnosis because of their superiority in extracting transient features. However, the short-time Fourier Transform is insufficient in time-frequency analysis and kurtosis is deficient in detecting cyclic transients. Those factors weaken the performance of the original kurtogram in extracting weak fault features. Correlated Kurtosis (CK) is then designed, as a more effective solution, in detecting cyclic transients. Redundant Second Generation Wavelet Packet Transform (RSGWPT) is deemed to be effective in capturing more detailed local time-frequency description of the signal, and restricting the frequency aliasing components of the analysis results. The authors in this manuscript, combining the CK with the RSGWPT, propose an improved kurtogram to extract weak fault features from bearing vibration signals. The analysis of simulation signals and real application cases demonstrate that the proposed method is relatively more accurate and effective in extracting weak fault features. PMID:27649171

A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

NASA Technical Reports Server (NTRS)

Tejada, Arturo

2009-01-01

An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

Fault detection and accommodation testing on an F100 engine in an F-15 airplane. [digital engine control system

NASA Technical Reports Server (NTRS)

Myers, L. P.; Baer-Riedhart, J. L.; Maxwell, M. D.

1985-01-01

The fault detection and accommodation (FDA) methods that can be used for digital engine control systems are presently subjected to a flight test program in the case of the F-15 fighter's F100 engine electronic controls, inducing selected faults and then evaluating the resulting digital engine control responses. In general, flight test results were found to compare well with both ground tests and predictions. It is noted that the inducement of dual-pressure failures was not feasible, since FDA logic was not designed to accommodate them.

Automatic Detection of Electric Power Troubles (ADEPT)

NASA Technical Reports Server (NTRS)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint; Brady, Mike; Ford, Donnie

1988-01-01

ADEPT is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system, and is designed for two modes of operation: real-time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a Laser printer. This system consists of a simulated Space Station power module using direct-current power supplies for Solar arrays on three power busses. For tests of the system's ability to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three busses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modelling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base. A load scheduler and a fault recovery system are currently under development to support both modes of operation.

An improved PCA method with application to boiler leak detection.

PubMed

Sun, Xi; Marquez, Horacio J; Chen, Tongwen; Riaz, Muhammad

2005-07-01

Principal component analysis (PCA) is a popular fault detection technique. It has been widely used in process industries, especially in the chemical industry. In industrial applications, achieving a sensitive system capable of detecting incipient faults, which maintains the false alarm rate to a minimum, is a crucial issue. Although a lot of research has been focused on these issues for PCA-based fault detection and diagnosis methods, sensitivity of the fault detection scheme versus false alarm rate continues to be an important issue. In this paper, an improved PCA method is proposed to address this problem. In this method, a new data preprocessing scheme and a new fault detection scheme designed for Hotelling's T2 as well as the squared prediction error are developed. A dynamic PCA model is also developed for boiler leak detection. This new method is applied to boiler water/steam leak detection with real data from Syncrude Canada's utility plant in Fort McMurray, Canada. Our results demonstrate that the proposed method can effectively reduce false alarm rate, provide effective and correct leak alarms, and give early warning to operators.

Detection of faults and software reliability analysis

NASA Technical Reports Server (NTRS)

Knight, John C.

1987-01-01

Multi-version or N-version programming is proposed as a method of providing fault tolerance in software. The approach requires the separate, independent preparation of multiple versions of a piece of software for some application. These versions are executed in parallel in the application environment; each receives identical inputs and each produces its version of the required outputs. The outputs are collected by a voter and, in principle, they should all be the same. In practice there may be some disagreement. If this occurs, the results of the majority are taken to be the correct output, and that is the output used by the system. A total of 27 programs were produced. Each of these programs was then subjected to one million randomly-generated test cases. The experiment yielded a number of programs containing faults that are useful for general studies of software reliability as well as studies of N-version programming. Fault tolerance through data diversity and analytic models of comparison testing are discussed.

Process fault detection and nonlinear time series analysis for anomaly detection in safeguards

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burr, T.L.; Mullen, M.F.; Wangen, L.E.

In this paper we discuss two advanced techniques, process fault detection and nonlinear time series analysis, and apply them to the analysis of vector-valued and single-valued time-series data. We investigate model-based process fault detection methods for analyzing simulated, multivariate, time-series data from a three-tank system. The model-predictions are compared with simulated measurements of the same variables to form residual vectors that are tested for the presence of faults (possible diversions in safeguards terminology). We evaluate two methods, testing all individual residuals with a univariate z-score and testing all variables simultaneously with the Mahalanobis distance, for their ability to detect lossmore » of material from two different leak scenarios from the three-tank system: a leak without and with replacement of the lost volume. Nonlinear time-series analysis tools were compared with the linear methods popularized by Box and Jenkins. We compare prediction results using three nonlinear and two linear modeling methods on each of six simulated time series: two nonlinear and four linear. The nonlinear methods performed better at predicting the nonlinear time series and did as well as the linear methods at predicting the linear values.« less

Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection

NASA Astrophysics Data System (ADS)

Yuan, Jing; Ji, Feng; Gao, Yuan; Zhu, Jun; Wei, Chenjun; Zhou, Yu

2018-05-01

A new branch of fault detection is utilizing the noise such as enhancing, adding or estimating the noise so as to improve the signal-to-noise ratio (SNR) and extract the fault signatures. Hereinto, ensemble noise-reconstructed empirical mode decomposition (ENEMD) is a novel noise utilization method to ameliorate the mode mixing and denoised the intrinsic mode functions (IMFs). Despite the possibility of superior performance in detecting weak and multiple faults, the method still suffers from the major problems of the user-defined parameter and the powerless capability for a high SNR case. Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy. Independent from the artificial setup, the noise estimation by the minimax thresholding is improved for a low SNR case, which especially shows an outstanding interpretation for signature enhancement. For approximating the weak noise precisely, the noise estimation by the local reconfiguration using singular value decomposition (SVD) is proposed for a high SNR case, which is particularly powerful for reducing the mode mixing. Thereinto, the sliding window for projecting the phase space is optimally designed by the correlation minimization. Meanwhile, the reasonable singular order for the local reconfiguration to estimate the noise is determined by the inflection point of the increment trend of normalized singular entropy. Furthermore, the noise estimation strategy, i.e. the selection approaches of the two estimation techniques along with the critical case, is developed and discussed for different SNRs by means of the possible noise-only IMF family. The method is validated by the repeatable simulations to demonstrate the synthetical performance and especially confirm the capability of noise estimation. Finally, the method is applied to detect the local wear fault from a dual-axis stabilized platform and the gear crack from an operating electric locomotive to verify its effectiveness and feasibility.

An Analytical Model for Assessing Stability of Pre-Existing Faults in Caprock Caused by Fluid Injection and Extraction in a Reservoir

NASA Astrophysics Data System (ADS)

Wang, Lei; Bai, Bing; Li, Xiaochun; Liu, Mingze; Wu, Haiqing; Hu, Shaobin

2016-07-01

Induced seismicity and fault reactivation associated with fluid injection and depletion were reported in hydrocarbon, geothermal, and waste fluid injection fields worldwide. Here, we establish an analytical model to assess fault reactivation surrounding a reservoir during fluid injection and extraction that considers the stress concentrations at the fault tips and the effects of fault length. In this model, induced stress analysis in a full-space under the plane strain condition is implemented based on Eshelby's theory of inclusions in terms of a homogeneous, isotropic, and poroelastic medium. The stress intensity factor concept in linear elastic fracture mechanics is adopted as an instability criterion for pre-existing faults in surrounding rocks. To characterize the fault reactivation caused by fluid injection and extraction, we define a new index, the "fault reactivation factor" η, which can be interpreted as an index of fault stability in response to fluid pressure changes per unit within a reservoir resulting from injection or extraction. The critical fluid pressure change within a reservoir is also determined by the superposition principle using the in situ stress surrounding a fault. Our parameter sensitivity analyses show that the fault reactivation tendency is strongly sensitive to fault location, fault length, fault dip angle, and Poisson's ratio of the surrounding rock. Our case study demonstrates that the proposed model focuses on the mechanical behavior of the whole fault, unlike the conventional methodologies. The proposed method can be applied to engineering cases related to injection and depletion within a reservoir owing to its efficient computational codes implementation.

Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

NASA Astrophysics Data System (ADS)

Li, Gang; McDonald, Geoff L.; Zhao, Qing

2017-01-01

This paper presents a novel Sinusoidal Synthesis Based Adaptive Tracking (SSBAT) technique for vibration-based rotating machinery fault detection. The proposed SSBAT algorithm is an adaptive time series technique that makes use of both frequency and time domain information of vibration signals. Such information is incorporated in a time varying dynamic model. Signal tracking is then realized by applying adaptive sinusoidal synthesis to the vibration signal. A modified Least-Squares (LS) method is adopted to estimate the model parameters. In addition to tracking, the proposed vibration synthesis model is mainly used as a linear time-varying predictor. The health condition of the rotating machine is monitored by checking the residual between the predicted and measured signal. The SSBAT method takes advantage of the sinusoidal nature of vibration signals and transfers the nonlinear problem into a linear adaptive problem in the time domain based on a state-space realization. It has low computation burden and does not need a priori knowledge of the machine under the no-fault condition which makes the algorithm ideal for on-line fault detection. The method is validated using both numerical simulation and practical application data. Meanwhile, the fault detection results are compared with the commonly adopted autoregressive (AR) and autoregressive Minimum Entropy Deconvolution (ARMED) method to verify the feasibility and performance of the SSBAT method.

Development and evaluation of virtual refrigerant mass flow sensors for fault detection and diagnostics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Woohyun; Braun, J.

Refrigerant mass flow rate is an important measurement for monitoring equipment performance and enabling fault detection and diagnostics. However, a traditional mass flow meter is expensive to purchase and install. A virtual refrigerant mass flow sensor (VRMF) uses a mathematical model to estimate flow rate using low-cost measurements and can potentially be implemented at low cost. This study evaluates three VRMFs for estimating refrigerant mass flow rate. The first model uses a compressor map that relates refrigerant flow rate to measurements of inlet and outlet pressure, and inlet temperature measurements. The second model uses an energy-balance method on the compressormore » that uses a compressor map for power consumption, which is relatively independent of compressor faults that influence mass flow rate. The third model is developed using an empirical correlation for an electronic expansion valve (EEV) based on an orifice equation. The three VRMFs are shown to work well in estimating refrigerant mass flow rate for various systems under fault-free conditions with less than 5% RMS error. Each of the three mass flow rate estimates can be utilized to diagnose and track the following faults: 1) loss of compressor performance, 2) fouled condenser or evaporator filter, 3) faulty expansion device, respectively. For example, a compressor refrigerant flow map model only provides an accurate estimation when the compressor operates normally. When a compressor is not delivering the expected flow due to a leaky suction or discharge valve or other internal fault, the energy-balance or EEV model can provide accurate flow estimates. In this paper, the flow differences provide an indication of loss of compressor performance and can be used for fault detection and diagnostics.« less

Model-based design and experimental verification of a monitoring concept for an active-active electromechanical aileron actuation system

NASA Astrophysics Data System (ADS)

Arriola, David; Thielecke, Frank

2017-09-01

Electromechanical actuators have become a key technology for the onset of power-by-wire flight control systems in the next generation of commercial aircraft. The design of robust control and monitoring functions for these devices capable to mitigate the effects of safety-critical faults is essential in order to achieve the required level of fault tolerance. A primary flight control system comprising two electromechanical actuators nominally operating in active-active mode is considered. A set of five signal-based monitoring functions are designed using a detailed model of the system under consideration which includes non-linear parasitic effects, measurement and data acquisition effects, and actuator faults. Robust detection thresholds are determined based on the analysis of parametric and input uncertainties. The designed monitoring functions are verified experimentally and by simulation through the injection of faults in the validated model and in a test-rig suited to the actuation system under consideration, respectively. They guarantee a robust and efficient fault detection and isolation with a low risk of false alarms, additionally enabling the correct reconfiguration of the system for an enhanced operational availability. In 98% of the performed experiments and simulations, the correct faults were detected and confirmed within the time objectives set.

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of a Baseline System

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2008-01-01

In this paper, a baseline system which utilizes dual-channel sensor measurements for aircraft engine on-line diagnostics is developed. This system is composed of a linear on-board engine model (LOBEM) and fault detection and isolation (FDI) logic. The LOBEM provides the analytical third channel against which the dual-channel measurements are compared. When the discrepancy among the triplex channels exceeds a tolerance level, the FDI logic determines the cause of the discrepancy. Through this approach, the baseline system achieves the following objectives: (1) anomaly detection, (2) component fault detection, and (3) sensor fault detection and isolation. The performance of the baseline system is evaluated in a simulation environment using faults in sensors and components.

Feasibility Study on the Use of On-line Multivariate Statistical Process Control for Safeguards Applications in Natural Uranium Conversion Plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ladd-Lively, Jennifer L

2014-01-01

The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component inmore » the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or normal operating conditions of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.« less

Reliability Assessment for Low-cost Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Freeman, Paul Michael

Existing low-cost unmanned aerospace systems are unreliable, and engineers must blend reliability analysis with fault-tolerant control in novel ways. This dissertation introduces the University of Minnesota unmanned aerial vehicle flight research platform, a comprehensive simulation and flight test facility for reliability and fault-tolerance research. An industry-standard reliability assessment technique, the failure modes and effects analysis, is performed for an unmanned aircraft. Particular attention is afforded to the control surface and servo-actuation subsystem. Maintaining effector health is essential for safe flight; failures may lead to loss of control incidents. Failure likelihood, severity, and risk are qualitatively assessed for several effector failure modes. Design changes are recommended to improve aircraft reliability based on this analysis. Most notably, the control surfaces are split, providing independent actuation and dual-redundancy. The simulation models for control surface aerodynamic effects are updated to reflect the split surfaces using a first-principles geometric analysis. The failure modes and effects analysis is extended by using a high-fidelity nonlinear aircraft simulation. A trim state discovery is performed to identify the achievable steady, wings-level flight envelope of the healthy and damaged vehicle. Tolerance of elevator actuator failures is studied using familiar tools from linear systems analysis. This analysis reveals significant inherent performance limitations for candidate adaptive/reconfigurable control algorithms used for the vehicle. Moreover, it demonstrates how these tools can be applied in a design feedback loop to make safety-critical unmanned systems more reliable. Control surface impairments that do occur must be quickly and accurately detected. This dissertation also considers fault detection and identification for an unmanned aerial vehicle using model-based and model-free approaches and applies those algorithms to experimental faulted and unfaulted flight test data. Flight tests are conducted with actuator faults that affect the plant input and sensor faults that affect the vehicle state measurements. A model-based detection strategy is designed and uses robust linear filtering methods to reject exogenous disturbances, e.g. wind, while providing robustness to model variation. A data-driven algorithm is developed to operate exclusively on raw flight test data without physical model knowledge. The fault detection and identification performance of these complementary but different methods is compared. Together, enhanced reliability assessment and multi-pronged fault detection and identification techniques can help to bring about the next generation of reliable low-cost unmanned aircraft.

Real-time fault diagnosis for propulsion systems

NASA Technical Reports Server (NTRS)

Merrill, Walter C.; Guo, Ten-Huei; Delaat, John C.; Duyar, Ahmet

1991-01-01

Current research toward real time fault diagnosis for propulsion systems at NASA-Lewis is described. The research is being applied to both air breathing and rocket propulsion systems. Topics include fault detection methods including neural networks, system modeling, and real time implementations.

Automatic Fault Characterization via Abnormality-Enhanced Classification

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bronevetsky, G; Laguna, I; de Supinski, B R

Enterprise and high-performance computing systems are growing extremely large and complex, employing hundreds to hundreds of thousands of processors and software/hardware stacks built by many people across many organizations. As the growing scale of these machines increases the frequency of faults, system complexity makes these faults difficult to detect and to diagnose. Current system management techniques, which focus primarily on efficient data access and query mechanisms, require system administrators to examine the behavior of various system services manually. Growing system complexity is making this manual process unmanageable: administrators require more effective management tools that can detect faults and help tomore » identify their root causes. System administrators need timely notification when a fault is manifested that includes the type of fault, the time period in which it occurred and the processor on which it originated. Statistical modeling approaches can accurately characterize system behavior. However, the complex effects of system faults make these tools difficult to apply effectively. This paper investigates the application of classification and clustering algorithms to fault detection and characterization. We show experimentally that naively applying these methods achieves poor accuracy. Further, we design novel techniques that combine classification algorithms with information on the abnormality of application behavior to improve detection and characterization accuracy. Our experiments demonstrate that these techniques can detect and characterize faults with 65% accuracy, compared to just 5% accuracy for naive approaches.« less

Hierarchical Simulation to Assess Hardware and Software Dependability

NASA Technical Reports Server (NTRS)

Ries, Gregory Lawrence

1997-01-01

This thesis presents a method for conducting hierarchical simulations to assess system hardware and software dependability. The method is intended to model embedded microprocessor systems. A key contribution of the thesis is the idea of using fault dictionaries to propagate fault effects upward from the level of abstraction where a fault model is assumed to the system level where the ultimate impact of the fault is observed. A second important contribution is the analysis of the software behavior under faults as well as the hardware behavior. The simulation method is demonstrated and validated in four case studies analyzing Myrinet, a commercial, high-speed networking system. One key result from the case studies shows that the simulation method predicts the same fault impact 87.5% of the time as is obtained by similar fault injections into a real Myrinet system. Reasons for the remaining discrepancy are examined in the thesis. A second key result shows the reduction in the number of simulations needed due to the fault dictionary method. In one case study, 500 faults were injected at the chip level, but only 255 propagated to the system level. Of these 255 faults, 110 shared identical fault dictionary entries at the system level and so did not need to be resimulated. The necessary number of system-level simulations was therefore reduced from 500 to 145. Finally, the case studies show how the simulation method can be used to improve the dependability of the target system. The simulation analysis was used to add recovery to the target software for the most common fault propagation mechanisms that would cause the software to hang. After the modification, the number of hangs was reduced by 60% for fault injections into the real system.

Weak fault detection and health degradation monitoring using customized standard multiwavelets

NASA Astrophysics Data System (ADS)

Yuan, Jing; Wang, Yu; Peng, Yizhen; Wei, Chenjun

2017-09-01

Due to the nonobvious symptoms contaminated by a large amount of background noise, it is challenging to beforehand detect and predictively monitor the weak faults for machinery security assurance. Multiwavelets can act as adaptive non-stationary signal processing tools, potentially viable for weak fault diagnosis. However, the signal-based multiwavelets suffer from such problems as the imperfect properties missing the crucial orthogonality, the decomposition distortion impossibly reflecting the relationships between the faults and signatures, the single objective optimization and independence for fault prognostic. Thus, customized standard multiwavelets are proposed for weak fault detection and health degradation monitoring, especially the weak fault signature quantitative identification. First, the flexible standard multiwavelets are designed using the construction method derived from scalar wavelets, seizing the desired properties for accurate detection of weak faults and avoiding the distortion issue for feature quantitative identification. Second, the multi-objective optimization combined three dimensionless indicators of the normalized energy entropy, normalized singular entropy and kurtosis index is introduced to the evaluation criterions, and benefits for selecting the potential best basis functions for weak faults without the influence of the variable working condition. Third, an ensemble health indicator fused by the kurtosis index, impulse index and clearance index of the original signal along with the normalized energy entropy and normalized singular entropy by the customized standard multiwavelets is achieved using Mahalanobis distance to continuously monitor the health condition and track the performance degradation. Finally, three experimental case studies are implemented to demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed method can quantitatively identify the fault signature of a slight rub on the inner race of a locomotive bearing, effectively detect and locate the potential failure from a complicated epicyclic gear train and successfully reveal the fault development and performance degradation of a test bearing in the lifetime.

Optimization of Second Fault Detection Thresholds to Maximize Mission POS

NASA Technical Reports Server (NTRS)

Anzalone, Evan

2018-01-01

In order to support manned spaceflight safety requirements, the Space Launch System (SLS) has defined program-level requirements for key systems to ensure successful operation under single fault conditions. To accommodate this with regards to Navigation, the SLS utilizes an internally redundant Inertial Navigation System (INS) with built-in capability to detect, isolate, and recover from first failure conditions and still maintain adherence to performance requirements. The unit utilizes multiple hardware- and software-level techniques to enable detection, isolation, and recovery from these events in terms of its built-in Fault Detection, Isolation, and Recovery (FDIR) algorithms. Successful operation is defined in terms of sufficient navigation accuracy at insertion while operating under worst case single sensor outages (gyroscope and accelerometer faults at launch). In addition to first fault detection and recovery, the SLS program has also levied requirements relating to the capability of the INS to detect a second fault, tracking any unacceptable uncertainty in knowledge of the vehicle's state. This detection functionality is required in order to feed abort analysis and ensure crew safety. Increases in navigation state error and sensor faults can drive the vehicle outside of its operational as-designed environments and outside of its performance envelope causing loss of mission, or worse, loss of crew. The criteria for operation under second faults allows for a larger set of achievable missions in terms of potential fault conditions, due to the INS operating at the edge of its capability. As this performance is defined and controlled at the vehicle level, it allows for the use of system level margins to increase probability of mission success on the operational edges of the design space. Due to the implications of the vehicle response to abort conditions (such as a potentially failed INS), it is important to consider a wide range of failure scenarios in terms of both magnitude and time. As such, the Navigation team is taking advantage of the INS's capability to schedule and change fault detection thresholds in flight. These values are optimized along a nominal trajectory in order to maximize probability of mission success, and reducing the probability of false positives (defined as when the INS would report a second fault condition resulting in loss of mission, but the vehicle would still meet insertion requirements within system-level margins). This paper will describe an optimization approach using Genetic Algorithms to tune the threshold parameters to maximize vehicle resilience to second fault events as a function of potential fault magnitude and time of fault over an ascent mission profile. The analysis approach, and performance assessment of the results will be presented to demonstrate the applicability of this process to second fault detection to maximize mission probability of success.

An online outlier identification and removal scheme for improving fault detection performance.

PubMed

Ferdowsi, Hasan; Jagannathan, Sarangapani; Zawodniok, Maciej

2014-05-01

Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions.

Generalized analytic solutions and response characteristics of magnetotelluric fields on anisotropic infinite faults

NASA Astrophysics Data System (ADS)

Bing, Xue; Yicai, Ji

2018-06-01

In order to understand directly and analyze accurately the detected magnetotelluric (MT) data on anisotropic infinite faults, two-dimensional partial differential equations of MT fields are used to establish a model of anisotropic infinite faults using the Fourier transform method. A multi-fault model is developed to expand the one-fault model. The transverse electric mode and transverse magnetic mode analytic solutions are derived using two-infinite-fault models. The infinite integral terms of the quasi-analytic solutions are discussed. The dual-fault model is computed using the finite element method to verify the correctness of the solutions. The MT responses of isotropic and anisotropic media are calculated to analyze the response functions by different anisotropic conductivity structures. The thickness and conductivity of the media, influencing MT responses, are discussed. The analytic principles are also given. The analysis results are significant to how MT responses are perceived and to the data interpretation of the complex anisotropic infinite faults.

A Doppler Transient Model Based on the Laplace Wavelet and Spectrum Correlation Assessment for Locomotive Bearing Fault Diagnosis

PubMed Central

Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W.

2013-01-01

The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully. PMID:24253191

A Doppler transient model based on the laplace wavelet and spectrum correlation assessment for locomotive bearing fault diagnosis.

PubMed

Shen, Changqing; Liu, Fang; Wang, Dong; Zhang, Ao; Kong, Fanrang; Tse, Peter W

2013-11-18

The condition of locomotive bearings, which are essential components in trains, is crucial to train safety. The Doppler effect significantly distorts acoustic signals during high movement speeds, substantially increasing the difficulty of monitoring locomotive bearings online. In this study, a new Doppler transient model based on the acoustic theory and the Laplace wavelet is presented for the identification of fault-related impact intervals embedded in acoustic signals. An envelope spectrum correlation assessment is conducted between the transient model and the real fault signal in the frequency domain to optimize the model parameters. The proposed method can identify the parameters used for simulated transients (periods in simulated transients) from acoustic signals. Thus, localized bearing faults can be detected successfully based on identified parameters, particularly period intervals. The performance of the proposed method is tested on a simulated signal suffering from the Doppler effect. Besides, the proposed method is used to analyze real acoustic signals of locomotive bearings with inner race and outer race faults, respectively. The results confirm that the periods between the transients, which represent locomotive bearing fault characteristics, can be detected successfully.

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter.

PubMed

Yang, Chun; Mohammadi, Arash; Chen, Qing-Wei

2016-11-02

Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system's error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts.

Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter

PubMed Central

Yang, Chun; Mohammadi, Arash; Chen, Qing-Wei

2016-01-01

Motivated by the key importance of multi-sensor information fusion algorithms in the state-of-the-art integrated navigation systems due to recent advancements in sensor technologies, telecommunication, and navigation systems, the paper proposes an improved and innovative fault-tolerant fusion framework. An integrated navigation system is considered consisting of four sensory sub-systems, i.e., Strap-down Inertial Navigation System (SINS), Global Navigation System (GPS), the Bei-Dou2 (BD2) and Celestial Navigation System (CNS) navigation sensors. In such multi-sensor applications, on the one hand, the design of an efficient fusion methodology is extremely constrained specially when no information regarding the system’s error characteristics is available. On the other hand, the development of an accurate fault detection and integrity monitoring solution is both challenging and critical. The paper addresses the sensitivity issues of conventional fault detection solutions and the unavailability of a precisely known system model by jointly designing fault detection and information fusion algorithms. In particular, by using ideas from Interacting Multiple Model (IMM) filters, the uncertainty of the system will be adjusted adaptively by model probabilities and using the proposed fuzzy-based fusion framework. The paper also addresses the problem of using corrupted measurements for fault detection purposes by designing a two state propagator chi-square test jointly with the fusion algorithm. Two IMM predictors, running in parallel, are used and alternatively reactivated based on the received information form the fusion filter to increase the reliability and accuracy of the proposed detection solution. With the combination of the IMM and the proposed fusion method, we increase the failure sensitivity of the detection system and, thereby, significantly increase the overall reliability and accuracy of the integrated navigation system. Simulation results indicate that the proposed fault tolerant fusion framework provides superior performance over its traditional counterparts. PMID:27827832

Spatiotemporal complexity of 2-D rupture nucleation process observed by direct monitoring during large-scale biaxial rock friction experiments

NASA Astrophysics Data System (ADS)

Fukuyama, Eiichi; Tsuchida, Kotoyo; Kawakata, Hironori; Yamashita, Futoshi; Mizoguchi, Kazuo; Xu, Shiqing

2018-05-01

We were able to successfully capture rupture nucleation processes on a 2-D fault surface during large-scale biaxial friction experiments using metagabbro rock specimens. Several rupture nucleation patterns have been detected by a strain gauge array embedded inside the rock specimens as well as by that installed along the edge walls of the fault. In most cases, the unstable rupture started just after the rupture front touched both ends of the rock specimen (i.e., when rupture front extended to the entire width of the fault). In some cases, rupture initiated at multiple locations and the rupture fronts coalesced to generate unstable ruptures, which could only be detected from the observation inside the rock specimen. Therefore, we need to carefully examine the 2-D nucleation process of the rupture especially when analyzing the data measured only outside the rock specimen. At least the measurements should be done at both sides of the fault to identify the asymmetric rupture propagation on the fault surface, although this is not perfect yet. In the present experiment, we observed three typical types of the 2-D rupture propagation patterns, two of which were initiated at a single location either close to the fault edge or inside the fault. This initiation could be accelerated by the free surface effect at the fault edge. The third one was initiated at multiple locations and had a rupture coalescence at the middle of the fault. These geometrically complicated rupture initiation patterns are important for understanding the earthquake nucleation process in nature.

A Model-Based Probabilistic Inversion Framework for Wire Fault Detection Using TDR

NASA Technical Reports Server (NTRS)

Schuet, Stefan R.; Timucin, Dogan A.; Wheeler, Kevin R.

2010-01-01

Time-domain reflectometry (TDR) is one of the standard methods for diagnosing faults in electrical wiring and interconnect systems, with a long-standing history focused mainly on hardware development of both high-fidelity systems for laboratory use and portable hand-held devices for field deployment. While these devices can easily assess distance to hard faults such as sustained opens or shorts, their ability to assess subtle but important degradation such as chafing remains an open question. This paper presents a unified framework for TDR-based chafing fault detection in lossy coaxial cables by combining an S-parameter based forward modeling approach with a probabilistic (Bayesian) inference algorithm. Results are presented for the estimation of nominal and faulty cable parameters from laboratory data.

Automatic detection of electric power troubles (AI application)

NASA Technical Reports Server (NTRS)

Wang, Caroline; Zeanah, Hugh; Anderson, Audie; Patrick, Clint

1987-01-01

The design goals for the Automatic Detection of Electric Power Troubles (ADEPT) were to enhance Fault Diagnosis Techniques in a very efficient way. ADEPT system was designed in two modes of operation: (1) Real time fault isolation, and (2) a local simulator which simulates the models theoretically.

Health Monitoring of a Satellite System

NASA Technical Reports Server (NTRS)

Chen, Robert H.; Ng, Hok K.; Speyer, Jason L.; Guntur, Lokeshkumar S.; Carpenter, Russell

2004-01-01

A health monitoring system based on analytical redundancy is developed for satellites on elliptical orbits. First, the dynamics of the satellite including orbital mechanics and attitude dynamics is modelled as a periodic system. Then, periodic fault detection filters are designed to detect and identify the satellite's actuator and sensor faults. In addition, parity equations are constructed using the algebraic redundant relationship among the actuators and sensors. Furthermore, a residual processor is designed to generate the probability of each of the actuator and sensor faults by using a sequential probability test. Finally, the health monitoring system, consisting of periodic fault detection lters, parity equations and residual processor, is evaluated in the simulation in the presence of disturbances and uncertainty.

Fault detection for hydraulic pump based on chaotic parallel RBF network

NASA Astrophysics Data System (ADS)

Lu, Chen; Ma, Ning; Wang, Zhipeng

2011-12-01

In this article, a parallel radial basis function network in conjunction with chaos theory (CPRBF network) is presented, and applied to practical fault detection for hydraulic pump, which is a critical component in aircraft. The CPRBF network consists of a number of radial basis function (RBF) subnets connected in parallel. The number of input nodes for each RBF subnet is determined by different embedding dimension based on chaotic phase-space reconstruction. The output of CPRBF is a weighted sum of all RBF subnets. It was first trained using the dataset from normal state without fault, and then a residual error generator was designed to detect failures based on the trained CPRBF network. Then, failure detection can be achieved by the analysis of the residual error. Finally, two case studies are introduced to compare the proposed CPRBF network with traditional RBF networks, in terms of prediction and detection accuracy.

Fault Detection and Severity Analysis of Servo Valves Using Recurrence Quantification Analysis

DTIC Science & Technology

2014-10-02

Fault Detection and Severity Analysis of Servo Valves Using Recurrence Quantification Analysis M. Samadani1, C. A. Kitio Kwuimy2, and C. Nataraj3...diagnostics of nonlinear systems. A detailed nonlinear math- ematical model of a servo electro-hydraulic system has been used to demonstrate the procedure...Two faults have been considered associated with the servo valve including the in- creased friction between spool and sleeve and the degradation of the

NHPP-Based Software Reliability Models Using Equilibrium Distribution

NASA Astrophysics Data System (ADS)

Xiao, Xiao; Okamura, Hiroyuki; Dohi, Tadashi

Non-homogeneous Poisson processes (NHPPs) have gained much popularity in actual software testing phases to estimate the software reliability, the number of remaining faults in software and the software release timing. In this paper, we propose a new modeling approach for the NHPP-based software reliability models (SRMs) to describe the stochastic behavior of software fault-detection processes. The fundamental idea is to apply the equilibrium distribution to the fault-detection time distribution in NHPP-based modeling. We also develop efficient parameter estimation procedures for the proposed NHPP-based SRMs. Through numerical experiments, it can be concluded that the proposed NHPP-based SRMs outperform the existing ones in many data sets from the perspective of goodness-of-fit and prediction performance.

Neural networks and fault probability evaluation for diagnosis issues.

PubMed

Kourd, Yahia; Lefebvre, Dimitri; Guersi, Noureddine

2014-01-01

This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also includes a comparison between the contributions of these tools and their limitations, particularly through the establishment of quantitative indicators to assess their performance. According to the computation of a confidence factor, the proposed method is suitable to evaluate the reliability of the FDI decision. The approach is applied to detect and isolate 19 fault candidates in the DAMADICS benchmark. The results obtained with the proposed scheme are compared with the results obtained according to a usual thresholding method.

Sensor fault detection and isolation system for a condensation process.

PubMed

Castro, M A López; Escobar, R F; Torres, L; Aguilar, J F Gómez; Hernández, J A; Olivares-Peregrino, V H

2016-11-01

This article presents the design of a sensor Fault Detection and Isolation (FDI) system for a condensation process based on a nonlinear model. The condenser is modeled by dynamic and thermodynamic equations. For this work, the dynamic equations are described by three pairs of differential equations which represent the energy balance between the fluids. The thermodynamic equations consist in algebraic heat transfer equations and empirical equations, that allow for the estimation of heat transfer coefficients. The FDI system consists of a bank of two nonlinear high-gain observers, in order to detect, estimate and to isolate the fault in any of both outlet temperature sensors. The main contributions of this work were the experimental validation of the condenser nonlinear model and the FDI system. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine

PubMed Central

Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

2016-01-01

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox. PMID:26848665

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine.

PubMed

Zhong, Jian-Hua; Wong, Pak Kin; Yang, Zhi-Xin

2016-02-02

This study combines signal de-noising, feature extraction, two pairwise-coupled relevance vector machines (PCRVMs) and particle swarm optimization (PSO) for parameter optimization to form an intelligent diagnostic framework for gearbox fault detection. Firstly, the noises of sensor signals are de-noised by using the wavelet threshold method to lower the noise level. Then, the Hilbert-Huang transform (HHT) and energy pattern calculation are applied to extract the fault features from de-noised signals. After that, an eleven-dimension vector, which consists of the energies of nine intrinsic mode functions (IMFs), maximum value of HHT marginal spectrum and its corresponding frequency component, is obtained to represent the features of each gearbox fault. The two PCRVMs serve as two different fault detection committee members, and they are trained by using vibration and sound signals, respectively. The individual diagnostic result from each committee member is then combined by applying a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable faults as compared to individual classifiers acting alone. The effectiveness of the proposed framework is experimentally verified by using test cases. The experimental results show the proposed framework is superior to existing single classifiers in terms of diagnostic accuracies for both single- and simultaneous-faults in the gearbox.

Unsupervised Pattern Classifier for Abnormality-Scaling of Vibration Features for Helicopter Gearbox Fault Diagnosis

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

1996-01-01

A new unsupervised pattern classifier is introduced for on-line detection of abnormality in features of vibration that are used for fault diagnosis of helicopter gearboxes. This classifier compares vibration features with their respective normal values and assigns them a value in (0, 1) to reflect their degree of abnormality. Therefore, the salient feature of this classifier is that it does not require feature values associated with faulty cases to identify abnormality. In order to cope with noise and changes in the operating conditions, an adaptation algorithm is incorporated that continually updates the normal values of the features. The proposed classifier is tested using experimental vibration features obtained from an OH-58A main rotor gearbox. The overall performance of this classifier is then evaluated by integrating the abnormality-scaled features for detection of faults. The fault detection results indicate that the performance of this classifier is comparable to the leading unsupervised neural networks: Kohonen's Feature Mapping and Adaptive Resonance Theory (AR72). This is significant considering that the independence of this classifier from fault-related features makes it uniquely suited to abnormality-scaling of vibration features for fault diagnosis.

A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites

NASA Astrophysics Data System (ADS)

Le, Huy Xuan; Matunaga, Saburo

2014-12-01

This paper presents an adaptive unscented Kalman filter (AUKF) to recover the satellite attitude in a fault detection and diagnosis (FDD) subsystem of microsatellites. The FDD subsystem includes a filter and an estimator with residual generators, hypothesis tests for fault detections and a reference logic table for fault isolations and fault recovery. The recovery process is based on the monitoring of mean and variance values of each attitude sensor behaviors from residual vectors. In the case of normal work, the residual vectors should be in the form of Gaussian white noise with zero mean and fixed variance. When the hypothesis tests for the residual vectors detect something unusual by comparing the mean and variance values with dynamic thresholds, the AUKF with real-time updated measurement noise covariance matrix will be used to recover the sensor faults. The scheme developed in this paper resolves the problem of the heavy and complex calculations during residual generations and therefore the delay in the isolation process is reduced. The numerical simulations for TSUBAME, a demonstration microsatellite of Tokyo Institute of Technology, are conducted and analyzed to demonstrate the working of the AUKF and FDD subsystem.

Fault-tolerant locomotion of the hexapod robot.

PubMed

Yang, J M; Kim, J H

1998-01-01

In this paper, we propose a scheme for fault detection and tolerance of the hexapod robot locomotion on even terrain. The fault stability margin is defined to represent potential stability which a gait can have in case a sudden fault event occurs to one leg. Based on this, the fault-tolerant quadruped periodic gaits of the hexapod walking over perfectly even terrain are derived. It is demonstrated that the derived quadruped gait is the optimal one the hexapod can have maintaining fault stability margin nonnegative and a geometric condition should be satisfied for the optimal locomotion. By this scheme, when one leg is in failure, the hexapod robot has the modified tripod gait to continue the optimal locomotion.

Selection of test paths for solder joint intermittent connection faults under DC stimulus

NASA Astrophysics Data System (ADS)

Huakang, Li; Kehong, Lv; Jing, Qiu; Guanjun, Liu; Bailiang, Chen

2018-06-01

The test path of solder joint intermittent connection faults under direct-current stimulus is examined in this paper. According to the physical structure of the circuit, a network model is established first. A network node is utilised to represent the test node. The path edge refers to the number of intermittent connection faults in the path. Then, the selection criteria of the test path based on the node degree index are proposed and the solder joint intermittent connection faults are covered using fewer test paths. Finally, three circuits are selected to verify the method. To test if the intermittent fault is covered by the test paths, the intermittent fault is simulated by a switch. The results show that the proposed method can detect the solder joint intermittent connection fault using fewer test paths. Additionally, the number of detection steps is greatly reduced without compromising fault coverage.

Experimental Fault Diagnosis in Systems Containing Finite Elements of Plate of Kirchoff by Using State Observers Methodology

NASA Astrophysics Data System (ADS)

Alegre, D. M.; Koroishi, E. H.; Melo, G. P.

2015-07-01

This paper presents a methodology for detection and localization of faults by using state observers. State Observers can rebuild the states not measured or values from points of difficult access in the system. So faults can be detected in these points without the knowledge of its measures, and can be track by the reconstructions of their states. In this paper this methodology will be applied in a system which represents a simplified model of a vehicle. In this model the chassis of the car was represented by a flat plate, which was divided in finite elements of plate (plate of Kirchoff), in addition, was considered the car suspension (springs and dampers). A test rig was built and the developed methodology was used to detect and locate faults on this system. In analyses done, the idea is to use a system with a specific fault, and then use the state observers to locate it, checking on a quantitative variation of the parameter of the system which caused this crash. For the computational simulations the software MATLAB was used.

Integrating physically based simulators with Event Detection Systems: Multi-site detection approach.

PubMed

Housh, Mashor; Ohar, Ziv

2017-03-01

The Fault Detection (FD) Problem in control theory concerns of monitoring a system to identify when a fault has occurred. Two approaches can be distinguished for the FD: Signal processing based FD and Model-based FD. The former concerns of developing algorithms to directly infer faults from sensors' readings, while the latter uses a simulation model of the real-system to analyze the discrepancy between sensors' readings and expected values from the simulation model. Most contamination Event Detection Systems (EDSs) for water distribution systems have followed the signal processing based FD, which relies on analyzing the signals from monitoring stations independently of each other, rather than evaluating all stations simultaneously within an integrated network. In this study, we show that a model-based EDS which utilizes a physically based water quality and hydraulics simulation models, can outperform the signal processing based EDS. We also show that the model-based EDS can facilitate the development of a Multi-Site EDS (MSEDS), which analyzes the data from all the monitoring stations simultaneously within an integrated network. The advantage of the joint analysis in the MSEDS is expressed by increased detection accuracy (higher true positive alarms and fewer false alarms) and shorter detection time. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

PubMed

Zhou, Shenghan; Qian, Silin; Chang, Wenbing; Xiao, Yiyong; Cheng, Yang

2018-06-14

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Improving the performance of univariate control charts for abnormal detection and classification

NASA Astrophysics Data System (ADS)

Yiakopoulos, Christos; Koutsoudaki, Maria; Gryllias, Konstantinos; Antoniadis, Ioannis

2017-03-01

Bearing failures in rotating machinery can cause machine breakdown and economical loss, if no effective actions are taken on time. Therefore, it is of prime importance to detect accurately the presence of faults, especially at their early stage, to prevent sequent damage and reduce costly downtime. The machinery fault diagnosis follows a roadmap of data acquisition, feature extraction and diagnostic decision making, in which mechanical vibration fault feature extraction is the foundation and the key to obtain an accurate diagnostic result. A challenge in this area is the selection of the most sensitive features for various types of fault, especially when the characteristics of failures are difficult to be extracted. Thus, a plethora of complex data-driven fault diagnosis methods are fed by prominent features, which are extracted and reduced through traditional or modern algorithms. Since most of the available datasets are captured during normal operating conditions, the last decade a number of novelty detection methods, able to work when only normal data are available, have been developed. In this study, a hybrid method combining univariate control charts and a feature extraction scheme is introduced focusing towards an abnormal change detection and classification, under the assumption that measurements under normal operating conditions of the machinery are available. The feature extraction method integrates the morphological operators and the Morlet wavelets. The effectiveness of the proposed methodology is validated on two different experimental cases with bearing faults, demonstrating that the proposed approach can improve the fault detection and classification performance of conventional control charts.

Orion GN&C Fault Management System Verification: Scope And Methodology

NASA Technical Reports Server (NTRS)

Brown, Denise; Weiler, David; Flanary, Ronald

2016-01-01

In order to ensure long-term ability to meet mission goals and to provide for the safety of the public, ground personnel, and any crew members, nearly all spacecraft include a fault management (FM) system. For a manned vehicle such as Orion, the safety of the crew is of paramount importance. The goal of the Orion Guidance, Navigation and Control (GN&C) fault management system is to detect, isolate, and respond to faults before they can result in harm to the human crew or loss of the spacecraft. Verification of fault management/fault protection capability is challenging due to the large number of possible faults in a complex spacecraft, the inherent unpredictability of faults, the complexity of interactions among the various spacecraft components, and the inability to easily quantify human reactions to failure scenarios. The Orion GN&C Fault Detection, Isolation, and Recovery (FDIR) team has developed a methodology for bounding the scope of FM system verification while ensuring sufficient coverage of the failure space and providing high confidence that the fault management system meets all safety requirements. The methodology utilizes a swarm search algorithm to identify failure cases that can result in catastrophic loss of the crew or the vehicle and rare event sequential Monte Carlo to verify safety and FDIR performance requirements.

Modelling earthquake ruptures with dynamic off-fault damage

NASA Astrophysics Data System (ADS)

Okubo, Kurama; Bhat, Harsha S.; Klinger, Yann; Rougier, Esteban

2017-04-01

Earthquake rupture modelling has been developed for producing scenario earthquakes. This includes understanding the source mechanisms and estimating far-field ground motion with given a priori constraints like fault geometry, constitutive law of the medium and friction law operating on the fault. It is necessary to consider all of the above complexities of a fault systems to conduct realistic earthquake rupture modelling. In addition to the complexity of the fault geometry in nature, coseismic off-fault damage, which is observed by a variety of geological and seismological methods, plays a considerable role on the resultant ground motion and its spectrum compared to a model with simple planer fault surrounded by purely elastic media. Ideally all of these complexities should be considered in earthquake modelling. State of the art techniques developed so far, however, cannot treat all of them simultaneously due to a variety of computational restrictions. Therefore, we adopt the combined finite-discrete element method (FDEM), which can effectively deal with pre-existing complex fault geometry such as fault branches and kinks and can describe coseismic off-fault damage generated during the dynamic rupture. The advantage of FDEM is that it can handle a wide range of length scales, from metric to kilometric scale, corresponding to the off-fault damage and complex fault geometry respectively. We used the FDEM-based software tool called HOSSedu (Hybrid Optimization Software Suite - Educational Version) for the earthquake rupture modelling, which was developed by Los Alamos National Laboratory. We firstly conducted the cross-validation of this new methodology against other conventional numerical schemes such as the finite difference method (FDM), the spectral element method (SEM) and the boundary integral equation method (BIEM), to evaluate the accuracy with various element sizes and artificial viscous damping values. We demonstrate the capability of the FDEM tool for modelling earthquake ruptures. We then modelled earthquake ruptures allowing for coseismic off-fault damage with appropriate fracture nucleation and growth criteria. We studied the effect of different conditions such as rupture speed (sub-Rayleigh or supershear), the orientation of the initial maximum principal stress with respect to the fault and the magnitude of the initial stress (to mimic depth). The comparison between the sub-Rayleigh and supershear case shows that the coseismic off-fault damage is enhanced in the supershear case when compared with the sub-Rayleigh case. The orientation of the maximum principal stress also has significant difference such that the dynamic off-fault cracking is more likely to occur on the extensional side of the fault for high principal stress orientation. It is found that the coseismic off-fault damage reduces the rupture speed due to the dissipation of the energy by dynamic off-fault cracking generated in the vicinity of the rupture front. In terms of the ground motion amplitude spectra it is shown that the high-frequency radiation is enhanced by the coseismic off-fault damage though it is quickly attenuated. This is caused by the intricate superposition of the radiation generated by the off-fault damage and the perturbation of the rupture speed on the main fault.

Fault detection and diagnosis using neural network approaches

NASA Technical Reports Server (NTRS)

Kramer, Mark A.

1992-01-01

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Model-Based Fault Diagnosis: Performing Root Cause and Impact Analyses in Real Time

NASA Technical Reports Server (NTRS)

Figueroa, Jorge F.; Walker, Mark G.; Kapadia, Ravi; Morris, Jonathan

2012-01-01

Generic, object-oriented fault models, built according to causal-directed graph theory, have been integrated into an overall software architecture dedicated to monitoring and predicting the health of mission- critical systems. Processing over the generic fault models is triggered by event detection logic that is defined according to the specific functional requirements of the system and its components. Once triggered, the fault models provide an automated way for performing both upstream root cause analysis (RCA), and for predicting downstream effects or impact analysis. The methodology has been applied to integrated system health management (ISHM) implementations at NASA SSC's Rocket Engine Test Stands (RETS).

GPS and InSAR Monitoring of the Mogul Swarm: Evidence for Mainly Aseismic Fault Creep, with Implications for Seismic Hazard

NASA Astrophysics Data System (ADS)

Blewitt, G.; Bell, J.; Hammond, W. C.; Kreemer, C.; Plag, H.; Depolo, C.

2008-12-01

The relative fraction of aseismic slip that occurs in the seismogenic zone has implications for earthquake hazard, because aseismic creep tends to release stresses that have accumulated by relative plate motion. The phenomenon of aseismic fault creep is well documented in segments of the San Andreas Fault, in Japan, and more recently, in the Cascadia subduction zone where creep occurs episodically in "slow earthquakes", as detected by continuous GPS (CGPS). Another mechanism for fault creep is afterslip following large or great earthquakes, which can rival the magnitude of the displacement associated with the main shock, as was the case for the 2005 Mw 8.7 Nias Earthquake, again detected by CGPS. Here we report on the CGPS detection of aseismic fault creep that significantly exceeds the co-seismic displacement of the moderate Mw 5.0 Mogul earthquake of 26 April 2008. This was the largest event of the Mogul-Somersett earthquake swarm that lasted from approximately March-July, 2008, a few km west of Reno, Nevada, USA. We installed the GPS network in March 2008, in rapid response to the onset of the swarm. The network has an inter-station spacing of ~2 km in the near field. The GPS data indicate that aseismic afterslip occurred for several weeks after the main event, with a decaying signature. Two stations apparently straddled the previously unrecognized NNW-SSE striking fault, and detected a total displacement of ~40 mm toward each other, yet only ~15 mm occurred on 26 April. As such, the Mw 5.0 event and the subsequent afterslip is perhaps the smallest event to have ever been observed directly by several GPS stations. Our modeling of the event is also constrained by InSAR, which helps constrain spatial details of the slip distribution. Results to date already indicate that the GPS and InSAR constraints appear to be compatible. The post-seismic surface displacement field has the same general pattern of the co-seismic displacement field, consistent with models of shallow slip (a few km) on a NNW-trending right-lateral strike-slip fault. Despite an extensive search on the ground, no surface rupture was found in the area of this modeled fault. Two potentially important questions arise from these observations: (1) Is mainly aseismic slip the rule or the exception for all moderate magnitude earthquakes, or is this only typically associated with shallow earthquake swarms like the 2008 Mogul-Somersett sequence? (2) What is the implication of the answer to the first question for monitoring earthquakes using InSAR, which is limited by the time interval between repeat passes? One thing that is clear, is that rapid-response GPS will be required to address these questions, which are important for the assessment of seismic hazards.

Time-frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis

NASA Astrophysics Data System (ADS)

Wang, Lei; Liu, Zhiwen; Miao, Qiang; Zhang, Xin

2018-03-01

A time-frequency analysis method based on ensemble local mean decomposition (ELMD) and fast kurtogram (FK) is proposed for rotating machinery fault diagnosis. Local mean decomposition (LMD), as an adaptive non-stationary and nonlinear signal processing method, provides the capability to decompose multicomponent modulation signal into a series of demodulated mono-components. However, the occurring mode mixing is a serious drawback. To alleviate this, ELMD based on noise-assisted method was developed. Still, the existing environmental noise in the raw signal remains in corresponding PF with the component of interest. FK has good performance in impulse detection while strong environmental noise exists. But it is susceptible to non-Gaussian noise. The proposed method combines the merits of ELMD and FK to detect the fault for rotating machinery. Primarily, by applying ELMD the raw signal is decomposed into a set of product functions (PFs). Then, the PF which mostly characterizes fault information is selected according to kurtosis index. Finally, the selected PF signal is further filtered by an optimal band-pass filter based on FK to extract impulse signal. Fault identification can be deduced by the appearance of fault characteristic frequencies in the squared envelope spectrum of the filtered signal. The advantages of ELMD over LMD and EEMD are illustrated in the simulation analyses. Furthermore, the efficiency of the proposed method in fault diagnosis for rotating machinery is demonstrated on gearbox case and rolling bearing case analyses.

Verification of Small Hole Theory for Application to Wire Chaffing Resulting in Shield Faults

NASA Technical Reports Server (NTRS)

Schuet, Stefan R.; Timucin, Dogan A.; Wheeler, Kevin R.

2011-01-01

Our work is focused upon developing methods for wire chafe fault detection through the use of reflectometry to assess shield integrity. When shielded electrical aircraft wiring first begins to chafe typically the resulting evidence is small hole(s) in the shielding. We are focused upon developing algorithms and the signal processing necessary to first detect these small holes prior to incurring damage to the inner conductors. Our approach has been to develop a first principles physics model combined with probabilistic inference, and to verify this model with laboratory experiments as well as through simulation. Previously we have presented the electromagnetic small-hole theory and how it might be applied to coaxial cable. In this presentation, we present our efforts to verify this theoretical approach with high-fidelity electromagnetic simulations (COMSOL). Laboratory observations are used to parameterize the computationally efficient theoretical model with probabilistic inference resulting in quantification of hole size and location. Our efforts in characterizing faults in coaxial cable are subsequently leading to fault detection in shielded twisted pair as well as analysis of intermittent faulty connectors using similar techniques.

Distributed Fault-Tolerant Control of Networked Uncertain Euler-Lagrange Systems Under Actuator Faults.

PubMed

Chen, Gang; Song, Yongduan; Lewis, Frank L

2016-05-03

This paper investigates the distributed fault-tolerant control problem of networked Euler-Lagrange systems with actuator and communication link faults. An adaptive fault-tolerant cooperative control scheme is proposed to achieve the coordinated tracking control of networked uncertain Lagrange systems on a general directed communication topology, which contains a spanning tree with the root node being the active target system. The proposed algorithm is capable of compensating for the actuator bias fault, the partial loss of effectiveness actuation fault, the communication link fault, the model uncertainty, and the external disturbance simultaneously. The control scheme does not use any fault detection and isolation mechanism to detect, separate, and identify the actuator faults online, which largely reduces the online computation and expedites the responsiveness of the controller. To validate the effectiveness of the proposed method, a test-bed of multiple robot-arm cooperative control system is developed for real-time verification. Experiments on the networked robot-arms are conduced and the results confirm the benefits and the effectiveness of the proposed distributed fault-tolerant control algorithms.

Haul truck tire dynamics due to tire condition

NASA Astrophysics Data System (ADS)

Vaghar Anzabi, R.; Nobes, D. S.; Lipsett, M. G.

2012-05-01

Pneumatic tires are costly components on large off-road haul trucks used in surface mining operations. Tires are prone to damage during operation, and these events can lead to injuries to personnel, loss of equipment, and reduced productivity. Damage rates have significant variability, due to operating conditions and a range of tire fault modes. Currently, monitoring of tire condition is done by physical inspection; and the mean time between inspections is often longer than the mean time between incipient failure and functional failure of the tire. Options for new condition monitoring methods include off-board thermal imaging and camera-based optical methods for detecting abnormal deformation and surface features, as well as on-board sensors to detect tire faults during vehicle operation. Physics-based modeling of tire dynamics can provide a good understanding of the tire behavior, and give insight into observability requirements for improved monitoring systems. This paper describes a model to simulate the dynamics of haul truck tires when a fault is present to determine the effects of physical parameter changes that relate to faults. To simulate the dynamics, a lumped mass 'quarter-vehicle' model has been used to determine the response of the system to a road profile when a failure changes the original properties of the tire. The result is a model of tire vertical displacement that can be used to detect a fault, which will be tested under field conditions in time-varying conditions.

Site-to-Source Finite Fault Distance Probability Distribution in Probabilistic Seismic Hazard and the Relationship Between Minimum Distances

NASA Astrophysics Data System (ADS)

Ortega, R.; Gutierrez, E.; Carciumaru, D. D.; Huesca-Perez, E.

2017-12-01

We present a method to compute the conditional and no-conditional probability density function (PDF) of the finite fault distance distribution (FFDD). Two cases are described: lines and areas. The case of lines has a simple analytical solution while, in the case of areas, the geometrical probability of a fault based on the strike, dip, and fault segment vertices is obtained using the projection of spheres in a piecewise rectangular surface. The cumulative distribution is computed by measuring the projection of a sphere of radius r in an effective area using an algorithm that estimates the area of a circle within a rectangle. In addition, we introduce the finite fault distance metrics. This distance is the distance where the maximum stress release occurs within the fault plane and generates a peak ground motion. Later, we can apply the appropriate ground motion prediction equations (GMPE) for PSHA. The conditional probability of distance given magnitude is also presented using different scaling laws. A simple model of constant distribution of the centroid at the geometrical mean is discussed, in this model hazard is reduced at the edges because the effective size is reduced. Nowadays there is a trend of using extended source distances in PSHA, however it is not possible to separate the fault geometry from the GMPE. With this new approach, it is possible to add fault rupture models separating geometrical and propagation effects.

On-board fault management for autonomous spacecraft

NASA Technical Reports Server (NTRS)

Fesq, Lorraine M.; Stephan, Amy; Doyle, Susan C.; Martin, Eric; Sellers, Suzanne

1991-01-01

The dynamic nature of the Cargo Transfer Vehicle's (CTV) mission and the high level of autonomy required mandate a complete fault management system capable of operating under uncertain conditions. Such a fault management system must take into account the current mission phase and the environment (including the target vehicle), as well as the CTV's state of health. This level of capability is beyond the scope of current on-board fault management systems. This presentation will discuss work in progress at TRW to apply artificial intelligence to the problem of on-board fault management. The goal of this work is to develop fault management systems. This presentation will discuss work in progress at TRW to apply artificial intelligence to the problem of on-board fault management. The goal of this work is to develop fault management systems that can meet the needs of spacecraft that have long-range autonomy requirements. We have implemented a model-based approach to fault detection and isolation that does not require explicit characterization of failures prior to launch. It is thus able to detect failures that were not considered in the failure and effects analysis. We have applied this technique to several different subsystems and tested our approach against both simulations and an electrical power system hardware testbed. We present findings from simulation and hardware tests which demonstrate the ability of our model-based system to detect and isolate failures, and describe our work in porting the Ada version of this system to a flight-qualified processor. We also discuss current research aimed at expanding our system to monitor the entire spacecraft.

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.

A new fault diagnosis algorithm for AUV cooperative localization system

NASA Astrophysics Data System (ADS)

Shi, Hongyang; Miao, Zhiyong; Zhang, Yi

2017-10-01

Multiple AUVs cooperative localization as a new kind of underwater positioning technology, not only can improve the positioning accuracy, but also has many advantages the single AUV does not have. It is necessary to detect and isolate the fault to increase the reliability and availability of the AUVs cooperative localization system. In this paper, the Extended Multiple Model Adaptive Cubature Kalmam Filter (EMMACKF) method is presented to detect the fault. The sensor failures are simulated based on the off-line experimental data. Experimental results have shown that the faulty apparatus can be diagnosed effectively using the proposed method. Compared with Multiple Model Adaptive Extended Kalman Filter and Multi-Model Adaptive Unscented Kalman Filter, both accuracy and timelines have been improved to some extent.

Reset Tree-Based Optical Fault Detection

PubMed Central

Lee, Dong-Geon; Choi, Dooho; Seo, Jungtaek; Kim, Howon

2013-01-01

In this paper, we present a new reset tree-based scheme to protect cryptographic hardware against optical fault injection attacks. As one of the most powerful invasive attacks on cryptographic hardware, optical fault attacks cause semiconductors to misbehave by injecting high-energy light into a decapped integrated circuit. The contaminated result from the affected chip is then used to reveal secret information, such as a key, from the cryptographic hardware. Since the advent of such attacks, various countermeasures have been proposed. Although most of these countermeasures are strong, there is still the possibility of attack. In this paper, we present a novel optical fault detection scheme that utilizes the buffers on a circuit's reset signal tree as a fault detection sensor. To evaluate our proposal, we model radiation-induced currents into circuit components and perform a SPICE simulation. The proposed scheme is expected to be used as a supplemental security tool. PMID:23698267

Fault detection and identification in missile system guidance and control: a filtering approach

NASA Astrophysics Data System (ADS)

Padgett, Mary Lou; Evers, Johnny; Karplus, Walter J.

1996-03-01

Real-world applications of computational intelligence can enhance the fault detection and identification capabilities of a missile guidance and control system. A simulation of a bank-to- turn missile demonstrates that actuator failure may cause the missile to roll and miss the target. Failure of one fin actuator can be detected using a filter and depicting the filter output as fuzzy numbers. The properties and limitations of artificial neural networks fed by these fuzzy numbers are explored. A suite of networks is constructed to (1) detect a fault and (2) determine which fin (if any) failed. Both the zero order moment term and the fin rate term show changes during actuator failure. Simulations address the following questions: (1) How bad does the actuator failure have to be for detection to occur, (2) How bad does the actuator failure have to be for fault detection and isolation to occur, (3) are both zero order moment and fine rate terms needed. A suite of target trajectories are simulated, and properties and limitations of the approach reported. In some cases, detection of the failed actuator occurs within 0.1 second, and isolation of the failure occurs 0.1 after that. Suggestions for further research are offered.

Graph-based real-time fault diagnostics

NASA Technical Reports Server (NTRS)

Padalkar, S.; Karsai, G.; Sztipanovits, J.

1988-01-01

A real-time fault detection and diagnosis capability is absolutely crucial in the design of large-scale space systems. Some of the existing AI-based fault diagnostic techniques like expert systems and qualitative modelling are frequently ill-suited for this purpose. Expert systems are often inadequately structured, difficult to validate and suffer from knowledge acquisition bottlenecks. Qualitative modelling techniques sometimes generate a large number of failure source alternatives, thus hampering speedy diagnosis. In this paper we present a graph-based technique which is well suited for real-time fault diagnosis, structured knowledge representation and acquisition and testing and validation. A Hierarchical Fault Model of the system to be diagnosed is developed. At each level of hierarchy, there exist fault propagation digraphs denoting causal relations between failure modes of subsystems. The edges of such a digraph are weighted with fault propagation time intervals. Efficient and restartable graph algorithms are used for on-line speedy identification of failure source components.

Precise tremor source locations and amplitude variations along the lower-crustal central San Andreas Fault

USGS Publications Warehouse

Shelly, David R.; Hardebeck, Jeanne L.

2010-01-01

We precisely locate 88 tremor families along the central San Andreas Fault using a 3D velocity model and numerous P and S wave arrival times estimated from seismogram stacks of up to 400 events per tremor family. Maximum tremor amplitudes vary along the fault by at least a factor of 7, with by far the strongest sources along a 25 km section of the fault southeast of Parkfield. We also identify many weaker tremor families, which have largely escaped prior detection. Together, these sources extend 150 km along the fault, beneath creeping, transitional, and locked sections of the upper crustal fault. Depths are mostly between 18 and 28 km, in the lower crust. Epicenters are concentrated within 3 km of the surface trace, implying a nearly vertical fault. A prominent gap in detectible activity is located directly beneath the region of maximum slip in the 2004 magnitude 6.0 Parkfield earthquake.

Focal mechanisms and inter-event times of low-frequency earthquakes reveal quasi-continuous deformation and triggered slow slip on the deep Alpine Fault

NASA Astrophysics Data System (ADS)

Baratin, Laura-May; Chamberlain, Calum J.; Townend, John; Savage, Martha K.

2018-02-01

Characterising the seismicity associated with slow deformation in the vicinity of the Alpine Fault may provide constraints on the stresses acting on a major transpressive margin prior to an anticipated great (≥M8) earthquake. Here, we use recently detected tremor and low-frequency earthquakes (LFEs) to examine how slow tectonic deformation is loading the Alpine Fault late in its typical ∼300-yr seismic cycle. We analyse a continuous seismic dataset recorded between 2009 and 2016 using a network of 10-13 short-period seismometers, the Southern Alps Microearthquake Borehole Array. Fourteen primary LFE templates are used in an iterative matched-filter and stacking routine, allowing the detection of similar signals corresponding to LFE families sharing common locations. This yields an 8-yr catalogue containing 10,000 LFEs that are combined for each of the 14 LFE families using phase-weighted stacking to produce signals with the highest possible signal-to-noise ratios. We show that LFEs occur almost continuously during the 8-yr study period and highlight two types of LFE distributions: (1) discrete behaviour with an inter-event time exceeding 2 min; (2) burst-like behaviour with an inter-event time below 2 min. We interpret the discrete events as small-scale frequent deformation on the deep extent of the Alpine Fault and LFE bursts (corresponding in most cases to known episodes of tremor or large regional earthquakes) as brief periods of increased slip activity indicative of slow slip. We compute improved non-linear earthquake locations using a 3-D velocity model. LFEs occur below the seismogenic zone at depths of 17-42 km, on or near the hypothesised deep extent of the Alpine Fault. The first estimates of LFE focal mechanisms associated with continental faulting, in conjunction with recurrence intervals, are consistent with quasi-continuous shear faulting on the deep extent of the Alpine Fault.

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

It's difficult to build precise mathematical models for complex engineering systems because of the complexity of the structure and dynamics characteristics. Intelligent fault diagnosis introduces artificial intelligence and works in a different way without building the analytical mathematical model of a diagnostic object, so it's a practical approach to solve diagnostic problems of complex systems. This paper presents an intelligent fault diagnosis method, an integrated fault-pattern classifier based on Hidden Markov Model (HMM). This classifier consists of dynamic time warping (DTW) algorithm, self-organizing feature mapping (SOFM) network and Hidden Markov Model. First, after dynamic observation vector in measuring space is processed by DTW, the error vector including the fault feature of being tested system is obtained. Then a SOFM network is used as a feature extractor and vector quantization processor. Finally, fault diagnosis is realized by fault patterns classifying with the Hidden Markov Model classifier. The importing of dynamic time warping solves the problem of feature extracting from dynamic process vectors of complex system such as aeroengine, and makes it come true to diagnose complex system by utilizing dynamic process information. Simulating experiments show that the diagnosis model is easy to extend, and the fault pattern classifier is efficient and is convenient to the detecting and diagnosing of new faults.

Three-dimensional characterization of microporosity and permeability in fault zones hosted in heterolithic succession

NASA Astrophysics Data System (ADS)

Riegel, H. B.; Zambrano, M.; Jablonska, D.; Emanuele, T.; Agosta, F.; Mattioni, L.; Rustichelli, A.

2017-12-01

The hydraulic properties of fault zones depend upon the individual contributions of the damage zone and the fault core. In the case of the damage zone, it is generally characterized by means of fracture analysis and modelling implementing multiple approaches, for instance the discrete fracture network model, the continuum model, and the channel network model. Conversely, the fault core is more difficult to characterize because it is normally composed of fine grain material generated by friction and wear. If the dimensions of the fault core allows it, the porosity and permeability are normally studied by means of laboratory analysis or in the other case by two dimensional microporosity analysis and in situ measurements of permeability (e.g. micro-permeameter). In this study, a combined approach consisting of fracture modeling, three-dimensional microporosity analysis, and computational fluid dynamics was applied to characterize the hydraulic properties of fault zones. The studied fault zones crosscut a well-cemented heterolithic succession (sandstone and mudstones) and may vary in terms of fault core thickness and composition, fracture properties, kinematics (normal or strike-slip), and displacement. These characteristics produce various splay and fault core behavior. The alternation of sandstone and mudstone layers is responsible for the concurrent occurrence of brittle (fractures) and ductile (clay smearing) deformation. When these alternating layers are faulted, they produce corresponding fault cores which act as conduits or barriers for fluid migration. When analyzing damage zones, accurate field and data acquisition and stochastic modeling was used to determine the hydraulic properties of the rock volume, in relation to the surrounding, undamaged host rock. In the fault cores, the three-dimensional pore network quantitative analysis based on X-ray microtomography images includes porosity, pore connectivity, and specific surface area. In addition, images were used to perform computational fluid simulation (Lattice-Boltzmann multi relaxation time method) and estimate the permeability. These results will be useful for understanding the deformation process and hydraulic properties across meter-scale damage zones.

Vibration modelling and verifications for whole aero-engine

NASA Astrophysics Data System (ADS)

Chen, G.

2015-08-01

In this study, a new rotor-ball-bearing-casing coupling dynamic model for a practical aero-engine is established. In the coupling system, the rotor and casing systems are modelled using the finite element method, support systems are modelled as lumped parameter models, nonlinear factors of ball bearings and faults are included, and four types of supports and connection models are defined to model the complex rotor-support-casing coupling system of the aero-engine. A new numerical integral method that combines the Newmark-β method and the improved Newmark-β method (Zhai method) is used to obtain the system responses. Finally, the new model is verified in three ways: (1) modal experiment based on rotor-ball bearing rig, (2) modal experiment based on rotor-ball-bearing-casing rig, and (3) fault simulations for a certain type of missile turbofan aero-engine vibration. The results show that the proposed model can not only simulate the natural vibration characteristics of the whole aero-engine but also effectively perform nonlinear dynamic simulations of a whole aero-engine with faults.

Fault detection and diagnosis of photovoltaic systems

NASA Astrophysics Data System (ADS)

Wu, Xing

The rapid growth of the solar industry over the past several years has expanded the significance of photovoltaic (PV) systems. One of the primary aims of research in building-integrated PV systems is to improve the performance of the system's efficiency, availability, and reliability. Although much work has been done on technological design to increase a photovoltaic module's efficiency, there is little research so far on fault diagnosis for PV systems. Faults in a PV system, if not detected, may not only reduce power generation, but also threaten the availability and reliability, effectively the "security" of the whole system. In this paper, first a circuit-based simulation baseline model of a PV system with maximum power point tracking (MPPT) is developed using MATLAB software. MATLAB is one of the most popular tools for integrating computation, visualization and programming in an easy-to-use modeling environment. Second, data collection of a PV system at variable surface temperatures and insolation levels under normal operation is acquired. The developed simulation model of PV system is then calibrated and improved by comparing modeled I-V and P-V characteristics with measured I--V and P--V characteristics to make sure the simulated curves are close to those measured values from the experiments. Finally, based on the circuit-based simulation model, a PV model of various types of faults will be developed by changing conditions or inputs in the MATLAB model, and the I--V and P--V characteristic curves, and the time-dependent voltage and current characteristics of the fault modalities will be characterized for each type of fault. These will be developed as benchmark I-V or P-V, or prototype transient curves. If a fault occurs in a PV system, polling and comparing actual measured I--V and P--V characteristic curves with both normal operational curves and these baseline fault curves will aid in fault diagnosis.

On-line bolt-loosening detection method of key components of running trains using binocular vision

NASA Astrophysics Data System (ADS)

Xie, Yanxia; Sun, Junhua

2017-11-01

Bolt loosening, as one of hidden faults, affects the running quality of trains and even causes serious safety accidents. However, the developed fault detection approaches based on two-dimensional images cannot detect bolt-loosening due to lack of depth information. Therefore, we propose a novel online bolt-loosening detection method using binocular vision. Firstly, the target detection model based on convolutional neural network (CNN) is used to locate the target regions. And then, stereo matching and three-dimensional reconstruction are performed to detect bolt-loosening faults. The experimental results show that the looseness of multiple bolts can be characterized by the method simultaneously. The measurement repeatability and precision are less than 0.03mm, 0.09mm respectively, and its relative error is controlled within 1.09%.

A high-fidelity airbus benchmark for system fault detection and isolation and flight control law clearance

NASA Astrophysics Data System (ADS)

Goupil, Ph.; Puyou, G.

2013-12-01

This paper presents a high-fidelity generic twin engine civil aircraft model developed by Airbus for advanced flight control system research. The main features of this benchmark are described to make the reader aware of the model complexity and representativeness. It is a complete representation including the nonlinear rigid-body aircraft model with a full set of control surfaces, actuator models, sensor models, flight control laws (FCL), and pilot inputs. Two applications of this benchmark in the framework of European projects are presented: FCL clearance using optimization and advanced fault detection and diagnosis (FDD).

How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data.

PubMed

Furquim, Gustavo; Filho, Geraldo P R; Jalali, Roozbeh; Pessin, Gustavo; Pazzi, Richard W; Ueyama, Jó

2018-03-19

The rise in the number and intensity of natural disasters is a serious problem that affects the whole world. The consequences of these disasters are significantly worse when they occur in urban districts because of the casualties and extent of the damage to goods and property that is caused. Until now feasible methods of dealing with this have included the use of wireless sensor networks (WSNs) for data collection and machine-learning (ML) techniques for forecasting natural disasters. However, there have recently been some promising new innovations in technology which have supplemented the task of monitoring the environment and carrying out the forecasting. One of these schemes involves adopting IP-based (Internet Protocol) sensor networks, by using emerging patterns for IoT. In light of this, in this study, an attempt has been made to set out and describe the results achieved by SENDI (System for dEtecting and forecasting Natural Disasters based on IoT). SENDI is a fault-tolerant system based on IoT, ML and WSN for the detection and forecasting of natural disasters and the issuing of alerts. The system was modeled by means of ns-3 and data collected by a real-world WSN installed in the town of São Carlos - Brazil, which carries out the data collection from rivers in the region. The fault-tolerance is embedded in the system by anticipating the risk of communication breakdowns and the destruction of the nodes during disasters. It operates by adding intelligence to the nodes to carry out the data distribution and forecasting, even in extreme situations. A case study is also included for flash flood forecasting and this makes use of the ns-3 SENDI model and data collected by WSN.

How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data

PubMed Central

Furquim, Gustavo; Filho, Geraldo P. R.; Pessin, Gustavo; Pazzi, Richard W.

2018-01-01

The rise in the number and intensity of natural disasters is a serious problem that affects the whole world. The consequences of these disasters are significantly worse when they occur in urban districts because of the casualties and extent of the damage to goods and property that is caused. Until now feasible methods of dealing with this have included the use of wireless sensor networks (WSNs) for data collection and machine-learning (ML) techniques for forecasting natural disasters. However, there have recently been some promising new innovations in technology which have supplemented the task of monitoring the environment and carrying out the forecasting. One of these schemes involves adopting IP-based (Internet Protocol) sensor networks, by using emerging patterns for IoT. In light of this, in this study, an attempt has been made to set out and describe the results achieved by SENDI (System for dEtecting and forecasting Natural Disasters based on IoT). SENDI is a fault-tolerant system based on IoT, ML and WSN for the detection and forecasting of natural disasters and the issuing of alerts. The system was modeled by means of ns-3 and data collected by a real-world WSN installed in the town of São Carlos - Brazil, which carries out the data collection from rivers in the region. The fault-tolerance is embedded in the system by anticipating the risk of communication breakdowns and the destruction of the nodes during disasters. It operates by adding intelligence to the nodes to carry out the data distribution and forecasting, even in extreme situations. A case study is also included for flash flood forecasting and this makes use of the ns-3 SENDI model and data collected by WSN. PMID:29562657

Investigation of the applicability of a functional programming model to fault-tolerant parallel processing for knowledge-based systems

NASA Technical Reports Server (NTRS)

Harper, Richard

1989-01-01

In a fault-tolerant parallel computer, a functional programming model can facilitate distributed checkpointing, error recovery, load balancing, and graceful degradation. Such a model has been implemented on the Draper Fault-Tolerant Parallel Processor (FTPP). When used in conjunction with the FTPP's fault detection and masking capabilities, this implementation results in a graceful degradation of system performance after faults. Three graceful degradation algorithms have been implemented and are presented. A user interface has been implemented which requires minimal cognitive overhead by the application programmer, masking such complexities as the system's redundancy, distributed nature, variable complement of processing resources, load balancing, fault occurrence and recovery. This user interface is described and its use demonstrated. The applicability of the functional programming style to the Activation Framework, a paradigm for intelligent systems, is then briefly described.

Detection of faults and software reliability analysis

NASA Technical Reports Server (NTRS)

Knight, J. C.

1986-01-01

Multiversion or N-version programming was proposed as a method of providing fault tolerance in software. The approach requires the separate, independent preparation of multiple versions of a piece of software for some application. Specific topics addressed are: failure probabilities in N-version systems, consistent comparison in N-version systems, descriptions of the faults found in the Knight and Leveson experiment, analytic models of comparison testing, characteristics of the input regions that trigger faults, fault tolerance through data diversity, and the relationship between failures caused by automatically seeded faults.

Transient Faults in Computer Systems

NASA Technical Reports Server (NTRS)

Masson, Gerald M.

1993-01-01

A powerful technique particularly appropriate for the detection of errors caused by transient faults in computer systems was developed. The technique can be implemented in either software or hardware; the research conducted thus far primarily considered software implementations. The error detection technique developed has the distinct advantage of having provably complete coverage of all errors caused by transient faults that affect the output produced by the execution of a program. In other words, the technique does not have to be tuned to a particular error model to enhance error coverage. Also, the correctness of the technique can be formally verified. The technique uses time and software redundancy. The foundation for an effective, low-overhead, software-based certification trail approach to real-time error detection resulting from transient fault phenomena was developed.

Ground Surface Deformation in Unconsolidated Sediments Caused by Bedrock Fault Movements: Dip-Slip and Strike-Slip Fault Model Test and Field Survey

NASA Astrophysics Data System (ADS)

Ueta, K.; Tani, K.

2001-12-01

Sandbox experiments were performed to investigate ground surface deformation in unconsolidated sediments caused by dip-slip and strike-slip motion on bedrock faults. A 332.5 cm long, 200 cm high, and 40 cm wide sandbox was used in a dip-slip fault model test. In the strike-slip fault test, a 600 cm long, 250 cm wide, and 60 cm high sandbox and a 170 cm long, 25 cm wide, 15 cm high sandbox were used. Computerized X-ray tomography applied to the sandbox experiments made it possible to analyze the kinematic evolution, as well as the three-dimensional geometry, of the faults. The fault type, fault dip, fault displacement, thickness and density of sandpack and grain size of the sand were varied for different experiments. Field survey of active faults in Japan and California were also made to investigate the deformation of unconsolidated sediments overlying bedrock faults. A comparison of the experimental results with natural cases of active faults reveals the following: (1) In the case of dip-slip faulting, the shear bands are not shown as one linear plane but as en echelon pattern. Thicker and finer unconsolidated sediments produce more shear bands and clearer en echelon shear band patterns. (2) In the case of left-lateral strike-slip faulting, the deformation of the sand pack with increasing basement displacement is observed as follows. a) In three dimensions, the right-stepping shears that have a "cirque" / "shell" / "ship body" shape develop on both sides of the basement fault. The shears on one side of the basement fault join those on the other side, resulting in helicoidal shaped shear surfaces. Shears reach the surface of the sand near or above the basement fault and en echelon Riedel shears are observed at the surface of the sand. b) Right-stepping pressure ridges develop within the zone defined by the Riedel shears. c) Lower-angle shears generally branch off from the first Riedel shears. d) Right-stepping helicoidal shaped lower-angle shears offset Riedel shears and pressure ridges, and left-stepping and right-stepping pressure ridges are observed. d) With displacement concentrated on the central throughgoing fault zone, a "Zone of shear band" (ZSB) developed directly above the basement fault. The geometry of the ZSB shows a strong resemblance to linear ridge and trough geomorphology associated with active strike-slip faulting. (3) In the case of normal faulting, the location of the surface fault rupture is just above the bedrock faults, which have no relationship with the fault dip. On the other hand, the location of the surface rupture of the reverse fault has closely relationship with the fault dip. In the case of strike-slip faulting, the width of the deformation zone in dense sand is wider than that in loose sand. (4) The horizontal distance of surface rupture from the bedrock fault normalized by the height of sand mass (W/H) does not depend on the height of sand mass and grain size of sand. The values of W/H from the test agree well with those of earthquake faults. (5) The normalized base displacement required to propagate the shear rupture zone to the ground surface (D/H), in the case of normal faulting, is lower than those for reverse faulting and strike-slip faulting.

A comparative study of sensor fault diagnosis methods based on observer for ECAS system

NASA Astrophysics Data System (ADS)

Xu, Xing; Wang, Wei; Zou, Nannan; Chen, Long; Cui, Xiaoli

2017-03-01

The performance and practicality of electronically controlled air suspension (ECAS) system are highly dependent on the state information supplied by kinds of sensors, but faults of sensors occur frequently. Based on a non-linearized 3-DOF 1/4 vehicle model, different methods of fault detection and isolation (FDI) are used to diagnose the sensor faults for ECAS system. The considered approaches include an extended Kalman filter (EKF) with concise algorithm, a strong tracking filter (STF) with robust tracking ability, and the cubature Kalman filter (CKF) with numerical precision. We propose three filters of EKF, STF, and CKF to design a state observer of ECAS system under typical sensor faults and noise. Results show that three approaches can successfully detect and isolate faults respectively despite of the existence of environmental noise, FDI time delay and fault sensitivity of different algorithms are different, meanwhile, compared with EKF and STF, CKF method has best performing FDI of sensor faults for ECAS system.

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles.

PubMed

Jeon, Namju; Lee, Hyeongcheol

2016-12-12

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed.

Failure Detecting Method of Fault Current Limiter System with Rectifier

NASA Astrophysics Data System (ADS)

Tokuda, Noriaki; Matsubara, Yoshio; Asano, Masakuni; Ohkuma, Takeshi; Sato, Yoshibumi; Takahashi, Yoshihisa

A fault current limiter (FCL) is extensively needed to suppress fault current, particularly required for trunk power systems connecting high-voltage transmission lines, such as 500kV class power system which constitutes the nucleus of the electric power system. We proposed a new type FCL system (rectifier type FCL), consisting of solid-state diodes, DC reactor and bypass AC reactor, and demonstrated the excellent performances of this FCL by developing the small 6.6kV and 66kV model. It is important to detect the failure of power devices used in the rectifier under the normal operating condition, for keeping the excellent reliability of the power system. In this paper, we have proposed a new failure detecting method of power devices most suitable for the rectifier type FCL. This failure detecting system is simple and compact. We have adapted the proposed system to the 66kV prototype single-phase model and successfully demonstrated to detect the failure of power devices.

Distributed fault detection over sensor networks with Markovian switching topologies

NASA Astrophysics Data System (ADS)

Ge, Xiaohua; Han, Qing-Long

2014-05-01

This paper deals with the distributed fault detection for discrete-time Markov jump linear systems over sensor networks with Markovian switching topologies. The sensors are scatteredly deployed in the sensor field and the fault detectors are physically distributed via a communication network. The system dynamics changes and sensing topology variations are modeled by a discrete-time Markov chain with incomplete mode transition probabilities. Each of these sensor nodes firstly collects measurement outputs from its all underlying neighboring nodes, processes these data in accordance with the Markovian switching topologies, and then transmits the processed data to the remote fault detector node. Network-induced delays and accumulated data packet dropouts are incorporated in the data transmission between the sensor nodes and the distributed fault detector nodes through the communication network. To generate localized residual signals, mode-independent distributed fault detection filters are proposed. By means of the stochastic Lyapunov functional approach, the residual system performance analysis is carried out such that the overall residual system is stochastically stable and the error between each residual signal and the fault signal is made as small as possible. Furthermore, a sufficient condition on the existence of the mode-independent distributed fault detection filters is derived in the simultaneous presence of incomplete mode transition probabilities, Markovian switching topologies, network-induced delays, and accumulated data packed dropouts. Finally, a stirred-tank reactor system is given to show the effectiveness of the developed theoretical results.

Multi-Unmanned Aerial Vehicle (UAV) Cooperative Fault Detection Employing Differential Global Positioning (DGPS), Inertial and Vision Sensors.

PubMed

Heredia, Guillermo; Caballero, Fernando; Maza, Iván; Merino, Luis; Viguria, Antidio; Ollero, Aníbal

2009-01-01

This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented.

Model-Based Assurance Case+ (MBAC+): Tutorial on Modeling Radiation Hardness Assurance Activities

NASA Technical Reports Server (NTRS)

Austin, Rebekah; Label, Ken A.; Sampson, Mike J.; Evans, John; Witulski, Art; Sierawski, Brian; Karsai, Gabor; Mahadevan, Nag; Schrimpf, Ron; Reed, Robert A.

2017-01-01

This presentation will cover why modeling is useful for radiation hardness assurance cases, and also provide information on Model-Based Assurance Case+ (MBAC+), NASAs Reliability Maintainability Template, and Fault Propagation Modeling.

Modeling the Fault Tolerant Capability of a Flight Control System: An Exercise in SCR Specification

NASA Technical Reports Server (NTRS)

Alexander, Chris; Cortellessa, Vittorio; DelGobbo, Diego; Mili, Ali; Napolitano, Marcello

2000-01-01

In life-critical and mission-critical applications, it is important to make provisions for a wide range of contingencies, by providing means for fault tolerance. In this paper, we discuss the specification of a flight control system that is fault tolerant with respect to sensor faults. Redundancy is provided by analytical relations that hold between sensor readings; depending on the conditions, this redundancy can be used to detect, identify and accommodate sensor faults.

A dynamic integrated fault diagnosis method for power transformers.

PubMed

Gao, Wensheng; Bai, Cuifen; Liu, Tong

2015-01-01

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified.

A Dynamic Integrated Fault Diagnosis Method for Power Transformers

PubMed Central

Gao, Wensheng; Liu, Tong

2015-01-01

In order to diagnose transformer fault efficiently and accurately, a dynamic integrated fault diagnosis method based on Bayesian network is proposed in this paper. First, an integrated fault diagnosis model is established based on the causal relationship among abnormal working conditions, failure modes, and failure symptoms of transformers, aimed at obtaining the most possible failure mode. And then considering the evidence input into the diagnosis model is gradually acquired and the fault diagnosis process in reality is multistep, a dynamic fault diagnosis mechanism is proposed based on the integrated fault diagnosis model. Different from the existing one-step diagnosis mechanism, it includes a multistep evidence-selection process, which gives the most effective diagnostic test to be performed in next step. Therefore, it can reduce unnecessary diagnostic tests and improve the accuracy and efficiency of diagnosis. Finally, the dynamic integrated fault diagnosis method is applied to actual cases, and the validity of this method is verified. PMID:25685841

Real-Time Model-Based Leak-Through Detection within Cryogenic Flow Systems

NASA Technical Reports Server (NTRS)

Walker, M.; Figueroa, F.

2015-01-01

The timely detection of leaks within cryogenic fuel replenishment systems is of significant importance to operators on account of the safety and economic impacts associated with material loss and operational inefficiencies. Associated loss in control of pressure also effects the stability and ability to control the phase of cryogenic fluids during replenishment operations. Current research dedicated to providing Prognostics and Health Management (PHM) coverage of such cryogenic replenishment systems has focused on the detection of leaks to atmosphere involving relatively simple model-based diagnostic approaches that, while effective, are unable to isolate the fault to specific piping system components. The authors have extended this research to focus on the detection of leaks through closed valves that are intended to isolate sections of the piping system from the flow and pressurization of cryogenic fluids. The described approach employs model-based detection of leak-through conditions based on correlations of pressure changes across isolation valves and attempts to isolate the faults to specific valves. Implementation of this capability is enabled by knowledge and information embedded in the domain model of the system. The approach has been used effectively to detect such leak-through faults during cryogenic operational testing at the Cryogenic Testbed at NASA's Kennedy Space Center.

Predeployment validation of fault-tolerant systems through software-implemented fault insertion

NASA Technical Reports Server (NTRS)

Czeck, Edward W.; Siewiorek, Daniel P.; Segall, Zary Z.

1989-01-01

Fault injection-based automated testing (FIAT) environment, which can be used to experimentally characterize and evaluate distributed realtime systems under fault-free and faulted conditions is described. A survey is presented of validation methodologies. The need for fault insertion based on validation methodologies is demonstrated. The origins and models of faults, and motivation for the FIAT concept are reviewed. FIAT employs a validation methodology which builds confidence in the system through first providing a baseline of fault-free performance data and then characterizing the behavior of the system with faults present. Fault insertion is accomplished through software and allows faults or the manifestation of faults to be inserted by either seeding faults into memory or triggering error detection mechanisms. FIAT is capable of emulating a variety of fault-tolerant strategies and architectures, can monitor system activity, and can automatically orchestrate experiments involving insertion of faults. There is a common system interface which allows ease of use to decrease experiment development and run time. Fault models chosen for experiments on FIAT have generated system responses which parallel those observed in real systems under faulty conditions. These capabilities are shown by two example experiments each using a different fault-tolerance strategy.

Hardware fault insertion and instrumentation system: Mechanization and validation

NASA Technical Reports Server (NTRS)

Benson, J. W.

1987-01-01

Automated test capability for extensive low-level hardware fault insertion testing is developed. The test capability is used to calibrate fault detection coverage and associated latency times as relevant to projecting overall system reliability. Described are modifications made to the NASA Ames Reconfigurable Flight Control System (RDFCS) Facility to fully automate the total test loop involving the Draper Laboratories' Fault Injector Unit. The automated capability provided included the application of sequences of simulated low-level hardware faults, the precise measurement of fault latency times, the identification of fault symptoms, and bulk storage of test case results. A PDP-11/60 served as a test coordinator, and a PDP-11/04 as an instrumentation device. The fault injector was controlled by applications test software in the PDP-11/60, rather than by manual commands from a terminal keyboard. The time base was especially developed for this application to use a variety of signal sources in the system simulator.

Post-seismic and interseismic fault creep I: model description

NASA Astrophysics Data System (ADS)

Hetland, E. A.; Simons, M.; Dunham, E. M.

2010-04-01

We present a model of localized, aseismic fault creep during the full interseismic period, including both transient and steady fault creep, in response to a sequence of imposed coseismic slip events and tectonic loading. We consider the behaviour of models with linear viscous, non-linear viscous, rate-dependent friction, and rate- and state-dependent friction fault rheologies. Both the transient post-seismic creep and the pattern of steady interseismic creep rates surrounding asperities depend on recent coseismic slip and fault rheologies. In these models, post-seismic fault creep is manifest as pulses of elevated creep rates that propagate from the coseismic slip, these pulses feature sharper fronts and are longer lived in models with rate-state friction compared to other models. With small characteristic slip distances in rate-state friction models, interseismic creep is similar to that in models with rate-dependent friction faults, except for the earliest periods of post-seismic creep. Our model can be used to constrain fault rheologies from geodetic observations in cases where the coseismic slip history is relatively well known. When only considering surface deformation over a short period of time, there are strong trade-offs between fault rheology and the details of the imposed coseismic slip. Geodetic observations over longer times following an earthquake will reduce these trade-offs, while simultaneous modelling of interseismic and post-seismic observations provide the strongest constraints on fault rheologies.

Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

2001-01-01

This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

Preliminary photovoltaic arc-fault prognostic tests using sacrificial fiber optic cabling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Jay Dean; Blemel, Kenneth D.; Peter, Francis

2013-02-01

Through the New Mexico Small Business Assistance Program, Sandia National Laboratories worked with Sentient Business Systems, Inc. to develop and test a novel photovoltaic (PV) arc-fault detection system. The system operates by pairing translucent polymeric fiber optic sensors with electrical circuitry so that any external abrasion to the system or internal heating causes the fiber optic connection to fail or detectably degrade. A periodic pulse of light is sent through the optical path using a transmitter-receiver pair. If the receiver does not detect the pulse, an alarm is sounded and the PV system can be de-energized. This technology has themore » unique ability to prognostically determine impending failures to the electrical system in two ways: (a) the optical connection is severed prior to physical abrasion or cutting of PV DC electrical conductors, and (b) the polymeric fiber optic cable melts via Joule heating before an arc-fault is established through corrosion. Three arc-faults were created in different configurations found in PV systems with the integrated fiber optic system to determine the feasibility of the technology. In each case, the fiber optic cable was broken and the system annunciated the fault.« less

Failure detection and fault management techniques for flush airdata sensing systems

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A.; Moes, Timothy R.; Leondes, Cornelius T.

1992-01-01

Methods based on chi-squared analysis are presented for detecting system and individual-port failures in the high-angle-of-attack flush airdata sensing system on the NASA F-18 High Alpha Research Vehicle. The HI-FADS hardware is introduced, and the aerodynamic model describes measured pressure in terms of dynamic pressure, angle of attack, angle of sideslip, and static pressure. Chi-squared analysis is described in the presentation of the concept for failure detection and fault management which includes nominal, iteration, and fault-management modes. A matrix of pressure orifices arranged in concentric circles on the nose of the aircraft indicate the parameters which are applied to the regression algorithms. The sensing techniques are applied to the F-18 flight data, and two examples are given of the computed angle-of-attack time histories. The failure-detection and fault-management techniques permit the matrix to be multiply redundant, and the chi-squared analysis is shown to be useful in the detection of failures.

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

PubMed Central

Bustillo, Andres; Correa, Maritza; Reñones, Anibal

2011-01-01

The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases. PMID:22163766

A virtual sensor for online fault detection of multitooth-tools.

PubMed

Bustillo, Andres; Correa, Maritza; Reñones, Anibal

2011-01-01

The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.

Real-time diagnostics for a reusable rocket engine

NASA Technical Reports Server (NTRS)

Guo, T. H.; Merrill, W.; Duyar, A.

1992-01-01

A hierarchical, decentralized diagnostic system is proposed for the Real-Time Diagnostic System component of the Intelligent Control System (ICS) for reusable rocket engines. The proposed diagnostic system has three layers of information processing: condition monitoring, fault mode detection, and expert system diagnostics. The condition monitoring layer is the first level of signal processing. Here, important features of the sensor data are extracted. These processed data are then used by the higher level fault mode detection layer to do preliminary diagnosis on potential faults at the component level. Because of the closely coupled nature of the rocket engine propulsion system components, it is expected that a given engine condition may trigger more than one fault mode detector. Expert knowledge is needed to resolve the conflicting reports from the various failure mode detectors. This is the function of the diagnostic expert layer. Here, the heuristic nature of this decision process makes it desirable to use an expert system approach. Implementation of the real-time diagnostic system described above requires a wide spectrum of information processing capability. Generally, in the condition monitoring layer, fast data processing is often needed for feature extraction and signal conditioning. This is usually followed by some detection logic to determine the selected faults on the component level. Three different techniques are used to attack different fault detection problems in the NASA LeRC ICS testbed simulation. The first technique employed is the neural network application for real-time sensor validation which includes failure detection, isolation, and accommodation. The second approach demonstrated is the model-based fault diagnosis system using on-line parameter identification. Besides these model based diagnostic schemes, there are still many failure modes which need to be diagnosed by the heuristic expert knowledge. The heuristic expert knowledge is implemented using a real-time expert system tool called G2 by Gensym Corp. Finally, the distributed diagnostic system requires another level of intelligence to oversee the fault mode reports generated by component fault detectors. The decision making at this level can best be done using a rule-based expert system. This level of expert knowledge is also implemented using G2.

Common faults and their impacts for rooftop air conditioners

DOE Office of Scientific and Technical Information (OSTI.GOV)

Breuker, M.S.; Braun, J.E.

This paper identifies important faults and their performance impacts for rooftop air conditioners. The frequencies of occurrence and the relative costs of service for different faults were estimated through analysis of service records. Several of the important and difficult to diagnose refrigeration cycle faults were simulated in the laboratory. Also, the impacts on several performance indices were quantified through transient testing for a range of conditions and fault levels. The transient test results indicated that fault detection and diagnostics could be performed using methods that incorporate steady-state assumptions and models. Furthermore, the fault testing led to a set of genericmore » rules for the impacts of faults on measurements that could be used for fault diagnoses. The average impacts of the faults on cooling capacity and coefficient of performance (COP) were also evaluated. Based upon the results, all of the faults are significant at the levels introduced, and should be detected and diagnosed by an FDD system. The data set obtained during this work was very comprehensive, and was used to design and evaluate the performance of an FDD method that will be reported in a future paper.« less

Learning and diagnosing faults using neural networks

NASA Technical Reports Server (NTRS)

Whitehead, Bruce A.; Kiech, Earl L.; Ali, Moonis

1990-01-01

Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Model-Based Diagnosis and Prognosis of a Water Recycling System

NASA Technical Reports Server (NTRS)

Roychoudhury, Indranil; Hafiychuk, Vasyl; Goebel, Kai Frank

2013-01-01

A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

Constraints on the stress state of the San Andreas Fault with analysis based on core and cuttings from San Andreas Fault Observatory at Depth (SAFOD) drilling phases 1 and 2

USGS Publications Warehouse

Tembe, S.; Lockner, D.; Wong, T.-F.

2009-01-01

Analysis of field data has led different investigators to conclude that the San Andreas Fault (SAF) has either anomalously low frictional sliding strength (?? 0.6). Arguments for the apparent weakness of the SAF generally hinge on conceptual models involving intrinsically weak gouge or elevated pore pressure within the fault zone. Some models assert that weak gouge and/or high pore pressure exist under static conditions while others consider strength loss or fluid pressure increase due to rapid coseismic fault slip. The present paper is composed of three parts. First, we develop generalized equations, based on and consistent with the Rice (1992) fault zone model to relate stress orientation and magnitude to depth-dependent coefficient of friction and pore pressure. Second, we present temperature-and pressure-dependent friction measurements from wet illite-rich fault gouge extracted from San Andreas Fault Observatory at Depth (SAFOD) phase 1 core samples and from weak minerals associated with the San Andreas Fault. Third, we reevaluate the state of stress on the San Andreas Fault in light of new constraints imposed by SAFOD borehole data. Pure talc (?????0.1) had the lowest strength considered and was sufficiently weak to satisfy weak fault heat flow and stress orientation constraints with hydrostatic pore pressure. Other fault gouges showed a systematic increase in strength with increasing temperature and pressure. In this case, heat flow and stress orientation constraints would require elevated pore pressure and, in some cases, fault zone pore pressure in excess of vertical stress. Copyright 2009 by the American Geophysical Union.

Fault Diagnostics and Prognostics for Large Segmented SRMs

NASA Technical Reports Server (NTRS)

Luchinsky, Dmitry; Osipov, Viatcheslav V.; Smelyanskiy, Vadim N.; Timucin, Dogan A.; Uckun, Serdar; Hayashida, Ben; Watson, Michael; McMillin, Joshua; Shook, David; Johnson, Mont;

Online Fault Detection of Permanent Magnet Demagnetization for IPMSMs by Nonsingular Fast Terminal-Sliding-Mode Observer

PubMed Central

Zhao, Kai-Hui; Chen, Te-Fang; Zhang, Chang-Fan; He, Jing; Huang, Gang

2014-01-01

To prevent irreversible demagnetization of a permanent magnet (PM) for interior permanent magnet synchronous motors (IPMSMs) by flux-weakening control, a robust PM flux-linkage nonsingular fast terminal-sliding-mode observer (NFTSMO) is proposed to detect demagnetization faults. First, the IPMSM mathematical model of demagnetization is presented. Second, the construction of the NFTSMO to estimate PM demagnetization faults in IPMSM is described, and a proof of observer stability is given. The fault decision criteria and fault-processing method are also presented. Finally, the proposed scheme was simulated using MATLAB/Simulink and implemented on the RT-LAB platform. A number of robustness tests have been carried out. The scheme shows good performance in spite of speed fluctuations, torque ripples and the uncertainties of stator resistance. PMID:25490582

Online fault detection of permanent magnet demagnetization for IPMSMs by nonsingular fast terminal-sliding-mode observer.

PubMed

Zhao, Kai-Hui; Chen, Te-Fang; Zhang, Chang-Fan; He, Jing; Huang, Gang

2014-12-05

To prevent irreversible demagnetization of a permanent magnet (PM) for interior permanent magnet synchronous motors (IPMSMs) by flux-weakening control, a robust PM flux-linkage nonsingular fast terminal-sliding-mode observer (NFTSMO) is proposed to detect demagnetization faults. First, the IPMSM mathematical model of demagnetization is presented. Second, the construction of the NFTSMO to estimate PM demagnetization faults in IPMSM is described, and a proof of observer stability is given. The fault decision criteria and fault-processing method are also presented. Finally, the proposed scheme was simulated using MATLAB/Simulink and implemented on the RT-LAB platform. A number of robustness tests have been carried out. The scheme shows good performance in spite of speed fluctuations, torque ripples and the uncertainties of stator resistance.

Efficient fault diagnosis of helicopter gearboxes

NASA Technical Reports Server (NTRS)

Chin, H.; Danai, K.; Lewicki, D. G.

1993-01-01

Application of a diagnostic system to a helicopter gearbox is presented. The diagnostic system is a nonparametric pattern classifier that uses a multi-valued influence matrix (MVIM) as its diagnostic model and benefits from a fast learning algorithm that enables it to estimate its diagnostic model from a small number of measurement-fault data. To test this diagnostic system, vibration measurements were collected from a helicopter gearbox test stand during accelerated fatigue tests and at various fault instances. The diagnostic results indicate that the MVIM system can accurately detect and diagnose various gearbox faults so long as they are included in training.

Large-Scale Exploratory Analysis, Cleaning, and Modeling for Event Detection in Real-World Power Systems Data

DTIC Science & Technology

2013-11-01

big data with R is relatively new. RHadoop is a mature product from Revolution Analytics that uses R with Hadoop Streaming [15] and provides...agnostic all- data summaries or computations, in which case we use MapReduce directly. 2.3 D&R Software Environment In this work, we use the Hadoop ...job scheduling and tracking, data distribu- tion, system architecture, heterogeneity, and fault-tolerance. Hadoop also provides a distributed key-value

Case study: Optimizing fault model input parameters using bio-inspired algorithms

NASA Astrophysics Data System (ADS)

Plucar, Jan; Grunt, Onřej; Zelinka, Ivan

2017-07-01

We present a case study that demonstrates a bio-inspired approach in the process of finding optimal parameters for GSM fault model. This model is constructed using Petri Nets approach it represents dynamic model of GSM network environment in the suburban areas of Ostrava city (Czech Republic). We have been faced with a task of finding optimal parameters for an application that requires high amount of data transfers between the application itself and secure servers located in datacenter. In order to find the optimal set of parameters we employ bio-inspired algorithms such as Differential Evolution (DE) or Self Organizing Migrating Algorithm (SOMA). In this paper we present use of these algorithms, compare results and judge their performance in fault probability mitigation.

Detecting Faults In High-Voltage Transformers

NASA Technical Reports Server (NTRS)

Blow, Raymond K.

1988-01-01

Simple fixture quickly shows whether high-voltage transformer has excessive voids in dielectric materials and whether high-voltage lead wires too close to transformer case. Fixture is "go/no-go" indicator; corona appears if transformer contains such faults. Nests in wire mesh supported by cap of clear epoxy. If transformer has defects, blue glow of corona appears in mesh and is seen through cap.

Using Markov Models of Fault Growth Physics and Environmental Stresses to Optimize Control Actions

NASA Technical Reports Server (NTRS)

Bole, Brian; Goebel, Kai; Vachtsevanos, George

2012-01-01

A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.

Weighted low-rank sparse model via nuclear norm minimization for bearing fault detection

NASA Astrophysics Data System (ADS)

Du, Zhaohui; Chen, Xuefeng; Zhang, Han; Yang, Boyuan; Zhai, Zhi; Yan, Ruqiang

2017-07-01

It is a fundamental task in the machine fault diagnosis community to detect impulsive signatures generated by the localized faults of bearings. The main goal of this paper is to exploit the low-rank physical structure of periodic impulsive features and further establish a weighted low-rank sparse model for bearing fault detection. The proposed model mainly consists of three basic components: an adaptive partition window, a nuclear norm regularization and a weighted sequence. Firstly, due to the periodic repetition mechanism of impulsive feature, an adaptive partition window could be designed to transform the impulsive feature into a data matrix. The highlight of partition window is to accumulate all local feature information and align them. Then, all columns of the data matrix share similar waveforms and a core physical phenomenon arises, i.e., these singular values of the data matrix demonstrates a sparse distribution pattern. Therefore, a nuclear norm regularization is enforced to capture that sparse prior. However, the nuclear norm regularization treats all singular values equally and thus ignores one basic fact that larger singular values have more information volume of impulsive features and should be preserved as much as possible. Therefore, a weighted sequence with adaptively tuning weights inversely proportional to singular amplitude is adopted to guarantee the distribution consistence of large singular values. On the other hand, the proposed model is difficult to solve due to its non-convexity and thus a new algorithm is developed to search one satisfying stationary solution through alternatively implementing one proximal operator operation and least-square fitting. Moreover, the sensitivity analysis and selection principles of algorithmic parameters are comprehensively investigated through a set of numerical experiments, which shows that the proposed method is robust and only has a few adjustable parameters. Lastly, the proposed model is applied to the wind turbine (WT) bearing fault detection and its effectiveness is sufficiently verified. Compared with the current popular bearing fault diagnosis techniques, wavelet analysis and spectral kurtosis, our model achieves a higher diagnostic accuracy.

Pressure Monitoring to Detect Fault Rupture Due to CO 2 Injection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keating, Elizabeth; Dempsey, David; Pawar, Rajesh

The capacity for fault systems to be reactivated by fluid injection is well-known. In the context of CO 2 sequestration, however, the consequence of reactivated faults with respect to leakage and monitoring is poorly understood. Using multi-phase fluid flow simulations, this study addresses key questions concerning the likelihood of ruptures, the timing of consequent upward leakage of CO 2, and the effectiveness of pressure monitoring in the reservoir and overlying zones for rupture detection. A range of injection scenarios was simulated using random sampling of uncertain parameters. These include the assumed distance between the injector and the vulnerable fault zone,more » the critical overpressure required for the fault to rupture, reservoir permeability, and the CO 2 injection rate. We assumed a conservative scenario, in which if at any time during the five-year simulations the critical fault overpressure is exceeded, the fault permeability is assumed to instantaneously increase. For the purposes of conservatism we assume that CO 2 injection continues ‘blindly’ after fault rupture. We show that, despite this assumption, in most cases the CO 2 plume does not reach the base of the ruptured fault after 5 years. As a result, one possible implication of this result is that leak mitigation strategies such as pressure management have a reasonable chance of preventing a CO 2 leak.« less

Pressure Monitoring to Detect Fault Rupture Due to CO 2 Injection

DOE PAGES

Keating, Elizabeth; Dempsey, David; Pawar, Rajesh

2017-08-18

The capacity for fault systems to be reactivated by fluid injection is well-known. In the context of CO 2 sequestration, however, the consequence of reactivated faults with respect to leakage and monitoring is poorly understood. Using multi-phase fluid flow simulations, this study addresses key questions concerning the likelihood of ruptures, the timing of consequent upward leakage of CO 2, and the effectiveness of pressure monitoring in the reservoir and overlying zones for rupture detection. A range of injection scenarios was simulated using random sampling of uncertain parameters. These include the assumed distance between the injector and the vulnerable fault zone,more » the critical overpressure required for the fault to rupture, reservoir permeability, and the CO 2 injection rate. We assumed a conservative scenario, in which if at any time during the five-year simulations the critical fault overpressure is exceeded, the fault permeability is assumed to instantaneously increase. For the purposes of conservatism we assume that CO 2 injection continues ‘blindly’ after fault rupture. We show that, despite this assumption, in most cases the CO 2 plume does not reach the base of the ruptured fault after 5 years. As a result, one possible implication of this result is that leak mitigation strategies such as pressure management have a reasonable chance of preventing a CO 2 leak.« less

Wind turbine fault detection and classification by means of image texture analysis

NASA Astrophysics Data System (ADS)

Ruiz, Magda; Mujica, Luis E.; Alférez, Santiago; Acho, Leonardo; Tutivén, Christian; Vidal, Yolanda; Rodellar, José; Pozo, Francesc

2018-07-01

The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly offshore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of offshore wind turbines is approximately 15-35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to different faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for detection and classification of wind turbine actuators and sensors faults in variable-speed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are represented as two-dimensional matrices to obtain grayscale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture characteristics are used: statistical, wavelet, granulometric and Gabor features. Next, the most significant ones are selected using the conditional mutual criterion. Finally, the faults are detected and distinguished between them (classified) using an automatic classification tool. In particular, a 10-fold cross-validation is used to obtain a more generalized model and evaluates the classification performance. Coupled non-linear aero-hydro-servo-elastic simulations of a 5 MW offshore type wind turbine are carried out in several fault scenarios. The results show a promising methodology able to detect and classify the most common wind turbine faults.

Detection and Correction of Silent Data Corruption for Large-Scale High-Performance Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fiala, David J; Mueller, Frank; Engelmann, Christian

Faults have become the norm rather than the exception for high-end computing on clusters with 10s/100s of thousands of cores. Exacerbating this situation, some of these faults remain undetected, manifesting themselves as silent errors that corrupt memory while applications continue to operate and report incorrect results. This paper studies the potential for redundancy to both detect and correct soft errors in MPI message-passing applications. Our study investigates the challenges inherent to detecting soft errors within MPI application while providing transparent MPI redundancy. By assuming a model wherein corruption in application data manifests itself by producing differing MPI message data betweenmore » replicas, we study the best suited protocols for detecting and correcting MPI data that is the result of corruption. To experimentally validate our proposed detection and correction protocols, we introduce RedMPI, an MPI library which resides in the MPI profiling layer. RedMPI is capable of both online detection and correction of soft errors that occur in MPI applications without requiring any modifications to the application source by utilizing either double or triple redundancy. Our results indicate that our most efficient consistency protocol can successfully protect applications experiencing even high rates of silent data corruption with runtime overheads between 0% and 30% as compared to unprotected applications without redundancy. Using our fault injector within RedMPI, we observe that even a single soft error can have profound effects on running applications, causing a cascading pattern of corruption in most cases causes that spreads to all other processes. RedMPI's protection has been shown to successfully mitigate the effects of soft errors while allowing applications to complete with correct results even in the face of errors.« less

A method of real-time fault diagnosis for power transformers based on vibration analysis

NASA Astrophysics Data System (ADS)

Hong, Kaixing; Huang, Hai; Zhou, Jianping; Shen, Yimin; Li, Yujie

2015-11-01

In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.

An Indirect Adaptive Control Scheme in the Presence of Actuator and Sensor Failures

NASA Technical Reports Server (NTRS)

Sun, Joy Z.; Josh, Suresh M.

2009-01-01

The problem of controlling a system in the presence of unknown actuator and sensor faults is addressed. The system is assumed to have groups of actuators, and groups of sensors, with each group consisting of multiple redundant similar actuators or sensors. The types of actuator faults considered consist of unknown actuators stuck in unknown positions, as well as reduced actuator effectiveness. The sensor faults considered include unknown biases and outages. The approach employed for fault detection and estimation consists of a bank of Kalman filters based on multiple models, and subsequent control reconfiguration to mitigate the effect of biases caused by failed components as well as to obtain stability and satisfactory performance using the remaining actuators and sensors. Conditions for fault identifiability are presented, and the adaptive scheme is applied to an aircraft flight control example in the presence of actuator failures. Simulation results demonstrate that the method can rapidly and accurately detect faults and estimate the fault values, thus enabling safe operation and acceptable performance in spite of failures.

Fault detection and accommodation testing on an F100 engine in an F-15 airplane

NASA Technical Reports Server (NTRS)

Myers, L. P.; Baer-Riedhart, J. L.; Maxwell, M. D.

1985-01-01

The fault detection and accommodation (FDA) methodology for digital engine-control systems may range from simple comparisons of redundant parameters to the more complex and sophisticated observer models of the entire engine system. Evaluations of the various FDA schemes are done using analytical methods, simulation, and limited-altitude-facility testing. Flight testing of the FDA logic has been minimal because of the difficulty of inducing realistic faults in flight. A flight program was conducted to evaluate the fault detection and accommodation capability of a digital electronic engine control in an F-15 aircraft. The objective of the flight program was to induce selected faults and evaluate the resulting actions of the digital engine controller. Comparisons were made between the flight results and predictions. Several anomalies were found in flight and during the ground test. Simulation results showed that the inducement of dual pressure failures was not feasible since the FDA logic was not designed to accommodate these types of failures.

A fault injection experiment using the AIRLAB Diagnostic Emulation Facility

NASA Technical Reports Server (NTRS)

Baker, Robert; Mangum, Scott; Scheper, Charlotte

1988-01-01

The preparation for, conduct of, and results of a simulation based fault injection experiment conducted using the AIRLAB Diagnostic Emulation facilities is described. An objective of this experiment was to determine the effectiveness of the diagnostic self-test sequences used to uncover latent faults in a logic network providing the key fault tolerance features for a flight control computer. Another objective was to develop methods, tools, and techniques for conducting the experiment. More than 1600 faults were injected into a logic gate level model of the Data Communicator/Interstage (C/I). For each fault injected, diagnostic self-test sequences consisting of over 300 test vectors were supplied to the C/I model as inputs. For each test vector within a test sequence, the outputs from the C/I model were compared to the outputs of a fault free C/I. If the outputs differed, the fault was considered detectable for the given test vector. These results were then analyzed to determine the effectiveness of some test sequences. The results established coverage of selt-test diagnostics, identified areas in the C/I logic where the tests did not locate faults, and suggest fault latency reduction opportunities.

Online Detection of Broken Rotor Bar Fault in Induction Motors by Combining Estimation of Signal Parameters via Min-norm Algorithm and Least Square Method

NASA Astrophysics Data System (ADS)

Wang, Pan-Pan; Yu, Qiang; Hu, Yong-Jun; Miao, Chang-Xin

2017-11-01

Current research in broken rotor bar (BRB) fault detection in induction motors is primarily focused on a high-frequency resolution analysis of the stator current. Compared with a discrete Fourier transformation, the parametric spectrum estimation technique has a higher frequency accuracy and resolution. However, the existing detection methods based on parametric spectrum estimation cannot realize online detection, owing to the large computational cost. To improve the efficiency of BRB fault detection, a new detection method based on the min-norm algorithm and least square estimation is proposed in this paper. First, the stator current is filtered using a band-pass filter and divided into short overlapped data windows. The min-norm algorithm is then applied to determine the frequencies of the fundamental and fault characteristic components with each overlapped data window. Next, based on the frequency values obtained, a model of the fault current signal is constructed. Subsequently, a linear least squares problem solved through singular value decomposition is designed to estimate the amplitudes and phases of the related components. Finally, the proposed method is applied to a simulated current and an actual motor, the results of which indicate that, not only parametric spectrum estimation technique.

Constraints on the stress state of the San Andreas fault with analysis based on core and cuttings from SAFOD drilling phases I and II

USGS Publications Warehouse

Lockner, David A.; Tembe, Cheryl; Wong, Teng-fong

2009-01-01

Analysis of field data has led different investigators to conclude that the San Andreas Fault (SAF) has either anomalously low frictional sliding strength (m < 0.2) or strength consistent with standard laboratory tests (m > 0.6). Arguments for the apparent weakness of the SAF generally hinge on conceptual models involving intrinsically weak gouge or elevated pore pressure within the fault zone. Some models assert that weak gouge and/or high pore pressure exist under static conditions while others consider strength loss or fluid pressure increase due to rapid coseismic fault slip. The present paper is composed of three parts. First, we develop generalized equations, based on and consistent with the Rice (1992) fault zone model to relate stress orientation and magnitude to depth-dependent coefficient of friction and pore pressure. Second, we present temperature- and pressure-dependent friction measurements from wet illite-rich fault gouge extracted from San Andreas Fault Observatory at Depth (SAFOD) phase 1 core samples and from weak minerals associated with the San Andreas Fault. Third, we reevaluate the state of stress on the San Andreas Fault in light of new constraints imposed by SAFOD borehole data. Pure talc (m0.1) had the lowest strength considered and was sufficiently weak to satisfy weak fault heat flow and stress orientation constraints with hydrostatic pore pressure. Other fault gouges showed a systematic increase in strength with increasing temperature and pressure. In this case, heat flow and stress orientation constraints would require elevated pore pressure and, in some cases, fault zone pore pressure in excess of vertical stress.

Fault detection method for railway wheel flat using an adaptive multiscale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Zuo, Ming J.; Lin, Jianhui; Liu, Jianxin

2017-02-01

This study explores the capacity of the morphology analysis for railway wheel flat fault detection. A dynamic model of vehicle systems with 56 degrees of freedom was set up along with a wheel flat model to calculate the dynamic responses of axle box. The vehicle axle box vibration signal is complicated because it not only contains the information of wheel defect, but also includes track condition information. Thus, how to extract the influential features of wheels from strong background noise effectively is a typical key issue for railway wheel fault detection. In this paper, an algorithm for adaptive multiscale morphological filtering (AMMF) was proposed, and its effect was evaluated by a simulated signal. And then this algorithm was employed to study the axle box vibration caused by wheel flats, as well as the influence of track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method was verified by bench testing. Research results demonstrate that the AMMF extracts the influential characteristic of axle box vibration signals effectively and can diagnose wheel flat faults in real time.

Final Technical Report: PV Fault Detection Tool.

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, Bruce Hardison; Jones, Christian Birk

The PV Fault Detection Tool project plans to demonstrate that the FDT can (a) detect catastrophic and degradation faults and (b) identify the type of fault. This will be accomplished by collecting fault signatures using different instruments and integrating this information to establish a logical controller for detecting, diagnosing and classifying each fault.

Impact of device level faults in a digital avionic processor

NASA Technical Reports Server (NTRS)

Suk, Ho Kim

1989-01-01

This study describes an experimental analysis of the impact of gate and device-level faults in the processor of a Bendix BDX-930 flight control system. Via mixed mode simulation, faults were injected at the gate (stuck-at) and at the transistor levels and, their propagation through the chip to the output pins was measured. The results show that there is little correspondence between a stuck-at and a device-level fault model, as far as error activity or detection within a functional unit is concerned. In so far as error activity outside the injected unit and at the output pins are concerned, the stuck-at and device models track each other. The stuck-at model, however, overestimates, by over 100 percent, the probability of fault propagation to the output pins. An evaluation of the Mean Error Durations and the Mean Time Between Errors at the output pins shows that the stuck-at model significantly underestimates (by 62 percent) the impact of an internal chip fault on the output pins. Finally, the study also quantifies the impact of device fault by location, both internally and at the output pins.

Thrust-wrench fault interference in a brittle medium: new insights from analogue modelling experiments

NASA Astrophysics Data System (ADS)

Rosas, Filipe; Duarte, Joao; Schellart, Wouter; Tomas, Ricardo; Grigorova, Vili; Terrinha, Pedro

2015-04-01

We present analogue modelling experimental results concerning thrust-wrench fault interference in a brittle medium, to try to evaluate the influence exerted by different prescribed interference angles in the formation of morpho-structural interference fault patterns. All the experiments were conceived to simulate simultaneous reactivation of confining strike-slip and thrust faults defining a (corner) zone of interference, contrasting with previously reported discrete (time and space) superposition of alternating thrust and strike-slip events. Different interference angles of 60°, 90° and 120° were experimentally investigated by comparing the specific structural configurations obtained in each case. Results show that a deltoid-shaped morpho-structural pattern is consistently formed in the fault interference (corner) zone, exhibiting a specific geometry that is fundamentally determined by the different prescribed fault interference angle. Such angle determines the orientation of the displacement vector shear component along the main frontal thrust direction, determining different fault confinement conditions in each case, and imposing a complying geometry and kinematics of the interference deltoid structure. Model comparison with natural examples worldwide shows good geometric and kinematic similarity, pointing to the existence of matching underlying dynamic process. Acknowledgments This work was sponsored by the Fundação para a Ciência e a Tecnologia (FCT) through project MODELINK EXPL/GEO-GEO/0714/2013.

Integrated Fault Diagnosis Algorithm for Motor Sensors of In-Wheel Independent Drive Electric Vehicles

PubMed Central

Jeon, Namju; Lee, Hyeongcheol

2016-01-01

An integrated fault-diagnosis algorithm for a motor sensor of in-wheel independent drive electric vehicles is presented. This paper proposes a method that integrates the high- and low-level fault diagnoses to improve the robustness and performance of the system. For the high-level fault diagnosis of vehicle dynamics, a planar two-track non-linear model is first selected, and the longitudinal and lateral forces are calculated. To ensure redundancy of the system, correlation between the sensor and residual in the vehicle dynamics is analyzed to detect and separate the fault of the drive motor system of each wheel. To diagnose the motor system for low-level faults, the state equation of an interior permanent magnet synchronous motor is developed, and a parity equation is used to diagnose the fault of the electric current and position sensors. The validity of the high-level fault-diagnosis algorithm is verified using Carsim and Matlab/Simulink co-simulation. The low-level fault diagnosis is verified through Matlab/Simulink simulation and experiments. Finally, according to the residuals of the high- and low-level fault diagnoses, fault-detection flags are defined. On the basis of this information, an integrated fault-diagnosis strategy is proposed. PMID:27973431

Structural and numerical modeling of fluid flow and evolving stress fields at a transtensional stepover: A Miocene Andean porphyry copper system as a case study.

NASA Astrophysics Data System (ADS)

Nuñez, R. C.; Griffith, W. A.; Mitchell, T. M.; Marquardt, C.; Iturrieta, P. C.; Cembrano, J. M.

2017-12-01

Obliquely convergent subduction orogens show both margin-parallel and margin-oblique fault systems that are spatially and temporally associated with ore deposits and geothermal systems within the volcanic arc. Fault orientation and mechanical interaction among different fault systems influence the stress field in these arrangements, thus playing a first order control on the regional to local-scale fluid migration paths as documented by the spatial distribution of fault-vein arrays. Our selected case study is a Miocene porphyry copper-type system that crops out in the precordillera of the Maule region along the Teno river Valley (ca. 35°S). Several regional to local faults were recognized in the field: (1) Two first-order, N-striking subvertical dextral faults overlapping at a right stepover; (2) Second-order, N60°E-striking steeply-dipping, dextral-normal faults located at the stepover, and (3) N40°-60°W striking subvertical, sinistral faults crossing the stepover zone. The regional and local scale geology is characterized by volcano-sedimentary rocks (Upper Eocene- Lower Miocene), intruded by Miocene granodioritic plutons (U-Pb zircon age of 18.2 ± 0.11 Ma) and coeval dikes. We implement a 2D boundary element displacement discontinuity method (BEM) model to test the mechanical feasibility of kinematic model of the structural development of the porphyry copper-type system in the stepover between N-striking faults. The model yields the stress field within the stepover region and shows slip and potential opening distribution along the N-striking master faults under a regionally imposed stress field. The model shows that σ1 rotates clockwise where the main faults approach each other, becoming EW when they overlap. This, in turn leads to the generation of both NE- and NW-striking faults within the stepover area. Model results are consistent with the structural and kinematic data collected in the field attesting for enhanced permeability and fluid flow transport and arrest spatially associated with the stepover.

A combined approach based on MAF analysis and AHP method to fault detection mapping: A case study from a gas field, southwest of Iran

NASA Astrophysics Data System (ADS)

Shakiba, Sima; Asghari, Omid; Khah, Nasser Keshavarz Faraj

2018-01-01

A combined geostatitical methodology based on Min/Max Auto-correlation Factor (MAF) analysis and Analytical Hierarchy Process (AHP) is presented to generate a suitable Fault Detection Map (FDM) through seismic attributes. Five seismic attributes derived from a 2D time slice obtained from data related to a gas field located in southwest of Iran are used including instantaneous amplitude, similarity, energy, frequency, and Fault Enhancement Filter (FEF). The MAF analysis is implemented to reduce dimension of input variables, and then AHP method is applied on three obtained de-correlated MAF factors as evidential layer. Three Decision Makers (DMs) are used to construct PCMs for determining weights of selected evidential layer. Finally, weights obtained by AHP were multiplied in normalized valued of each alternative (MAF layers) and the concluded weighted layers were integrated in order to prepare final FDM. Results proved that applying algorithm proposed in this study generate a map more acceptable than the each individual attribute and sharpen the non-surface discontinuities as well as enhancing continuity of detected faults.

Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms

NASA Astrophysics Data System (ADS)

Gangsar, Purushottam; Tiwari, Rajiv

2017-09-01

This paper presents an investigation of vibration and current monitoring for effective fault prediction in induction motor (IM) by using multiclass support vector machine (MSVM) algorithms. Failures of IM may occur due to propagation of a mechanical or electrical fault. Hence, for timely detection of these faults, the vibration as well as current signals was acquired after multiple experiments of varying speeds and external torques from an experimental test rig. Here, total ten different fault conditions that frequently encountered in IM (four mechanical fault, five electrical fault conditions and one no defect condition) have been considered. In the case of stator winding fault, and phase unbalance and single phasing fault, different level of severity were also considered for the prediction. In this study, the identification has been performed of the mechanical and electrical faults, individually and collectively. Fault predictions have been performed using vibration signal alone, current signal alone and vibration-current signal concurrently. The one-versus-one MSVM has been trained at various operating conditions of IM using the radial basis function (RBF) kernel and tested for same conditions, which gives the result in the form of percentage fault prediction. The prediction performance is investigated for the wide range of RBF kernel parameter, i.e. gamma, and selected the best result for one optimal value of gamma for each case. Fault predictions has been performed and investigated for the wide range of operational speeds of the IM as well as external torques on the IM.

Fail-Safe Design for Large Capacity Lithium-Ion Battery Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, G. H.; Smith, K.; Ireland, J.

2012-07-15

A fault leading to a thermal runaway in a lithium-ion battery is believed to grow over time from a latent defect. Significant efforts have been made to detect lithium-ion battery safety faults to proactively facilitate actions minimizing subsequent losses. Scaling up a battery greatly changes the thermal and electrical signals of a system developing a defect and its consequent behaviors during fault evolution. In a large-capacity system such as a battery for an electric vehicle, detecting a fault signal and confining the fault locally in the system are extremely challenging. This paper introduces a fail-safe design methodology for large-capacity lithium-ionmore » battery systems. Analysis using an internal short circuit response model for multi-cell packs is presented that demonstrates the viability of the proposed concept for various design parameters and operating conditions. Locating a faulty cell in a multiple-cell module and determining the status of the fault's evolution can be achieved using signals easily measured from the electric terminals of the module. A methodology is introduced for electrical isolation of a faulty cell from the healthy cells in a system to prevent further electrical energy feed into the fault. Experimental demonstration is presented supporting the model results.« less

Distributed bearing fault diagnosis based on vibration analysis

NASA Astrophysics Data System (ADS)

Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

2016-01-01

Distributed bearing faults appear under various circumstances, for example due to electroerosion or the progression of localized faults. Bearings with distributed faults tend to generate more complex vibration patterns than those with localized faults. Despite the frequent occurrence of such faults, their diagnosis has attracted limited attention. This paper examines a method for the diagnosis of distributed bearing faults employing vibration analysis. The vibrational patterns generated are modeled by incorporating the geometrical imperfections of the bearing components. Comparing envelope spectra of vibration signals shows that one can distinguish between localized and distributed faults. Furthermore, a diagnostic procedure for the detection of distributed faults is proposed. This is evaluated on several bearings with naturally born distributed faults, which are compared with fault-free bearings and bearings with localized faults. It is shown experimentally that features extracted from vibrations in fault-free, localized and distributed fault conditions form clearly separable clusters, thus enabling diagnosis.

Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems

PubMed Central

Huang, Gang; Luo, Yi-Ping; Zhang, Chang-Fan; Huang, Yi-Shan; Zhao, Kai-Hui

2015-01-01

This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in αβ coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method. PMID:25970258

Inverse Transient Analysis for Classification of Wall Thickness Variations in Pipelines

PubMed Central

Tuck, Jeffrey; Lee, Pedro

2013-01-01

Analysis of transient fluid pressure signals has been investigated as an alternative method of fault detection in pipeline systems and has shown promise in both laboratory and field trials. The advantage of the method is that it can potentially provide a fast and cost effective means of locating faults such as leaks, blockages and pipeline wall degradation within a pipeline while the system remains fully operational. The only requirement is that high speed pressure sensors are placed in contact with the fluid. Further development of the method requires detailed numerical models and enhanced understanding of transient flow within a pipeline where variations in pipeline condition and geometry occur. One such variation commonly encountered is the degradation or thinning of pipe walls, which can increase the susceptible of a pipeline to leak development. This paper aims to improve transient-based fault detection methods by investigating how changes in pipe wall thickness will affect the transient behaviour of a system; this is done through the analysis of laboratory experiments. The laboratory experiments are carried out on a stainless steel pipeline of constant outside diameter, into which a pipe section of variable wall thickness is inserted. In order to detect the location and severity of these changes in wall conditions within the laboratory system an inverse transient analysis procedure is employed which considers independent variations in wavespeed and diameter. Inverse transient analyses are carried out using a genetic algorithm optimisation routine to match the response from a one-dimensional method of characteristics transient model to the experimental time domain pressure responses. The accuracy of the detection technique is evaluated and benefits associated with various simplifying assumptions and simulation run times are investigated. It is found that for the case investigated, changes in the wavespeed and nominal diameter of the pipeline are both important to the accuracy of the inverse analysis procedure and can be used to differentiate the observed transient behaviour caused by changes in wall thickness from that caused by other known faults such as leaks. Further application of the method to real pipelines is discussed.

Model Transformation for a System of Systems Dependability Safety Case

NASA Technical Reports Server (NTRS)

Murphy, Judy; Driskell, Stephen B.

2010-01-01

Software plays an increasingly larger role in all aspects of NASA's science missions. This has been extended to the identification, management and control of faults which affect safety-critical functions and by default, the overall success of the mission. Traditionally, the analysis of fault identification, management and control are hardware based. Due to the increasing complexity of system, there has been a corresponding increase in the complexity in fault management software. The NASA Independent Validation & Verification (IV&V) program is creating processes and procedures to identify, and incorporate safety-critical software requirements along with corresponding software faults so that potential hazards may be mitigated. This Specific to Generic ... A Case for Reuse paper describes the phases of a dependability and safety study which identifies a new, process to create a foundation for reusable assets. These assets support the identification and management of specific software faults and, their transformation from specific to generic software faults. This approach also has applications to other systems outside of the NASA environment. This paper addresses how a mission specific dependability and safety case is being transformed to a generic dependability and safety case which can be reused for any type of space mission with an emphasis on software fault conditions.

On damage detection in wind turbine gearboxes using outlier analysis

NASA Astrophysics Data System (ADS)

Antoniadou, Ifigeneia; Manson, Graeme; Dervilis, Nikolaos; Staszewski, Wieslaw J.; Worden, Keith

2012-04-01

The proportion of worldwide installed wind power in power systems increases over the years as a result of the steadily growing interest in renewable energy sources. Still, the advantages offered by the use of wind power are overshadowed by the high operational and maintenance costs, resulting in the low competitiveness of wind power in the energy market. In order to reduce the costs of corrective maintenance, the application of condition monitoring to gearboxes becomes highly important, since gearboxes are among the wind turbine components with the most frequent failure observations. While condition monitoring of gearboxes in general is common practice, with various methods having been developed over the last few decades, wind turbine gearbox condition monitoring faces a major challenge: the detection of faults under the time-varying load conditions prevailing in wind turbine systems. Classical time and frequency domain methods fail to detect faults under variable load conditions, due to the temporary effect that these faults have on vibration signals. This paper uses the statistical discipline of outlier analysis for the damage detection of gearbox tooth faults. A simplified two-degree-of-freedom gearbox model considering nonlinear backlash, time-periodic mesh stiffness and static transmission error, simulates the vibration signals to be analysed. Local stiffness reduction is used for the simulation of tooth faults and statistical processes determine the existence of intermittencies. The lowest level of fault detection, the threshold value, is considered and the Mahalanobis squared-distance is calculated for the novelty detection problem.

Aircraft Engine On-Line Diagnostics Through Dual-Channel Sensor Measurements: Development of an Enhanced System

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2008-01-01

In this paper, an enhanced on-line diagnostic system which utilizes dual-channel sensor measurements is developed for the aircraft engine application. The enhanced system is composed of a nonlinear on-board engine model (NOBEM), the hybrid Kalman filter (HKF) algorithm, and fault detection and isolation (FDI) logic. The NOBEM provides the analytical third channel against which the dual-channel measurements are compared. The NOBEM is further utilized as part of the HKF algorithm which estimates measured engine parameters. Engine parameters obtained from the dual-channel measurements, the NOBEM, and the HKF are compared against each other. When the discrepancy among the signals exceeds a tolerance level, the FDI logic determines the cause of discrepancy. Through this approach, the enhanced system achieves the following objectives: 1) anomaly detection, 2) component fault detection, and 3) sensor fault detection and isolation. The performance of the enhanced system is evaluated in a simulation environment using faults in sensors and components, and it is compared to an existing baseline system.

A Comparison of Hybrid Approaches for Turbofan Engine Gas Path Fault Diagnosis

NASA Astrophysics Data System (ADS)

Lu, Feng; Wang, Yafan; Huang, Jinquan; Wang, Qihang

2016-09-01

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

Latest Progress of Fault Detection and Localization in Complex Electrical Engineering

NASA Astrophysics Data System (ADS)

Zhao, Zheng; Wang, Can; Zhang, Yagang; Sun, Yi

2014-01-01

In the researches of complex electrical engineering, efficient fault detection and localization schemes are essential to quickly detect and locate faults so that appropriate and timely corrective mitigating and maintenance actions can be taken. In this paper, under the current measurement precision of PMU, we will put forward a new type of fault detection and localization technology based on fault factor feature extraction. Lots of simulating experiments indicate that, although there are disturbances of white Gaussian stochastic noise, based on fault factor feature extraction principal, the fault detection and localization results are still accurate and reliable, which also identifies that the fault detection and localization technology has strong anti-interference ability and great redundancy.

Voltage Based Detection Method for High Impedance Fault in a Distribution System

NASA Astrophysics Data System (ADS)

Thomas, Mini Shaji; Bhaskar, Namrata; Prakash, Anupama

2016-09-01

High-impedance faults (HIFs) on distribution feeders cannot be detected by conventional protection schemes, as HIFs are characterized by their low fault current level and waveform distortion due to the nonlinearity of the ground return path. This paper proposes a method to identify the HIFs in distribution system and isolate the faulty section, to reduce downtime. This method is based on voltage measurements along the distribution feeder and utilizes the sequence components of the voltages. Three models of high impedance faults have been considered and source side and load side breaking of the conductor have been studied in this work to capture a wide range of scenarios. The effect of neutral grounding of the source side transformer is also accounted in this study. The results show that the algorithm detects the HIFs accurately and rapidly. Thus, the faulty section can be isolated and service can be restored to the rest of the consumers.

On-line diagnosis of inter-turn short circuit fault for DC brushed motor.

PubMed

Zhang, Jiayuan; Zhan, Wei; Ehsani, Mehrdad

2018-06-01

Extensive research effort has been made in fault diagnosis of motors and related components such as winding and ball bearing. In this paper, a new concept of inter-turn short circuit fault for DC brushed motors is proposed to include the short circuit ratio and short circuit resistance. A first-principle model is derived for motors with inter-turn short circuit fault. A statistical model based on Hidden Markov Model is developed for fault diagnosis purpose. This new method not only allows detection of motor winding short circuit fault, it can also provide estimation of the fault severity, as indicated by estimation of the short circuit ratio and the short circuit resistance. The estimated fault severity can be used for making appropriate decisions in response to the fault condition. The feasibility of the proposed methodology is studied for inter-turn short circuit of DC brushed motors using simulation in MATLAB/Simulink environment. In addition, it is shown that the proposed methodology is reliable with the presence of small random noise in the system parameters and measurement. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Big Data Analysis of Manufacturing Processes

NASA Astrophysics Data System (ADS)

Windmann, Stefan; Maier, Alexander; Niggemann, Oliver; Frey, Christian; Bernardi, Ansgar; Gu, Ying; Pfrommer, Holger; Steckel, Thilo; Krüger, Michael; Kraus, Robert

2015-11-01

The high complexity of manufacturing processes and the continuously growing amount of data lead to excessive demands on the users with respect to process monitoring, data analysis and fault detection. For these reasons, problems and faults are often detected too late, maintenance intervals are chosen too short and optimization potential for higher output and increased energy efficiency is not sufficiently used. A possibility to cope with these challenges is the development of self-learning assistance systems, which identify relevant relationships by observation of complex manufacturing processes so that failures, anomalies and need for optimization are automatically detected. The assistance system developed in the present work accomplishes data acquisition, process monitoring and anomaly detection in industrial and agricultural processes. The assistance system is evaluated in three application cases: Large distillation columns, agricultural harvesting processes and large-scale sorting plants. In this paper, the developed infrastructures for data acquisition in these application cases are described as well as the developed algorithms and initial evaluation results.

A rapid calculation system for tsunami propagation in Japan by using the AQUA-MT/CMT solutions

NASA Astrophysics Data System (ADS)

Nakamura, T.; Suzuki, W.; Yamamoto, N.; Kimura, H.; Takahashi, N.

2017-12-01

We developed a rapid calculation system of geodetic deformations and tsunami propagation in and around Japan. The system automatically conducts their forward calculations by using point source parameters estimated by the AQUA system (Matsumura et al., 2006), which analyze magnitude, hypocenter, and moment tensors for an event occurring in Japan in 3 minutes of the origin time at the earliest. An optimized calculation code developed by Nakamura and Baba (2016) is employed for the calculations on our computer server with 12 core processors of Intel Xeon 2.60 GHz. Assuming a homogeneous fault slip in the single fault plane as the source fault, the developed system calculates each geodetic deformation and tsunami propagation by numerically solving the 2D linear long-wave equations for the grid interval of 1 arc-min from two fault orientations simultaneously; i.e., one fault and its conjugate fault plane. Because fault models based on moment tensor analyses of event data are used, the system appropriately evaluate tsunami propagation even for unexpected events such as normal faulting in the subduction zone, which differs with the evaluation of tsunami arrivals and heights from a pre-calculated database by using fault models assuming typical types of faulting in anticipated source areas (e.g., Tatehata, 1998; Titov et al., 2005; Yamamoto et al., 2016). By the complete automation from event detection to output graphical figures, the calculation results can be available via e-mail and web site in 4 minutes of the origin time at the earliest. For moderate-sized events such as M5 to 6 events, the system helps us to rapidly investigate whether amplitudes of tsunamis at nearshore and offshore stations exceed a noise level or not, and easily identify actual tsunamis at the stations by comparing with obtained synthetic waveforms. In the case of using source models investigated from GNSS data, such evaluations may be difficult because of the low resolution of sources due to a low signal to noise ratio at land stations. For large to huge events in offshore areas, the developed system may be useful to decide to starting or stopping preparations and precautions against tsunami arrivals, because calculation results including arrival times and heights of initial and maximum waves can be rapidly available before their arrivals at coastal areas.

The Effects of Fault Bends on Rupture Propagation: A Parameter Study

NASA Astrophysics Data System (ADS)

Lozos, J. C.; Oglesby, D. D.; Duan, B.; Wesnousky, S. G.

2008-12-01

Segmented faults with stepovers are ubiquitous, and occur at a variety of scales, ranging from small stepovers on the San Jacinto Fault, to the large-scale stepover on of the San Andreas Fault between Tejon Pass and San Gorgonio Pass. Because this type of fault geometry is so prevalent, understanding how rupture propagates through such systems is important for evaluating seismic hazard at different points along these faults. In the present study, we systematically investigate how far rupture will propagate through a fault with a linked (i.e., continuous fault) stepover, based on the length of the linking fault segment and the angle that connects the linking segment to adjacent segments. We conducted dynamic models of such systems using a two-dimensional finite element code (Duan and Oglesby 2007). The fault system in our models consists of three segments: two parallel 10km-long faults linked at a specified angle by a linking segment of between 500 m and 5 km. This geometry was run both as a extensional system and a compressional system. We observed several distinct rupture behaviors, with systematic differences between compressional and extensional cases. Both shear directions rupture straight through the stepover for very shallow stepover angles. In compressional systems with steeper angles, rupture may jump ahead from the stepover segment onto the far segment; whether or not rupture on this segment reaches critical patch size and slips fully is also a function of angle and stepover length. In some compressional cases, if the angle is steep enough and the stepover short enough, rupture may jump over the step entirely and propagate down the far segment without touching the linking segment. In extensional systems, rupture jumps from the nucleating segment onto the linking segment even at shallow angles, but at steeper angles, rupture propagates through without jumping. It is easier to propagate through a wider range of angles in extensional cases. In both extensional and compressional cases, for each stepover length there exists a maximum angle through which rupture can fully propagate; this maximum angle decreases asymptotically to a minimum value as the stepover length increases. We also found that a wave associated with a stopping phase coming from the far end of the fault may restart rupture and induce full propagation after a significant delay in some cases where the initial rupture terminated.

Ares I-X Ground Diagnostic Prototype

NASA Technical Reports Server (NTRS)

Schwabacher, Mark; Martin, Rodney; Waterman, Robert; Oostdyk, Rebecca; Ossenfort, John; Matthews, Bryan

2010-01-01

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. The full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes.

FAULT PROPAGATION AND EFFECTS ANALYSIS FOR DESIGNING AN ONLINE MONITORING SYSTEM FOR THE SECONDARY LOOP OF A NUCLEAR POWER PLANT PART OF A HYBRID ENERGY SYSTEM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Huijuan; Diao, Xiaoxu; Li, Boyuan

This paper studies the propagation and effects of faults of critical components that pertain to the secondary loop of a nuclear power plant found in Nuclear Hybrid Energy Systems (NHES). This information is used to design an on-line monitoring (OLM) system which is capable of detecting and forecasting faults that are likely to occur during NHES operation. In this research, the causes, features, and effects of possible faults are investigated by simulating the propagation of faults in the secondary loop. The simulation is accomplished by using the Integrated System Failure Analysis (ISFA). ISFA is used for analyzing hardware and softwaremore » faults during the conceptual design phase. In this paper, the models of system components required by ISFA are initially constructed. Then, the fault propagation analysis is implemented, which is conducted under the bounds set by acceptance criteria derived from the design of an OLM system. The result of the fault simulation is utilized to build a database for fault detection and diagnosis, provide preventive measures, and propose an optimization plan for the OLM system.« less

Applicability of computer-aided comprehensive tool (LINDA: LINeament Detection and Analysis) and shaded digital elevation model for characterizing and interpreting morphotectonic features from lineaments

NASA Astrophysics Data System (ADS)

Masoud, Alaa; Koike, Katsuaki

2017-09-01

Detection and analysis of linear features related to surface and subsurface structures have been deemed necessary in natural resource exploration and earth surface instability assessment. Subjectivity in choosing control parameters required in conventional methods of lineament detection may cause unreliable results. To reduce this ambiguity, we developed LINDA (LINeament Detection and Analysis), an integrated tool with graphical user interface in Visual Basic. This tool automates processes of detection and analysis of linear features from grid data of topography (digital elevation model; DEM), gravity and magnetic surfaces, as well as data from remote sensing imagery. A simple interface with five display windows forms a user-friendly interactive environment. The interface facilitates grid data shading, detection and grouping of segments, lineament analyses for calculating strike and dip and estimating fault type, and interactive viewing of lineament geometry. Density maps of the center and intersection points of linear features (segments and lineaments) are also included. A systematic analysis of test DEMs and Landsat 7 ETM+ imagery datasets in the North and South Eastern Deserts of Egypt is implemented to demonstrate the capability of LINDA and correct use of its functions. Linear features from the DEM are superior to those from the imagery in terms of frequency, but both linear features agree with location and direction of V-shaped valleys and dykes and reference fault data. Through the case studies, LINDA applicability is demonstrated to highlight dominant structural trends, which can aid understanding of geodynamic frameworks in any region.

Modeling and characterization of partially inserted electrical connector faults

NASA Astrophysics Data System (ADS)

Tokgöz, ćaǧatay; Dardona, Sameh; Soldner, Nicholas C.; Wheeler, Kevin R.

2016-03-01

Faults within electrical connectors are prominent in avionics systems due to improper installation, corrosion, aging, and strained harnesses. These faults usually start off as undetectable with existing inspection techniques and increase in magnitude during the component lifetime. Detection and modeling of these faults are significantly more challenging than hard failures such as open and short circuits. Hence, enabling the capability to locate and characterize the precursors of these faults is critical for timely preventive maintenance and mitigation well before hard failures occur. In this paper, an electrical connector model based on a two-level nonlinear least squares approach is proposed. The connector is first characterized as a transmission line, broken into key components such as the pin, socket, and connector halves. Then, the fact that the resonance frequencies of the connector shift as insertion depth changes from a fully inserted to a barely touching contact is exploited. The model precisely captures these shifts by varying only two length parameters. It is demonstrated that the model accurately characterizes a partially inserted connector.

Analytical and experimental vibration analysis of a faulty gear system

NASA Astrophysics Data System (ADS)

Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.

1994-10-01

A comprehensive analytical procedure was developed for predicting faults in gear transmission systems under normal operating conditions. A gear tooth fault model is developed to simulate the effects of pitting and wear on the vibration signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth faults. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structures. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville Distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other fault detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental vibration data of the damaged test gear and the predicted vibration from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.

Analytical and experimental vibration analysis of a faulty gear system

NASA Astrophysics Data System (ADS)

Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.

1994-10-01

A comprehensive analytical procedure was developed for predicting faults in gear transmission systems under normal operating conditions. A gear tooth fault model is developed to simulate the effects of pitting and wear on the vibration signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth faults. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structure. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other fault detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental vibration data of the damaged test gear and the predicted vibration from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.

Analytical and Experimental Vibration Analysis of a Faulty Gear System

NASA Technical Reports Server (NTRS)

Choy, F. K.; Braun, M. J.; Polyshchuk, V.; Zakrajsek, J. J.; Townsend, D. P.; Handschuh, R. F.

1994-01-01

A comprehensive analytical procedure was developed for predicting faults in gear transmission systems under normal operating conditions. A gear tooth fault model is developed to simulate the effects of pitting and wear on the vibration signal under normal operating conditions. The model uses changes in the gear mesh stiffness to simulate the effects of gear tooth faults. The overall dynamics of the gear transmission system is evaluated by coupling the dynamics of each individual gear-rotor system through gear mesh forces generated between each gear-rotor system and the bearing forces generated between the rotor and the gearbox structure. The predicted results were compared with experimental results obtained from a spiral bevel gear fatigue test rig at NASA Lewis Research Center. The Wigner-Ville distribution (WVD) was used to give a comprehensive comparison of the predicted and experimental results. The WVD method applied to the experimental results were also compared to other fault detection techniques to verify the WVD's ability to detect the pitting damage, and to determine its relative performance. Overall results show good correlation between the experimental vibration data of the damaged test gear and the predicted vibration from the model with simulated gear tooth pitting damage. Results also verified that the WVD method can successfully detect and locate gear tooth wear and pitting damage.

Source characterization and dynamic fault modeling of induced seismicity

NASA Astrophysics Data System (ADS)

Lui, S. K. Y.; Young, R. P.

2017-12-01

In recent years there are increasing concerns worldwide that industrial activities in the sub-surface can cause or trigger damaging earthquakes. In order to effectively mitigate the damaging effects of induced seismicity, the key is to better understand the source physics of induced earthquakes, which still remain elusive at present. Furthermore, an improved understanding of induced earthquake physics is pivotal to assess large-magnitude earthquake triggering. A better quantification of the possible causes of induced earthquakes can be achieved through numerical simulations. The fault model used in this study is governed by the empirically-derived rate-and-state friction laws, featuring a velocity-weakening (VW) patch embedded into a large velocity-strengthening (VS) region. Outside of that, the fault is slipping at the background loading rate. The model is fully dynamic, with all wave effects resolved, and is able to resolve spontaneous long-term slip history on a fault segment at all stages of seismic cycles. An earlier study using this model has established that aseismic slip plays a major role in the triggering of small repeating earthquakes. This study presents a series of cases with earthquakes occurring on faults with different fault frictional properties and fluid-induced stress perturbations. The effects to both the overall seismicity rate and fault slip behavior are investigated, and the causal relationship between the pre-slip pattern prior to the event and the induced source characteristics is discussed. Based on simulation results, the subsequent step is to select specific cases for laboratory experiments which allow well controlled variables and fault parameters. Ultimately, the aim is to provide better constraints on important parameters for induced earthquakes based on numerical modeling and laboratory data, and hence to contribute to a physics-based induced earthquake hazard assessment.

Fault Detection of Rotating Machinery using the Spectral Distribution Function

NASA Technical Reports Server (NTRS)

Davis, Sanford S.

1997-01-01

The spectral distribution function is introduced to characterize the process leading to faults in rotating machinery. It is shown to be a more robust indicator than conventional power spectral density estimates, but requires only slightly more computational effort. The method is illustrated with examples from seeded gearbox transmission faults and an analytical model of a defective bearing. Procedures are suggested for implementation in realistic environments.

Crustal Density Variation Along the San Andreas Fault Controls Its Secondary Faults Distribution and Dip Direction

NASA Astrophysics Data System (ADS)

Yang, H.; Moresi, L. N.

2017-12-01

The San Andreas fault forms a dominant component of the transform boundary between the Pacific and the North American plate. The density and strength of the complex accretionary margin is very heterogeneous. Based on the density structure of the lithosphere in the SW United States, we utilize the 3D finite element thermomechanical, viscoplastic model (Underworld2) to simulate deformation in the San Andreas Fault system. The purpose of the model is to examine the role of a big bend in the existing geometry. In particular, the big bend of the fault is an initial condition of in our model. We first test the strength of the fault by comparing the surface principle stresses from our numerical model with the in situ tectonic stress. The best fit model indicates the model with extremely weak fault (friction coefficient < 0.1) is requisite. To the first order, there is significant density difference between the Great Valley and the adjacent Mojave block. The Great Valley block is much colder and of larger density (>200 kg/m3) than surrounding blocks. In contrast, the Mojave block is detected to find that it has lost its mafic lower crust by other geophysical surveys. Our model indicates strong strain localization at the jointer boundary between two blocks, which is an analogue for the Garlock fault. High density lower crust material of the Great Valley tends to under-thrust beneath the Transverse Range near the big bend. This motion is likely to rotate the fault plane from the initial vertical direction to dip to the southwest. For the straight section, north to the big bend, the fault is nearly vertical. The geometry of the fault plane is consistent with field observations.

Learning in the model space for cognitive fault diagnosis.

PubMed

Chen, Huanhuan; Tino, Peter; Rodan, Ali; Yao, Xin

2014-01-01

The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.

Prediction of the area affected by earthquake-induced landsliding based on seismological parameters

NASA Astrophysics Data System (ADS)

Marc, Odin; Meunier, Patrick; Hovius, Niels

2017-07-01

We present an analytical, seismologically consistent expression for the surface area of the region within which most landslides triggered by an earthquake are located (landslide distribution area). This expression is based on scaling laws relating seismic moment, source depth, and focal mechanism with ground shaking and fault rupture length and assumes a globally constant threshold of acceleration for onset of systematic mass wasting. The seismological assumptions are identical to those recently used to propose a seismologically consistent expression for the total volume and area of landslides triggered by an earthquake. To test the accuracy of the model we gathered geophysical information and estimates of the landslide distribution area for 83 earthquakes. To reduce uncertainties and inconsistencies in the estimation of the landslide distribution area, we propose an objective definition based on the shortest distance from the seismic wave emission line containing 95 % of the total landslide area. Without any empirical calibration the model explains 56 % of the variance in our dataset, and predicts 35 to 49 out of 83 cases within a factor of 2, depending on how we account for uncertainties on the seismic source depth. For most cases with comprehensive landslide inventories we show that our prediction compares well with the smallest region around the fault containing 95 % of the total landslide area. Aspects ignored by the model that could explain the residuals include local variations of the threshold of acceleration and processes modulating the surface ground shaking, such as the distribution of seismic energy release on the fault plane, the dynamic stress drop, and rupture directivity. Nevertheless, its simplicity and first-order accuracy suggest that the model can yield plausible and useful estimates of the landslide distribution area in near-real time, with earthquake parameters issued by standard detection routines.

Advanced Diagnostic System on Earth Observing One

NASA Technical Reports Server (NTRS)

Hayden, Sandra C.; Sweet, Adam J.; Christa, Scott E.; Tran, Daniel; Shulman, Seth

2004-01-01

In this infusion experiment, the Livingstone 2 (L2) model-based diagnosis engine, developed by the Computational Sciences division at NASA Ames Research Center, has been uploaded to the Earth Observing One (EO-1) satellite. L2 is integrated with the Autonomous Sciencecraft Experiment (ASE) which provides an on-board planning capability and a software bridge to the spacecraft's 1773 data bus. Using a model of the spacecraft subsystems, L2 predicts nominal state transitions initiated by control commands, monitors the spacecraft sensors, and, in the case of failure, isolates the fault based on the discrepant observations. Fault detection and isolation is done by determining a set of component modes, including most likely failures, which satisfy the current observations. All mode transitions and diagnoses are telemetered to the ground for analysis. The initial L2 model is scoped to EO-1's imaging instruments and solid state recorder. Diagnostic scenarios for EO-1's nominal imaging timeline are demonstrated by injecting simulated faults on-board the spacecraft. The solid state recorder stores the science images and also hosts: the experiment software. The main objective of the experiment is to mature the L2 technology to Technology Readiness Level (TRL) 7. Experiment results are presented, as well as a discussion of the challenging technical issues encountered. Future extensions may explore coordination with the planner, and model-based ground operations.

Accuracy of lineaments mapping from space

NASA Technical Reports Server (NTRS)

Short, Nicholas M.

1989-01-01

The use of Landsat and other space imaging systems for lineaments detection is analyzed in terms of their effectiveness in recognizing and mapping fractures and faults, and the results of several studies providing a quantitative assessment of lineaments mapping accuracies are discussed. The cases under investigation include a Landsat image of the surface overlying a part of the Anadarko Basin of Oklahoma, the Landsat images and selected radar imagery of major lineaments systems distributed over much of Canadian Shield, and space imagery covering a part of the East African Rift in Kenya. It is demonstrated that space imagery can detect a significant portion of a region's fracture pattern, however, significant fractions of faults and fractures recorded on a field-produced geological map are missing from the imagery as it is evident in the Kenya case.

Functional Fault Modeling Conventions and Practices for Real-Time Fault Isolation

NASA Technical Reports Server (NTRS)

Ferrell, Bob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Brown, Barbara

2010-01-01

The purpose of this paper is to present the conventions, best practices, and processes that were established based on the prototype development of a Functional Fault Model (FFM) for a Cryogenic System that would be used for real-time Fault Isolation in a Fault Detection, Isolation, and Recovery (FDIR) system. The FDIR system is envisioned to perform health management functions for both a launch vehicle and the ground systems that support the vehicle during checkout and launch countdown by using a suite of complimentary software tools that alert operators to anomalies and failures in real-time. The FFMs were created offline but would eventually be used by a real-time reasoner to isolate faults in a Cryogenic System. Through their development and review, a set of modeling conventions and best practices were established. The prototype FFM development also provided a pathfinder for future FFM development processes. This paper documents the rationale and considerations for robust FFMs that can easily be transitioned to a real-time operating environment.

Testability analysis on a hydraulic system in a certain equipment based on simulation model

NASA Astrophysics Data System (ADS)

Zhang, Rui; Cong, Hua; Liu, Yuanhong; Feng, Fuzhou

2018-03-01

Aiming at the problem that the complicated structure and the shortage of fault statistics information in hydraulic systems, a multi value testability analysis method based on simulation model is proposed. Based on the simulation model of AMESim, this method injects the simulated faults and records variation of test parameters ,such as pressure, flow rate, at each test point compared with those under normal conditions .Thus a multi-value fault-test dependency matrix is established. Then the fault detection rate (FDR) and fault isolation rate (FIR) are calculated based on the dependency matrix. Finally the system of testability and fault diagnosis capability are analyzed and evaluated, which can only reach a lower 54%(FDR) and 23%(FIR). In order to improve testability performance of the system,. number and position of the test points are optimized on the system. Results show the proposed test placement scheme can be used to solve the problems that difficulty, inefficiency and high cost in the system maintenance.

Bond graph modeling and experimental verification of a novel scheme for fault diagnosis of rolling element bearings in special operating conditions

NASA Astrophysics Data System (ADS)

Mishra, C.; Samantaray, A. K.; Chakraborty, G.

2016-09-01

Vibration analysis for diagnosis of faults in rolling element bearings is complicated when the rotor speed is variable or slow. In the former case, the time interval between the fault-induced impact responses in the vibration signal are non-uniform and the signal strength is variable. In the latter case, the fault-induced impact response strength is weak and generally gets buried in the noise, i.e. noise dominates the signal. This article proposes a diagnosis scheme based on a combination of a few signal processing techniques. The proposed scheme initially represents the vibration signal in terms of uniformly resampled angular position of the rotor shaft by using the interpolated instantaneous angular position measurements. Thereafter, intrinsic mode functions (IMFs) are generated through empirical mode decomposition (EMD) of resampled vibration signal which is followed by thresholding of IMFs and signal reconstruction to de-noise the signal and envelope order tracking to diagnose the faults. Data for validating the proposed diagnosis scheme are initially generated from a multi-body simulation model of rolling element bearing which is developed using bond graph approach. This bond graph model includes the ball and cage dynamics, localized fault geometry, contact mechanics, rotor unbalance, and friction and slip effects. The diagnosis scheme is finally validated with experiments performed with the help of a machine fault simulator (MFS) system. Some fault scenarios which could not be experimentally recreated are then generated through simulations and analyzed through the developed diagnosis scheme.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter D.; Dawson, Andrew

2017-03-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelization to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. In this paper, we present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform model simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13 % for the shallow water model.

Application of fault factor method to fault detection and diagnosis for space shuttle main engine

NASA Astrophysics Data System (ADS)

Cha, Jihyoung; Ha, Chulsu; Ko, Sangho; Koo, Jaye

2016-09-01

This paper deals with an application of the multiple linear regression algorithm to fault detection and diagnosis for the space shuttle main engine (SSME) during a steady state. In order to develop the algorithm, the energy balance equations, which balances the relation among pressure, mass flow rate and power at various locations within the SSME, are obtained. Then using the measurement data of some important parameters of the engine, fault factors which reflects the deviation of each equation from the normal state are estimated. The probable location of each fault and the levels of severity can be obtained from the estimated fault factors. This process is numerically demonstrated for the SSME at 104% Rated Propulsion Level (RPL) by using the simulated measurement data from the mathematical models of the engine. The result of the current study is particularly important considering that the recently developed reusable Liquid Rocket Engines (LREs) have staged-combustion cycles similarly to the SSME.

Real-time diagnostics of the reusable rocket engine using on-line system identification

NASA Technical Reports Server (NTRS)

Guo, T.-H.; Merrill, W.; Duyar, A.

1990-01-01

A model-based failure diagnosis system has been proposed for real-time diagnosis of SSME failures. Actuation, sensor, and system degradation failure modes are all considered by the proposed system. In the case of SSME actuation failures, it was shown that real-time identification can effectively be used for failure diagnosis purposes. It is a direct approach since it reduces the detection, isolation, and the estimation of the extent of the failures to the comparison of parameter values before and after the failure. As with any model-based failure detection system, the proposed approach requires a fault model that embodies the essential characteristics of the failure process. The proposed diagnosis approach has the added advantage that it can be used as part of an intelligent control system for failure accommodation purposes.

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems.

PubMed

Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun; Wang, Gi-Nam

2016-01-01

Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

PubMed Central

Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun

2016-01-01

Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively. PMID:27974882

A probabilistic method to diagnose faults of air handling units

NASA Astrophysics Data System (ADS)

Dey, Debashis

Air handling unit (AHU) is one of the most extensively used equipment in large commercial buildings. This device is typically customized and lacks quality system integration which can result in hardwire failures and controller errors. Air handling unit Performance Assessment Rules (APAR) is a fault detection tool that uses a set of expert rules derived from mass and energy balances to detect faults in air handling units. APAR is computationally simple enough that it can be embedded in commercial building automation and control systems and relies only upon sensor data and control signals that are commonly available in these systems. Although APAR has many advantages over other methods, for example no training data required and easy to implement commercially, most of the time it is unable to provide the diagnosis of the faults. For instance, a fault on temperature sensor could be fixed bias, drifting bias, inappropriate location, complete failure. Also a fault in mixing box can be return and outdoor damper leak or stuck. In addition, when multiple rules are satisfied the list of faults increases. There is no proper way to have the correct diagnosis for rule based fault detection system. To overcome this limitation we proposed Bayesian Belief Network (BBN) as a diagnostic tool. BBN can be used to simulate diagnostic thinking of FDD experts through a probabilistic way. In this study we developed a new way to detect and diagnose faults in AHU through combining APAR rules and Bayesian Belief network. Bayesian Belief Network is used as a decision support tool for rule based expert system. BBN is highly capable to prioritize faults when multiple rules are satisfied simultaneously. Also it can get information from previous AHU operating conditions and maintenance records to provide proper diagnosis. The proposed model is validated with real time measured data of a campus building at University of Texas at San Antonio (UTSA).The results show that BBN is correctly able to prioritize faults which can be verified by manual investigation.

Test pattern generation for ILA sequential circuits

NASA Technical Reports Server (NTRS)

Feng, YU; Frenzel, James F.; Maki, Gary K.

1993-01-01

An efficient method of generating test patterns for sequential machines implemented using one-dimensional, unilateral, iterative logic arrays (ILA's) of BTS pass transistor networks is presented. Based on a transistor level fault model, the method affords a unique opportunity for real-time fault detection with improved fault coverage. The resulting test sets are shown to be equivalent to those obtained using conventional gate level models, thus eliminating the need for additional test patterns. The proposed method advances the simplicity and ease of the test pattern generation for a special class of sequential circuitry.

Fault-tolerant building-block computer study

NASA Technical Reports Server (NTRS)

Rennels, D. A.

1978-01-01

Ultra-reliable core computers are required for improving the reliability of complex military systems. Such computers can provide reliable fault diagnosis, failure circumvention, and, in some cases serve as an automated repairman for their host systems. A small set of building-block circuits which can be implemented as single very large integration devices, and which can be used with off-the-shelf microprocessors and memories to build self checking computer modules (SCCM) is described. Each SCCM is a microcomputer which is capable of detecting its own faults during normal operation and is described to communicate with other identical modules over one or more Mil Standard 1553A buses. Several SCCMs can be connected into a network with backup spares to provide fault-tolerant operation, i.e. automated recovery from faults. Alternative fault-tolerant SCCM configurations are discussed along with the cost and reliability associated with their implementation.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter; Dawson, Andrew

2017-04-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelisation to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. We present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13% for the shallow water model.

Parameter Transient Behavior Analysis on Fault Tolerant Control System

NASA Technical Reports Server (NTRS)

Belcastro, Christine (Technical Monitor); Shin, Jong-Yeob

2003-01-01

In a fault tolerant control (FTC) system, a parameter varying FTC law is reconfigured based on fault parameters estimated by fault detection and isolation (FDI) modules. FDI modules require some time to detect fault occurrences in aero-vehicle dynamics. This paper illustrates analysis of a FTC system based on estimated fault parameter transient behavior which may include false fault detections during a short time interval. Using Lyapunov function analysis, the upper bound of an induced-L2 norm of the FTC system performance is calculated as a function of a fault detection time and the exponential decay rate of the Lyapunov function.

Experimental evaluation of the certification-trail method

NASA Technical Reports Server (NTRS)

Sullivan, Gregory F.; Wilson, Dwight S.; Masson, Gerald M.; Itoh, Mamoru; Smith, Warren W.; Kay, Jonathan S.

1993-01-01

Certification trails are a recently introduced and promising approach to fault-detection and fault-tolerance. A comprehensive attempt to assess experimentally the performance and overall value of the method is reported. The method is applied to algorithms for the following problems: huffman tree, shortest path, minimum spanning tree, sorting, and convex hull. Our results reveal many cases in which an approach using certification-trails allows for significantly faster overall program execution time than a basic time redundancy-approach. Algorithms for the answer-validation problem for abstract data types were also examined. This kind of problem provides a basis for applying the certification-trail method to wide classes of algorithms. Answer-validation solutions for two types of priority queues were implemented and analyzed. In both cases, the algorithm which performs answer-validation is substantially faster than the original algorithm for computing the answer. Next, a probabilistic model and analysis which enables comparison between the certification-trail method and the time-redundancy approach were presented. The analysis reveals some substantial and sometimes surprising advantages for ther certification-trail method. Finally, the work our group performed on the design and implementation of fault injection testbeds for experimental analysis of the certification trail technique is discussed. This work employs two distinct methodologies, software fault injection (modification of instruction, data, and stack segments of programs on a Sun Sparcstation ELC and on an IBM 386 PC) and hardware fault injection (control, address, and data lines of a Motorola MC68000-based target system pulsed at logical zero/one values). Our results indicate the viability of the certification trail technique. It is also believed that the tools developed provide a solid base for additional exploration.

Testing fault growth models with low-temperature thermochronology in the northwest Basin and Range, USA

USGS Publications Warehouse

Curry, Magdalena A. E.; Barnes, Jason B.; Colgan, Joseph P.

2016-01-01

Common fault growth models diverge in predicting how faults accumulate displacement and lengthen through time. A paucity of field-based data documenting the lateral component of fault growth hinders our ability to test these models and fully understand how natural fault systems evolve. Here we outline a framework for using apatite (U-Th)/He thermochronology (AHe) to quantify the along-strike growth of faults. To test our framework, we first use a transect in the normal fault-bounded Jackson Mountains in the Nevada Basin and Range Province, then apply the new framework to the adjacent Pine Forest Range. We combine new and existing cross sections with 18 new and 16 existing AHe cooling ages to determine the spatiotemporal variability in footwall exhumation and evaluate models for fault growth. Three age-elevation transects in the Pine Forest Range show that rapid exhumation began along the range-front fault between approximately 15 and 11 Ma at rates of 0.2–0.4 km/Myr, ultimately exhuming approximately 1.5–5 km. The ages of rapid exhumation identified at each transect lie within data uncertainty, indicating concomitant onset of faulting along strike. We show that even in the case of growth by fault-segment linkage, the fault would achieve its modern length within 3–4 Myr of onset. Comparison with the Jackson Mountains highlights the inadequacies of spatially limited sampling. A constant fault-length growth model is the best explanation for our thermochronology results. We advocate that low-temperature thermochronology can be further utilized to better understand and quantify fault growth with broader implications for seismic hazard assessments and the coevolution of faulting and topography.

The impact of splay faults on fluid flow, solute transport, and pore pressure distribution in subduction zones: A case study offshore the Nicoya Peninsula, Costa Rica

NASA Astrophysics Data System (ADS)

Lauer, Rachel M.; Saffer, Demian M.

2015-04-01

Observations of seafloor seeps on the continental slope of many subduction zones illustrate that splay faults represent a primary hydraulic connection to the plate boundary at depth, carry deeply sourced fluids to the seafloor, and are in some cases associated with mud volcanoes. However, the role of these structures in forearc hydrogeology remains poorly quantified. We use a 2-D numerical model that simulates coupled fluid flow and solute transport driven by fluid sources from tectonically driven compaction and smectite transformation to investigate the effects of permeable splay faults on solute transport and pore pressure distribution. We focus on the Nicoya margin of Costa Rica as a case study, where previous modeling and field studies constrain flow rates, thermal structure, and margin geology. In our simulations, splay faults accommodate up to 33% of the total dewatering flux, primarily along faults that outcrop within 25 km of the trench. The distribution and fate of dehydration-derived fluids is strongly dependent on thermal structure, which determines the locus of smectite transformation. In simulations of a cold end-member margin, smectite transformation initiates 30 km from the trench, and 64% of the dehydration-derived fluids are intercepted by splay faults and carried to the middle and upper slope, rather than exiting at the trench. For a warm end-member, smectite transformation initiates 7 km from the trench, and the associated fluids are primarily transmitted to the trench via the décollement (50%), and faults intercept only 21% of these fluids. For a wide range of splay fault permeabilities, simulated fluid pressures are near lithostatic where the faults intersect overlying slope sediments, providing a viable mechanism for the formation of mud volcanoes.

Care 3 phase 2 report, maintenance manual

NASA Technical Reports Server (NTRS)

Bryant, L. A.; Stiffler, J. J.

1982-01-01

CARE 3 (Computer-Aided Reliability Estimation, version three) is a computer program designed to help estimate the reliability of complex, redundant systems. Although the program can model a wide variety of redundant structures, it was developed specifically for fault-tolerant avionics systems--systems distinguished by the need for extremely reliable performance since a system failure could well result in the loss of human life. It substantially generalizes the class of redundant configurations that could be accommodated, and includes a coverage model to determine the various coverage probabilities as a function of the applicable fault recovery mechanisms (detection delay, diagnostic scheduling interval, isolation and recovery delay, etc.). CARE 3 further generalizes the class of system structures that can be modeled and greatly expands the coverage model to take into account such effects as intermittent and transient faults, latent faults, error propagation, etc.

Stability of faults with heterogeneous friction properties and effective normal stress

NASA Astrophysics Data System (ADS)

Luo, Yingdi; Ampuero, Jean-Paul

2018-05-01

Abundant geological, seismological and experimental evidence of the heterogeneous structure of natural faults motivates the theoretical and computational study of the mechanical behavior of heterogeneous frictional fault interfaces. Fault zones are composed of a mixture of materials with contrasting strength, which may affect the spatial variability of seismic coupling, the location of high-frequency radiation and the diversity of slip behavior observed in natural faults. To develop a quantitative understanding of the effect of strength heterogeneity on the mechanical behavior of faults, here we investigate a fault model with spatially variable frictional properties and pore pressure. Conceptually, this model may correspond to two rough surfaces in contact along discrete asperities, the space in between being filled by compressed gouge. The asperities have different permeability than the gouge matrix and may be hydraulically sealed, resulting in different pore pressure. We consider faults governed by rate-and-state friction, with mixtures of velocity-weakening and velocity-strengthening materials and contrasts of effective normal stress. We systematically study the diversity of slip behaviors generated by this model through multi-cycle simulations and linear stability analysis. The fault can be either stable without spontaneous slip transients, or unstable with spontaneous rupture. When the fault is unstable, slip can rupture either part or the entire fault. In some cases the fault alternates between these behaviors throughout multiple cycles. We determine how the fault behavior is controlled by the proportion of velocity-weakening and velocity-strengthening materials, their relative strength and other frictional properties. We also develop, through heuristic approximations, closed-form equations to predict the stability of slip on heterogeneous faults. Our study shows that a fault model with heterogeneous materials and pore pressure contrasts is a viable framework to reproduce the full spectrum of fault behaviors observed in natural faults: from fast earthquakes, to slow transients, to stable sliding. In particular, this model constitutes a building block for models of episodic tremor and slow slip events.

A SVM framework for fault detection of the braking system in a high speed train

NASA Astrophysics Data System (ADS)

Liu, Jie; Li, Yan-Fu; Zio, Enrico

2017-03-01

In April 2015, the number of operating High Speed Trains (HSTs) in the world has reached 3603. An efficient, effective and very reliable braking system is evidently very critical for trains running at a speed around 300 km/h. Failure of a highly reliable braking system is a rare event and, consequently, informative recorded data on fault conditions are scarce. This renders the fault detection problem a classification problem with highly unbalanced data. In this paper, a Support Vector Machine (SVM) framework, including feature selection, feature vector selection, model construction and decision boundary optimization, is proposed for tackling this problem. Feature vector selection can largely reduce the data size and, thus, the computational burden. The constructed model is a modified version of the least square SVM, in which a higher cost is assigned to the error of classification of faulty conditions than the error of classification of normal conditions. The proposed framework is successfully validated on a number of public unbalanced datasets. Then, it is applied for the fault detection of braking systems in HST: in comparison with several SVM approaches for unbalanced datasets, the proposed framework gives better results.

Induction motors airgap-eccentricity detection through the discrete wavelet transform of the apparent power signal under non-stationary operating conditions.

PubMed

Yahia, K; Cardoso, A J M; Ghoggal, A; Zouzou, S E

2014-03-01

Fast Fourier transform (FFT) analysis has been successfully used for fault diagnosis in induction machines. However, this method does not always provide good results for the cases of load torque, speed and voltages variation, leading to a variation of the motor-slip and the consequent FFT problems that appear due to the non-stationary nature of the involved signals. In this paper, the discrete wavelet transform (DWT) of the apparent-power signal for the airgap-eccentricity fault detection in three-phase induction motors is presented in order to overcome the above FFT problems. The proposed method is based on the decomposition of the apparent-power signal from which wavelet approximation and detail coefficients are extracted. The energy evaluation of a known bandwidth permits to define a fault severity factor (FSF). Simulation as well as experimental results are provided to illustrate the effectiveness and accuracy of the proposed method presented even for the case of load torque variations. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Optimal filtering and Bayesian detection for friction-based diagnostics in machines.

PubMed

Ray, L R; Townsend, J R; Ramasubramanian, A

2001-01-01

Non-model-based diagnostic methods typically rely on measured signals that must be empirically related to process behavior or incipient faults. The difficulty in interpreting a signal that is indirectly related to the fundamental process behavior is significant. This paper presents an integrated non-model and model-based approach to detecting when process behavior varies from a proposed model. The method, which is based on nonlinear filtering combined with maximum likelihood hypothesis testing, is applicable to dynamic systems whose constitutive model is well known, and whose process inputs are poorly known. Here, the method is applied to friction estimation and diagnosis during motion control in a rotating machine. A nonlinear observer estimates friction torque in a machine from shaft angular position measurements and the known input voltage to the motor. The resulting friction torque estimate can be analyzed directly for statistical abnormalities, or it can be directly compared to friction torque outputs of an applicable friction process model in order to diagnose faults or model variations. Nonlinear estimation of friction torque provides a variable on which to apply diagnostic methods that is directly related to model variations or faults. The method is evaluated experimentally by its ability to detect normal load variations in a closed-loop controlled motor driven inertia with bearing friction and an artificially-induced external line contact. Results show an ability to detect statistically significant changes in friction characteristics induced by normal load variations over a wide range of underlying friction behaviors.

Exploiting data representation for fault tolerance

DOE PAGES

Hoemmen, Mark Frederick; Elliott, J.; Sandia National Lab.; ...

2015-01-06

Incorrect computer hardware behavior may corrupt intermediate computations in numerical algorithms, possibly resulting in incorrect answers. Prior work models misbehaving hardware by randomly flipping bits in memory. We start by accepting this premise, and present an analytic model for the error introduced by a bit flip in an IEEE 754 floating-point number. We then relate this finding to the linear algebra concepts of normalization and matrix equilibration. In particular, we present a case study illustrating that normalizing both vector inputs of a dot product minimizes the probability of a single bit flip causing a large error in the dot product'smore » result. Moreover, the absolute error is either less than one or very large, which allows detection of large errors. Then, we apply this to the GMRES iterative solver. We count all possible errors that can be introduced through faults in arithmetic in the computationally intensive orthogonalization phase of GMRES, and show that when the matrix is equilibrated, the absolute error is bounded above by one.« less

Technical know-how relevant to planning of borehole investigation for fault characterization

NASA Astrophysics Data System (ADS)

Mizuno, T.; Takeuchi, R.; Tsuruta, T.; Matsuoka, T.; Kunimaru, T.; Saegusa, H.

2011-12-01

As part of the national R&D program for geological disposal of high-level radioactive waste (HLW), the broad scientific study of the deep geological environment, JAEA has established the Mizunami Underground Research Laboratory (MIU) in Central Japan as a generic underground research laboratory (URL) facility. The MIU Project focuses on the crystalline rocks. In the case of fractured rock, a fault is one of the major discontinuity structures which control the groundwater flow conditions. It is important to estimate geological, hydrogeological, hydrochemical and rock mechanical characteristics of faults, and then to evaluate its role in the engineering design of repository and the assessment of long-term safety of HLW disposal. Therefore, investigations for fault characterization have been performed to estimate its characteristics and to evaluate existing conceptual and/or numerical models of the geological environment in the MIU project. Investigations related to faults have been conducted based on the conventional concept that a fault consists of a "fault core (FC)" characterized by distribution of the faulted rocks and a "fractured zone (FZ)" along FC. With the progress of investigations, furthermore, it is clear that there is also a case in which an "altered zone (AZ)" characterized by alteration of host rocks to clay minerals can be developed around the FC. Intensity of alteration in AZ generally decreases with distance from the FC, and AZ transits to FZ. Therefore, the investigation program focusing on properties of AZ is required for revising the existing conceptual and/or numerical models of geological environment. In this study, procedures for planning of fault characterizations have been summarized based on the technical know-how learnt through the MIU Project for the development of Knowledge Management System performed by JAEA under a contract with the Ministry of Economy, Trade and Industry as part of its R&D supporting program for developing geological disposal technology in 2010. Taking into account the experience from the fault characterization in the MIU Project, an optimization procedure for investigation program is summarized as follows; 1) Definition of investigation aim, 2) Confirmation of current understanding of the geological environment, 3) Specification and prioritization of the data to be obtained 4) Selection of the methodology for obtaining the data, 5) Specification of sequence of the investigations, and 6) Establishment of drilling and casing program including optional cases and taking into account potential problems. Several geological conceptual models with uncertainty of geological structures were illustrated to define the investigation aim and to confirm the current uncertainties. These models were also available to establish optional cases by predicting the type and location of potential problems. The procedures and case study related to establishment of the investigation program are summarized in this study and can be available for site characterization works conducted by the implementing body (NUMO) in future candidate areas.

Set-membership fault detection under noisy environment with application to the detection of abnormal aircraft control surface positions

NASA Astrophysics Data System (ADS)

El Houda Thabet, Rihab; Combastel, Christophe; Raïssi, Tarek; Zolghadri, Ali

2015-09-01

The paper develops a set membership detection methodology which is applied to the detection of abnormal positions of aircraft control surfaces. Robust and early detection of such abnormal positions is an important issue for early system reconfiguration and overall optimisation of aircraft design. In order to improve fault sensitivity while ensuring a high level of robustness, the method combines a data-driven characterisation of noise and a model-driven approach based on interval prediction. The efficiency of the proposed methodology is illustrated through simulation results obtained based on data recorded in several flight scenarios of a highly representative aircraft benchmark.

Fast computation of the kurtogram for the detection of transient faults

NASA Astrophysics Data System (ADS)

Antoni, Jérôme

2007-01-01

The kurtogram is a fourth-order spectral analysis tool recently introduced for detecting and characterising non-stationarities in a signal. The paradigm relies on the assertion that each type of transient is associated with an optimal (frequency/frequency resolution) dyad { f,Δf} which maximises its kurtosis, and hence its detection. However, the complete exploration of the whole plane ( f,Δf) is a formidable task hardly amenable to on-line industrial applications. In this communication we describe a fast algorithm for computing the kurtogram over a grid that finely samples the ( f,Δf) plane. Its complexity is on the order of N log N, similarly to the FFT. The efficiency of the algorithm is then illustrated on several industrial cases concerned with the detection of incipient transient faults.

Investigation of advanced fault insertion and simulator methods

NASA Technical Reports Server (NTRS)

Dunn, W. R.; Cottrell, D.

1986-01-01

The cooperative agreement partly supported research leading to the open-literature publication cited. Additional efforts under the agreement included research into fault modeling of semiconductor devices. Results of this research are presented in this report which is summarized in the following paragraphs. As a result of the cited research, it appears that semiconductor failure mechanism data is abundant but of little use in developing pin-level device models. Failure mode data on the other hand does exist but is too sparse to be of any statistical use in developing fault models. What is significant in the failure mode data is that, unlike classical logic, MSI and LSI devices do exhibit more than 'stuck-at' and open/short failure modes. Specifically they are dominated by parametric failures and functional anomalies that can include intermittent faults and multiple-pin failures. The report discusses methods of developing composite pin-level models based on extrapolation of semiconductor device failure mechanisms, failure modes, results of temperature stress testing and functional modeling. Limitations of this model particularly with regard to determination of fault detection coverage and latency time measurement are discussed. Indicated research directions are presented.

Modeling of fault activation and seismicity by injection directly into a fault zone associated with hydraulic fracturing of shale-gas reservoirs

DOE PAGES

Rutqvist, Jonny; Rinaldi, Antonio P.; Cappa, Frédéric; ...

2015-03-01

We conducted three-dimensional coupled fluid-flow and geomechanical modeling of fault activation and seismicity associated with hydraulic fracturing stimulation of a shale-gas reservoir. We simulated a case in which a horizontal injection well intersects a steeply dip- ping fault, with hydraulic fracturing channeled within the fault, during a 3-hour hydraulic fracturing stage. Consistent with field observations, the simulation results show that shale-gas hydraulic fracturing along faults does not likely induce seismic events that could be felt on the ground surface, but rather results in numerous small microseismic events, as well as aseismic deformations along with the fracture propagation. The calculated seismicmore » moment magnitudes ranged from about -2.0 to 0.5, except for one case assuming a very brittle fault with low residual shear strength, for which the magnitude was 2.3, an event that would likely go unnoticed or might be barely felt by humans at its epicenter. The calculated moment magnitudes showed a dependency on injection depth and fault dip. We attribute such dependency to variation in shear stress on the fault plane and associated variation in stress drop upon reactivation. Our simulations showed that at the end of the 3-hour injection, the rupture zone associated with tensile and shear failure extended to a maximum radius of about 200 m from the injection well. The results of this modeling study for steeply dipping faults at 1000 to 2500 m depth is in agreement with earlier studies and field observations showing that it is very unlikely that activation of a fault by shale-gas hydraulic fracturing at great depth (thousands of meters) could cause felt seismicity or create a new flow path (through fault rupture) that could reach shallow groundwater resources.« less

Modeling of fault activation and seismicity by injection directly into a fault zone associated with hydraulic fracturing of shale-gas reservoirs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rutqvist, Jonny; Rinaldi, Antonio P.; Cappa, Frédéric

We conducted three-dimensional coupled fluid-flow and geomechanical modeling of fault activation and seismicity associated with hydraulic fracturing stimulation of a shale-gas reservoir. We simulated a case in which a horizontal injection well intersects a steeply dip- ping fault, with hydraulic fracturing channeled within the fault, during a 3-hour hydraulic fracturing stage. Consistent with field observations, the simulation results show that shale-gas hydraulic fracturing along faults does not likely induce seismic events that could be felt on the ground surface, but rather results in numerous small microseismic events, as well as aseismic deformations along with the fracture propagation. The calculated seismicmore » moment magnitudes ranged from about -2.0 to 0.5, except for one case assuming a very brittle fault with low residual shear strength, for which the magnitude was 2.3, an event that would likely go unnoticed or might be barely felt by humans at its epicenter. The calculated moment magnitudes showed a dependency on injection depth and fault dip. We attribute such dependency to variation in shear stress on the fault plane and associated variation in stress drop upon reactivation. Our simulations showed that at the end of the 3-hour injection, the rupture zone associated with tensile and shear failure extended to a maximum radius of about 200 m from the injection well. The results of this modeling study for steeply dipping faults at 1000 to 2500 m depth is in agreement with earlier studies and field observations showing that it is very unlikely that activation of a fault by shale-gas hydraulic fracturing at great depth (thousands of meters) could cause felt seismicity or create a new flow path (through fault rupture) that could reach shallow groundwater resources.« less

Secular Variation in Slip (Invited)

NASA Astrophysics Data System (ADS)

Cowgill, E.; Gold, R. D.

2010-12-01

Faults show temporal variations in slip rate at time scales ranging from the hours following a major rupture to the millions of years over which plate boundaries reorganize. One such behavior is secular variation in slip (SVS), which we define as a pulse of accelerated strain release along a single fault that occurs at a frequency that is > 1 order of magnitude longer than the recurrence interval of earthquakes within the pulse. Although numerous mechanical models have been proposed to explain SVS, it has proven much harder to measure long (5-500 kyr) records of fault displacement as a function of time. Such fault-slip histories may be obtained from morphochronologic data, which are measurements of offset and age obtained from faulted landforms. Here we describe slip-history modeling of morphochronologic data and show how this method holds promise for obtaining long records of fault slip. In detail we place SVS in the context of other types of time-varying fault-slip phenomena, explain the importance of measuring fault-slip histories, summarize models proposed to explain SVS, review current approaches for measuring SVS in the geologic record, and illustrate the slip-history modeling approach we advocate here using data from the active, left-slip Altyn Tagh fault in NW Tibet. In addition to SVS, other types of temporal variation in fault slip include post-seismic transients, discrepancies between geologic slip rates and those derived from geodetic and/or paleoseismic data, and single changes in slip rate resulting from plate reorganization. Investigating secular variation in slip is important for advancing understanding of long-term continental deformation, fault mechanics, and seismic risk. Mechanical models producing such behavior include self-driven mode switching, changes in pore-fluid pressure, viscoelasticity, postseismic reloading, and changes in local surface loads (e.g., ice sheets, large lakes, etc.) among others. However, a key problem in testing these models is the paucity of long records of fault slip. Paleoseismic data are unlikely to yield such histories because measurements of the slip associated with each event are generally unavailable and long records require large accumulated offsets, which can result in structural duplication or omission of the stratigraphic records of events. In contrast, morphochronologic data capture both the age and offset of individual piercing points, although this approach generally does not resolve individual earthquake events. Because the uncertainties in both age and offset are generally large (5-15%) for individual markers, SVS is best resolved by obtaining suites of such measurements, in which case the errors can be used to reduce the range of slip histories common to all such data points. A suite of such data from the central Altyn Tagh fault reveals a pulse of accelerated strain release in the mid Holocene, with ~20 m of slip being released from ~6.7 to ~5.9 ka at a short-term rate (~28 mm/yr) that is 3 times greater than the average rate (~9 mm/yr). We interpret this pulse to represent a cluster of two to six, Mw > 7.2 earthquakes. To our knowledge, this is the first possible earthquake cluster detected using morphochronologic techniques.

Statistical fault diagnosis of wind turbine drivetrain applied to a 5MW floating wind turbine

NASA Astrophysics Data System (ADS)

Ghane, Mahdi; Nejad, Amir R.; Blanke, Mogens; Gao, Zhen; Moan, Torgeir

2016-09-01

Deployment of large scale wind turbine parks, in particular offshore, requires well organized operation and maintenance strategies to make it as competitive as the classical electric power stations. It is important to ensure systems are safe, profitable, and cost-effective. In this regards, the ability to detect, isolate, estimate, and prognose faults plays an important role. One of the critical wind turbine components is the gearbox. Failures in the gearbox are costly both due to the cost of the gearbox itself and also due to high repair downtime. In order to detect faults as fast as possible to prevent them to develop into failure, statistical change detection is used in this paper. The Cumulative Sum Method (CUSUM) is employed to detect possible defects in the downwind main bearing. A high fidelity gearbox model on a 5-MW spar-type wind turbine is used to generate data for fault-free and faulty conditions of the bearing at the rated wind speed and the associated wave condition. Acceleration measurements are utilized to find residuals used to indirectly detect damages in the bearing. Residuals are found to be nonGaussian, following a t-distribution with multivariable characteristic parameters. The results in this paper show how the diagnostic scheme can detect change with desired false alarm and detection probabilities.

Nonlinear data-driven identification of polymer electrolyte membrane fuel cells for diagnostic purposes: A Volterra series approach

NASA Astrophysics Data System (ADS)

Ritzberger, D.; Jakubek, S.

2017-09-01

In this work, a data-driven identification method, based on polynomial nonlinear autoregressive models with exogenous inputs (NARX) and the Volterra series, is proposed to describe the dynamic and nonlinear voltage and current characteristics of polymer electrolyte membrane fuel cells (PEMFCs). The structure selection and parameter estimation of the NARX model is performed on broad-band voltage/current data. By transforming the time-domain NARX model into a Volterra series representation using the harmonic probing algorithm, a frequency-domain description of the linear and nonlinear dynamics is obtained. With the Volterra kernels corresponding to different operating conditions, information from existing diagnostic tools in the frequency domain such as electrochemical impedance spectroscopy (EIS) and total harmonic distortion analysis (THDA) are effectively combined. Additionally, the time-domain NARX model can be utilized for fault detection by evaluating the difference between measured and simulated output. To increase the fault detectability, an optimization problem is introduced which maximizes this output residual to obtain proper excitation frequencies. As a possible extension it is shown, that by optimizing the periodic signal shape itself that the fault detectability is further increased.

Thermal Expert System (TEXSYS): Systems autonomy demonstration project, volume 2. Results

NASA Technical Reports Server (NTRS)

Glass, B. J. (Editor)

1992-01-01

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS testbed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

Thermal Expert System (TEXSYS): Systems automony demonstration project, volume 1. Overview

NASA Technical Reports Server (NTRS)

Glass, B. J. (Editor)

1992-01-01

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS test bed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

Propulsion Health Monitoring for Enhanced Safety

NASA Technical Reports Server (NTRS)

Butz, Mark G.; Rodriguez, Hector M.

2003-01-01

This report presents the results of the NASA contract Propulsion System Health Management for Enhanced Safety performed by General Electric Aircraft Engines (GE AE), General Electric Global Research (GE GR), and Pennsylvania State University Applied Research Laboratory (PSU ARL) under the NASA Aviation Safety Program. This activity supports the overall goal of enhanced civil aviation safety through a reduction in the occurrence of safety-significant propulsion system malfunctions. Specific objectives are to develop and demonstrate vibration diagnostics techniques for the on-line detection of turbine rotor disk cracks, and model-based fault tolerant control techniques for the prevention and mitigation of in-flight engine shutdown, surge/stall, and flameout events. The disk crack detection work was performed by GE GR which focused on a radial-mode vibration monitoring technique, and PSU ARL which focused on a torsional-mode vibration monitoring technique. GE AE performed the Model-Based Fault Tolerant Control work which focused on the development of analytical techniques for detecting, isolating, and accommodating gas-path faults.

Thermal Expert System (TEXSYS): Systems autonomy demonstration project, volume 2. Results

NASA Astrophysics Data System (ADS)

Glass, B. J.

1992-10-01

The Systems Autonomy Demonstration Project (SADP) produced a knowledge-based real-time control system for control and fault detection, isolation, and recovery (FDIR) of a prototype two-phase Space Station Freedom external active thermal control system (EATCS). The Thermal Expert System (TEXSYS) was demonstrated in recent tests to be capable of reliable fault anticipation and detection, as well as ordinary control of the thermal bus. Performance requirements were addressed by adopting a hierarchical symbolic control approach-layering model-based expert system software on a conventional, numerical data acquisition and control system. The model-based reasoning capabilities of TEXSYS were shown to be advantageous over typical rule-based expert systems, particularly for detection of unforeseen faults and sensor failures. Volume 1 gives a project overview and testing highlights. Volume 2 provides detail on the EATCS testbed, test operations, and online test results. Appendix A is a test archive, while Appendix B is a compendium of design and user manuals for the TEXSYS software.

The Hills are Alive: Dynamic Ridges and Valleys in a Strike-Slip Environment

NASA Astrophysics Data System (ADS)

Duvall, A. R.; Tucker, G. E.

2014-12-01

Strike-slip fault zones have long been known for characteristic landforms such as offset and deflected rivers, linear strike-parallel valleys, and shutter ridges. Despite their common presence, questions remain about the mechanics of how these landforms arise or how their form varies as a function of slip rate, geomorphic process, or material properties. We know even less about what happens far from the fault, in drainage basin headwaters, as a result of strike-slip motion. Here we explore the effects of horizontal fault slip rate, bedrock erodibility, and hillslope diffusivity on river catchments that drain across an active strike-slip fault using the CHILD landscape evolution model. Model calculations demonstrate that lateral fault motion induces a permanent state of landscape disequilibrium brought about by fault offset-generated river lengthening alternating with abrupt shortening due to stream capture. This cycle of shifting drainage patterns and base level change continues until fault motion ceases thus creating a perpetual state of transience unique to strike-slip systems. Our models also make the surprising prediction that, in some cases, hillslope ridges oriented perpendicular to the fault migrate laterally in conjunction with fault motion. Ridge migration happens when slip rate is slow enough and/or diffusion and river incision are fast enough that the hillslopes can respond to the disequilibrium brought about by strike-slip motion. In models with faster slip rates, stronger rocks or less-diffusive hillslopes, ridge mobility is limited or arrested despite the fact that the process of river lengthening and capture continues. Fast-slip cases also develop prominent steep fault-facing hillslope facets proximal to the fault valley and along-strike topographic profiles with reduced local relief between ridges and valleys. Our results demonstrate the dynamic nature of strike-slip landscapes that vary systematically with a ratio of bedrock erodibility (K) and hillslope diffusivity (D) to the rate of horizontal advection of topography (v). These results also reveal a potential set of recognizable geomorphic signatures within strike-slip systems that should be looked to as indicators of fault activity and/or material properties.

How do horizontal, frictional discontinuities affect reverse fault-propagation folding?

NASA Astrophysics Data System (ADS)

Bonanno, Emanuele; Bonini, Lorenzo; Basili, Roberto; Toscani, Giovanni; Seno, Silvio

2017-09-01

The development of new reverse faults and related folds is strongly controlled by the mechanical characteristics of the host rocks. In this study we analyze the impact of a specific kind of anisotropy, i.e. thin mechanical and frictional discontinuities, in affecting the development of reverse faults and of the associated folds using physical scaled models. We perform analog modeling introducing one or two initially horizontal, thin discontinuities above an initially blind fault dipping at 30° in one case, and 45° in another, and then compare the results with those obtained from a fully isotropic model. The experimental results show that the occurrence of thin discontinuities affects both the development and the propagation of new faults and the shape of the associated folds. New faults 1) accelerate or decelerate their propagation depending on the location of the tips with respect to the discontinuities, 2) cross the discontinuities at a characteristic angle (∼90°), and 3) produce folds with different shapes, resulting not only from the dip of the new faults but also from their non-linear propagation history. Our results may have direct impact on future kinematic models, especially those aimed to reconstruct the tectonic history of faults that developed in layered rocks or in regions affected by pre-existing faults.

A recurrent neural-network-based sensor and actuator fault detection and isolation for nonlinear systems with application to the satellite's attitude control subsystem.

PubMed

Talebi, H A; Khorasani, K; Tafazoli, S

2009-01-01

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.

Collective properties of injection-induced earthquake sequences: 1. Model description and directivity bias

NASA Astrophysics Data System (ADS)

Dempsey, David; Suckale, Jenny

2016-05-01

Induced seismicity is of increasing concern for oil and gas, geothermal, and carbon sequestration operations, with several M > 5 events triggered in recent years. Modeling plays an important role in understanding the causes of this seismicity and in constraining seismic hazard. Here we study the collective properties of induced earthquake sequences and the physics underpinning them. In this first paper of a two-part series, we focus on the directivity ratio, which quantifies whether fault rupture is dominated by one (unilateral) or two (bilateral) propagating fronts. In a second paper, we focus on the spatiotemporal and magnitude-frequency distributions of induced seismicity. We develop a model that couples a fracture mechanics description of 1-D fault rupture with fractal stress heterogeneity and the evolving pore pressure distribution around an injection well that triggers earthquakes. The extent of fault rupture is calculated from the equations of motion for two tips of an expanding crack centered at the earthquake hypocenter. Under tectonic loading conditions, our model exhibits a preference for unilateral rupture and a normal distribution of hypocenter locations, two features that are consistent with seismological observations. On the other hand, catalogs of induced events when injection occurs directly onto a fault exhibit a bias toward ruptures that propagate toward the injection well. This bias is due to relatively favorable conditions for rupture that exist within the high-pressure plume. The strength of the directivity bias depends on a number of factors including the style of pressure buildup, the proximity of the fault to failure and event magnitude. For injection off a fault that triggers earthquakes, the modeled directivity bias is small and may be too weak for practical detection. For two hypothetical injection scenarios, we estimate the number of earthquake observations required to detect directivity bias.

Automated Generation of Fault Management Artifacts from a Simple System Model

NASA Technical Reports Server (NTRS)

Kennedy, Andrew K.; Day, John C.

2013-01-01

Our understanding of off-nominal behavior - failure modes and fault propagation - in complex systems is often based purely on engineering intuition; specific cases are assessed in an ad hoc fashion as a (fallible) fault management engineer sees fit. This work is an attempt to provide a more rigorous approach to this understanding and assessment by automating the creation of a fault management artifact, the Failure Modes and Effects Analysis (FMEA) through querying a representation of the system in a SysML model. This work builds off the previous development of an off-nominal behavior model for the upcoming Soil Moisture Active-Passive (SMAP) mission at the Jet Propulsion Laboratory. We further developed the previous system model to more fully incorporate the ideas of State Analysis, and it was restructured in an organizational hierarchy that models the system as layers of control systems while also incorporating the concept of "design authority". We present software that was developed to traverse the elements and relationships in this model to automatically construct an FMEA spreadsheet. We further discuss extending this model to automatically generate other typical fault management artifacts, such as Fault Trees, to efficiently portray system behavior, and depend less on the intuition of fault management engineers to ensure complete examination of off-nominal behavior.

Constraining the Distribution of Vertical Slip on the South Heli Shan Fault (Northeastern Tibet) From High-Resolution Topographic Data

NASA Astrophysics Data System (ADS)

Bi, Haiyun; Zheng, Wenjun; Ge, Weipeng; Zhang, Peizhen; Zeng, Jiangyuan; Yu, Jingxing

2018-03-01

Reconstruction of the along-fault slip distribution provides an insight into the long-term rupture patterns of a fault, thereby enabling more accurate assessment of its future behavior. The increasing wealth of high-resolution topographic data, such as Light Detection and Ranging and photogrammetric digital elevation models, allows us to better constrain the slip distribution, thus greatly improving our understanding of fault behavior. The South Heli Shan Fault is a major active fault on the northeastern margin of the Tibetan Plateau. In this study, we built a 2 m resolution digital elevation model of the South Heli Shan Fault based on high-resolution GeoEye-1 stereo satellite imagery and then measured 302 vertical displacements along the fault, which increased the measurement density of previous field surveys by a factor of nearly 5. The cumulative displacements show an asymmetric distribution along the fault, comprising three major segments. An increasing trend from west to east indicates that the fault has likely propagated westward over its lifetime. The topographic relief of Heli Shan shows an asymmetry similar to the measured cumulative slip distribution, suggesting that the uplift of Heli Shan may result mainly from the long-term activity of the South Heli Shan Fault. Furthermore, the cumulative displacements divide into discrete clusters along the fault, indicating that the fault has ruptured in several large earthquakes. By constraining the slip-length distribution of each rupture, we found that the events do not support a characteristic recurrence model for the fault.

Assessing active faulting by hydrogeological modeling and superconducting gravimetry: A case study for Hsinchu Fault, Taiwan

NASA Astrophysics Data System (ADS)

Lien, Tzuyi; Cheng, Ching-Chung; Hwang, Cheinway; Crossley, David

2014-09-01

We develop a new hydrology and gravimetry-based method to assess whether or not a local fault may be active. We take advantage of an existing superconducting gravimeter (SG) station and a comprehensive groundwater network in Hsinchu to apply the method to the Hsinchu Fault (HF) across the Hsinchu Science Park, whose industrial output accounts for 10% of Taiwan's gross domestic product. The HF is suspected to pose seismic hazards to the park, but its existence and structure are not clear. The a priori geometry of the HF is translated into boundary conditions imposed in the hydrodynamic model. By varying the fault's location, depth, and including a secondary wrench fault, we construct five hydrodynamic models to estimate groundwater variations, which are evaluated by comparing groundwater levels and SG observations. The results reveal that the HF contains a low hydraulic conductivity core and significantly impacts groundwater flows in the aquifers. Imposing the fault boundary conditions leads to about 63-77% reduction in the differences between modeled and observed values (both water level and gravity). The test with fault depth shows that the HF's most recent slip occurred in the beginning of Holocene, supplying a necessary (but not sufficient) condition that the HF is currently active. A portable SG can act as a virtual borehole well for model assessment at critical locations of a suspected active fault.

Contributory fault and level of personal injury to drivers involved in head-on collisions: Application of copula-based bivariate ordinal models.

PubMed

Wali, Behram; Khattak, Asad J; Xu, Jingjing

2018-01-01

The main objective of this study is to simultaneously investigate the degree of injury severity sustained by drivers involved in head-on collisions with respect to fault status designation. This is complicated to answer due to many issues, one of which is the potential presence of correlation between injury outcomes of drivers involved in the same head-on collision. To address this concern, we present seemingly unrelated bivariate ordered response models by analyzing the joint injury severity probability distribution of at-fault and not-at-fault drivers. Moreover, the assumption of bivariate normality of residuals and the linear form of stochastic dependence implied by such models may be unduly restrictive. To test this, Archimedean copula structures and normal mixture marginals are integrated into the joint estimation framework, which can characterize complex forms of stochastic dependencies and non-normality in residual terms. The models are estimated using 2013 Virginia police reported two-vehicle head-on collision data, where exactly one driver is at-fault. The results suggest that both at-fault and not-at-fault drivers sustained serious/fatal injuries in 8% of crashes, whereas, in 4% of the cases, the not-at-fault driver sustained a serious/fatal injury with no injury to the at-fault driver at all. Furthermore, if the at-fault driver is fatigued, apparently asleep, or has been drinking the not-at-fault driver is more likely to sustain a severe/fatal injury, controlling for other factors and potential correlations between the injury outcomes. While not-at-fault vehicle speed affects injury severity of at-fault driver, the effect is smaller than the effect of at-fault vehicle speed on at-fault injury outcome. Contrarily, and importantly, the effect of at-fault vehicle speed on injury severity of not-at-fault driver is almost equal to the effect of not-at-fault vehicle speed on injury outcome of not-at-fault driver. Compared to traditional ordered probability models, the study provides evidence that copula based bivariate models can provide more reliable estimates and richer insights. Practical implications of the results are discussed. Published by Elsevier Ltd.

Main propulsion functional path analysis for performance monitoring fault detection and annunciation

NASA Technical Reports Server (NTRS)

Keesler, E. L.

1974-01-01

A total of 48 operational flight instrumentation measurements were identified for use in performance monitoring and fault detection. The Operational Flight Instrumentation List contains all measurements identified for fault detection and annunciation. Some 16 controller data words were identified for use in fault detection and annunciation.

Impulse Testing of Corporate-Fed Patch Array Antennas

NASA Technical Reports Server (NTRS)

Chamberlain, Neil F.

2011-01-01

This paper discusses a novel method for detecting faults in antenna arrays. The method, termed Impulse Testing, was developed for corporate-fed patch arrays where the element is fed by a probe and is shorted at its center. Impulse Testing was devised to supplement conventional microwave measurements in order to quickly verify antenna integrity. The technique relies on exciting each antenna element in turn with a fast pulse (or impulse) that propagates through the feed network to the output port of the antenna. The resulting impulse response is characteristic of the path through the feed network. Using an oscilloscope, a simple amplitude measurement can be made to detect faults. A circuit model of the antenna elements and feed network was constructed to assess various fault scenarios and determine fault-detection thresholds. The experimental setup and impulse measurements for two patch array antennas are presented. Advantages and limitations of the technique are discussed along with applications to other antenna array topologies

Joint Inversion of 1-D Magnetotelluric and Surface-Wave Dispersion Data with an Improved Multi-Objective Genetic Algorithm and Application to the Data of the Longmenshan Fault Zone

NASA Astrophysics Data System (ADS)

Wu, Pingping; Tan, Handong; Peng, Miao; Ma, Huan; Wang, Mao

2018-05-01

Magnetotellurics and seismic surface waves are two prominent geophysical methods for deep underground exploration. Joint inversion of these two datasets can help enhance the accuracy of inversion. In this paper, we describe a method for developing an improved multi-objective genetic algorithm (NSGA-SBX) and applying it to two numerical tests to verify the advantages of the algorithm. Our findings show that joint inversion with the NSGA-SBX method can improve the inversion results by strengthening structural coupling when the discontinuities of the electrical and velocity models are consistent, and in case of inconsistent discontinuities between these models, joint inversion can retain the advantages of individual inversions. By applying the algorithm to four detection points along the Longmenshan fault zone, we observe several features. The Sichuan Basin demonstrates low S-wave velocity and high conductivity in the shallow crust probably due to thick sedimentary layers. The eastern margin of the Tibetan Plateau shows high velocity and high resistivity in the shallow crust, while two low velocity layers and a high conductivity layer are observed in the middle lower crust, probably indicating the mid-crustal channel flow. Along the Longmenshan fault zone, a high conductivity layer from 8 to 20 km is observed beneath the northern segment and decreases with depth beneath the middle segment, which might be caused by the elevated fluid content of the fault zone.

Induction machine bearing faults detection based on a multi-dimensional MUSIC algorithm and maximum likelihood estimation.

PubMed

Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

2016-07-01

Condition monitoring of electric drives is of paramount importance since it contributes to enhance the system reliability and availability. Moreover, the knowledge about the fault mode behavior is extremely important in order to improve system protection and fault-tolerant control. Fault detection and diagnosis in squirrel cage induction machines based on motor current signature analysis (MCSA) has been widely investigated. Several high resolution spectral estimation techniques have been developed and used to detect induction machine abnormal operating conditions. This paper focuses on the application of MCSA for the detection of abnormal mechanical conditions that may lead to induction machines failure. In fact, this paper is devoted to the detection of single-point defects in bearings based on parametric spectral estimation. A multi-dimensional MUSIC (MD MUSIC) algorithm has been developed for bearing faults detection based on bearing faults characteristic frequencies. This method has been used to estimate the fundamental frequency and the fault related frequency. Then, an amplitude estimator of the fault characteristic frequencies has been proposed and fault indicator has been derived for fault severity measurement. The proposed bearing faults detection approach is assessed using simulated stator currents data, issued from a coupled electromagnetic circuits approach for air-gap eccentricity emulating bearing faults. Then, experimental data are used for validation purposes. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Estimation of fault geometry of a slow slip event off the Kii Peninsula, southwest of Japan, detected by DONET

NASA Astrophysics Data System (ADS)

Suzuki, K.; Nakano, M.; Hori, T.; Takahashi, N.

2015-12-01

The Japan Agency for Marine-Earth Science and Technology installed permanent ocean bottom observation network called Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) off the Kii Peninsula, southwest of Japan, to monitor earthquakes and tsunamis. We detected the long-term vertical displacements of sea floor from the ocean-bottom pressure records, starting from March 2013, at several DONET stations (Suzuki et al., 2014). We consider that these displacements were caused by the crustal deformation due to a slow slip event (SSE). 　We estimated the fault geometry of the SSE by using the observed ocean-bottom displacements. The ocean-bottom displacements were obtained by removing the tidal components from the pressure records. We also subtracted the average of pressure changes taken over the records at stations connected to each science node from each record in order to remove the contributions due to atmospheric pressure changes and non-tidal ocean dynamic mass variations. Therefore we compared observed displacements with the theoretical ones that was subtracted the average displacement in the fault geometry estimation. We also compared observed and theoretical average displacements for the model evaluation. In this study, the observed average displacements were assumed to be zero. Although there are nine parameters in the fault model, we observed vertical displacements at only four stations. Therefore we assumed three fault geometries; (1) a reverse fault slip along the plate boundary, (2) a strike slip along a splay fault, and (3) a reverse fault slip along the splay fault. We obtained that the model (3) gives the smallest residual between observed and calculated displacements. We also observed that this SSE was synchronized with a decrease in the background seismicity within the area of a nearby earthquake cluster. In the future, we will investigate the relationship between the SSE and the seismicity change.

Phase response curves for models of earthquake fault dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Franović, Igor, E-mail: franovic@ipb.ac.rs; Kostić, Srdjan; Perc, Matjaž

We systematically study effects of external perturbations on models describing earthquake fault dynamics. The latter are based on the framework of the Burridge-Knopoff spring-block system, including the cases of a simple mono-block fault, as well as the paradigmatic complex faults made up of two identical or distinct blocks. The blocks exhibit relaxation oscillations, which are representative for the stick-slip behavior typical for earthquake dynamics. Our analysis is carried out by determining the phase response curves of first and second order. For a mono-block fault, we consider the impact of a single and two successive pulse perturbations, further demonstrating how themore » profile of phase response curves depends on the fault parameters. For a homogeneous two-block fault, our focus is on the scenario where each of the blocks is influenced by a single pulse, whereas for heterogeneous faults, we analyze how the response of the system depends on whether the stimulus is applied to the block having a shorter or a longer oscillation period.« less

Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis

NASA Astrophysics Data System (ADS)

Girondin, Victor; Pekpe, Komi Midzodzi; Morel, Herve; Cassar, Jean-Philippe

2013-07-01

The objective of this paper is to propose a vibration-based automated framework dealing with local faults occurring on bearings in the transmission of a helicopter. The knowledge of the shaft speed and kinematic computation provide theoretical frequencies that reveal deteriorations on the inner and outer races, on the rolling elements or on the cage. In practice, the theoretical frequencies of bearing faults may be shifted. They may also be masked by parasitical frequencies because the numerous noisy vibrations and the complexity of the transmission mechanics make the signal spectrum very profuse. Consequently, detection methods based on the monitoring of the theoretical frequencies may lead to wrong decisions. In order to deal with this drawback, we propose to readjust the fault frequencies from the theoretical frequencies using the redundancy introduced by the harmonics. The proposed method provides the confidence index of the readjusted frequency. Minor variations in shaft speed may induce random jitters. The change of the contact surface or of the transmission path brings also a random component in amplitude and phase. These random components in the signal destroy spectral localization of frequencies and thus hide the fault occurrence in the spectrum. Under the hypothesis that these random signals can be modeled as cyclostationary signals, the envelope spectrum can reveal that hidden patterns. In order to provide an indicator estimating fault severity, statistics are proposed. They make the hypothesis that the harmonics at the readjusted frequency are corrupted with an additive normally distributed noise. In this case, the statistics computed from the spectra are chi-square distributed and a signal-to-noise indicator is proposed. The algorithms are then tested with data from two test benches and from flight conditions. The bearing type and the radial load are the main differences between the experiences on the benches. The fault is mainly visible in the spectrum for the radially constrained bearing and only visible in the envelope spectrum for the "load-free" bearing. Concerning results in flight conditions, frequency readjustment demonstrates good performances when applied on the spectrum, showing that a fully automated bearing decision procedure is applicable for operational helicopter monitoring.

Aircraft Fault Detection and Classification Using Multi-Level Immune Learning Detection

NASA Technical Reports Server (NTRS)

Wong, Derek; Poll, Scott; KrishnaKumar, Kalmanje

2005-01-01

This work is an extension of a recently developed software tool called MILD (Multi-level Immune Learning Detection), which implements a negative selection algorithm for anomaly and fault detection that is inspired by the human immune system. The immunity-based approach can detect a broad spectrum of known and unforeseen faults. We extend MILD by applying a neural network classifier to identify the pattern of fault detectors that are activated during fault detection. Consequently, MILD now performs fault detection and identification of the system under investigation. This paper describes the application of MILD to detect and classify faults of a generic transport aircraft augmented with an intelligent flight controller. The intelligent control architecture is designed to accommodate faults without the need to explicitly identify them. Adding knowledge about the existence and type of a fault will improve the handling qualities of a degraded aircraft and impact tactical and strategic maneuvering decisions. In addition, providing fault information to the pilot is important for maintaining situational awareness so that he can avoid performing an action that might lead to unexpected behavior - e.g., an action that exceeds the remaining control authority of the damaged aircraft. We discuss the detection and classification results of simulated failures of the aircraft's control system and show that MILD is effective at determining the problem with low false alarm and misclassification rates.

Detecting drawdowns masked by environmental stresses with water-level models

USGS Publications Warehouse

Garcia, C.A.; Halford, K.J.; Fenelon, J.M.

2013-01-01

Detecting and quantifying small drawdown at observation wells distant from the pumping well greatly expands the characterized aquifer volume. However, this detection is often obscured by water level fluctuations such as barometric and tidal effects. A reliable analytical approach for distinguishing drawdown from nonpumping water-level fluctuations is presented and tested here. Drawdown is distinguished by analytically simulating all pumping and nonpumping water-level stresses simultaneously during the period of record. Pumping signals are generated with Theis models, where the pumping schedule is translated into water-level change with the Theis solution. This approach closely matched drawdowns simulated with a complex three-dimensional, hypothetical model and reasonably estimated drawdowns from an aquifer test conducted in a complex hydrogeologic system. Pumping-induced changes generated with a numerical model and analytical Theis model agreed (RMS as low as 0.007 m) in cases where pumping signals traveled more than 1 km across confining units and fault structures. Maximum drawdowns of about 0.05 m were analytically estimated from field investigations where environmental fluctuations approached 0.2 m during the analysis period.

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

NASA Technical Reports Server (NTRS)

Bregon, Anibal; Daigle, Matthew J.; Roychoudhury, Indranil

2012-01-01

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

Eastern Denali Fault surface trace map, eastern Alaska and Yukon, Canada

USGS Publications Warehouse

Bender, Adrian M.; Haeussler, Peter J.

2017-05-04

We map the 385-kilometer (km) long surface trace of the right-lateral, strike-slip Denali Fault between the Totschunda-Denali Fault intersection in Alaska, United States and the village of Haines Junction, Yukon, Canada. In Alaska, digital elevation models based on light detection and ranging and interferometric synthetic aperture radar data enabled our fault mapping at scales of 1:2,000 and 1:10,000, respectively. Lacking such resources in Yukon, we developed new structure-from-motion digital photogrammetry products from legacy aerial photos to map the fault surface trace at a scale of 1:10,000 east of the international border. The section of the fault that we map, referred to as the Eastern Denali Fault, did not rupture during the 2002 Denali Fault earthquake (moment magnitude 7.9). Seismologic, geodetic, and geomorphic evidence, along with a paleoseismic record of past ground-rupturing earthquakes, demonstrate Holocene and contemporary activity on the fault, however. This map of the Eastern Denali Fault surface trace complements other data sets by providing an openly accessible digital interpretation of the location, length, and continuity of the fault’s surface trace based on the accompanying digital topography dataset. Additionally, the digitized fault trace may provide geometric constraints useful for modeling earthquake scenarios and related seismic hazard.

A Novel Arc Fault Detector for Early Detection of Electrical Fires

PubMed Central

Yang, Kai; Zhang, Rencheng; Yang, Jianhong; Liu, Canhua; Chen, Shouhong; Zhang, Fujiang

2016-01-01

Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires. PMID:27070618

Pattern classifier for health monitoring of helicopter gearboxes

NASA Technical Reports Server (NTRS)

Chin, Hsinyung; Danai, Kourosh; Lewicki, David G.

1993-01-01

The application of a newly developed diagnostic method to a helicopter gearbox is demonstrated. This method is a pattern classifier which uses a multi-valued influence matrix (MVIM) as its diagnostic model. The method benefits from a fast learning algorithm, based on error feedback, that enables it to estimate gearbox health from a small set of measurement-fault data. The MVIM method can also assess the diagnosability of the system and variability of the fault signatures as the basis to improve fault signatures. This method was tested on vibration signals reflecting various faults in an OH-58A main rotor transmission gearbox. The vibration signals were then digitized and processed by a vibration signal analyzer to enhance and extract various features of the vibration data. The parameters obtained from this analyzer were utilized to train and test the performance of the MVIM method in both detection and diagnosis. The results indicate that the MVIM method provided excellent detection results when the full range of faults effects on the measurements were included in training, and it had a correct diagnostic rate of 95 percent when the faults were included in training.

Aircraft applications of fault detection and isolation techniques

NASA Astrophysics Data System (ADS)

Marcos Esteban, Andres

In this thesis the problems of fault detection & isolation and fault tolerant systems are studied from the perspective of LTI frequency-domain, model-based techniques. Emphasis is placed on the applicability of these LTI techniques to nonlinear models, especially to aerospace systems. Two applications of Hinfinity LTI fault diagnosis are given using an open-loop (no controller) design approach: one for the longitudinal motion of a Boeing 747-100/200 aircraft, the other for a turbofan jet engine. An algorithm formalizing a robust identification approach based on model validation ideas is also given and applied to the previous jet engine. A general linear fractional transformation formulation is given in terms of the Youla and Dual Youla parameterizations for the integrated (control and diagnosis filter) approach. This formulation provides better insight into the trade-off between the control and the diagnosis objectives. It also provides the basic groundwork towards the development of nested schemes for the integrated approach. These nested structures allow iterative improvements on the control/filter Youla parameters based on successive identification of the system uncertainty (as given by the Dual Youla parameter). The thesis concludes with an application of Hinfinity LTI techniques to the integrated design for the longitudinal motion of the previous Boeing 747-100/200 model.

Model-based development of a fault signature matrix to improve solid oxide fuel cell systems on-site diagnosis

NASA Astrophysics Data System (ADS)

Polverino, Pierpaolo; Pianese, Cesare; Sorrentino, Marco; Marra, Dario

2015-04-01

The paper focuses on the design of a procedure for the development of an on-field diagnostic algorithm for solid oxide fuel cell (SOFC) systems. The diagnosis design phase relies on an in-deep analysis of the mutual interactions among all system components by exploiting the physical knowledge of the SOFC system as a whole. This phase consists of the Fault Tree Analysis (FTA), which identifies the correlations among possible faults and their corresponding symptoms at system components level. The main outcome of the FTA is an inferential isolation tool (Fault Signature Matrix - FSM), which univocally links the faults to the symptoms detected during the system monitoring. In this work the FTA is considered as a starting point to develop an improved FSM. Making use of a model-based investigation, a fault-to-symptoms dependency study is performed. To this purpose a dynamic model, previously developed by the authors, is exploited to simulate the system under faulty conditions. Five faults are simulated, one for the stack and four occurring at BOP level. Moreover, the robustness of the FSM design is increased by exploiting symptom thresholds defined for the investigation of the quantitative effects of the simulated faults on the affected variables.

Identification of faulty sensor using relative partial decomposition via independent component analysis

NASA Astrophysics Data System (ADS)

Wang, Z.; Quek, S. T.

2015-07-01

Performance of any structural health monitoring algorithm relies heavily on good measurement data. Hence, it is necessary to employ robust faulty sensor detection approaches to isolate sensors with abnormal behaviour and exclude the highly inaccurate data in the subsequent analysis. The independent component analysis (ICA) is implemented to detect the presence of sensors showing abnormal behaviour. A normalized form of the relative partial decomposition contribution (rPDC) is proposed to identify the faulty sensor. Both additive and multiplicative types of faults are addressed and the detectability illustrated using a numerical and an experimental example. An empirical method to establish control limits for detecting and identifying the type of fault is also proposed. The results show the effectiveness of the ICA and rPDC method in identifying faulty sensor assuming that baseline cases are available.

Artificial immune system via Euclidean Distance Minimization for anomaly detection in bearings

NASA Astrophysics Data System (ADS)

Montechiesi, L.; Cocconcelli, M.; Rubini, R.

2016-08-01

In recent years new diagnostics methodologies have emerged, with particular interest into machinery operating in non-stationary conditions. In fact continuous speed changes and variable loads make non-trivial the spectrum analysis. A variable speed means a variable characteristic fault frequency related to the damage that is no more recognizable in the spectrum. To overcome this problem the scientific community proposed different approaches listed in two main categories: model-based approaches and expert systems. In this context the paper aims to present a simple expert system derived from the mechanisms of the immune system called Euclidean Distance Minimization, and its application in a real case of bearing faults recognition. The proposed method is a simplification of the original process, adapted by the class of Artificial Immune Systems, which proved to be useful and promising in different application fields. Comparative results are provided, with a complete explanation of the algorithm and its functioning aspects.

Real-time automated failure identification in the Control Center Complex (CCC)

NASA Technical Reports Server (NTRS)

Kirby, Sarah; Lauritsen, Janet; Pack, Ginger; Ha, Anhhoang; Jowers, Steven; Mcnenny, Robert; Truong, The; Dell, James

1993-01-01

A system which will provide real-time failure management support to the Space Station Freedom program is described. The system's use of a simplified form of model based reasoning qualifies it as an advanced automation system. However, it differs from most such systems in that it was designed from the outset to meet two sets of requirements. First, it must provide a useful increment to the fault management capabilities of the Johnson Space Center (JSC) Control Center Complex (CCC) Fault Detection Management system. Second, it must satisfy CCC operational environment constraints such as cost, computer resource requirements, verification, and validation, etc. The need to meet both requirement sets presents a much greater design challenge than would have been the case had functionality been the sole design consideration. The choice of technology, discussing aspects of that choice and the process for migrating it into the control center is overviewed.

The effect of gradational velocities and anisotropy on fault-zone trapped waves

NASA Astrophysics Data System (ADS)

Gulley, A. K.; Eccles, J. D.; Kaipio, J. P.; Malin, P. E.

2017-08-01

Synthetic fault-zone trapped wave (FZTW) dispersion curves and amplitude responses for FL (Love) and FR (Rayleigh) type phases are analysed in transversely isotropic 1-D elastic models. We explore the effects of velocity gradients, anisotropy, source location and mechanism. These experiments suggest: (i) A smooth exponentially decaying velocity model produces a significantly different dispersion curve to that of a three-layer model, with the main difference being that Airy phases are not produced. (ii) The FZTW dispersion and amplitude information of a waveguide with transverse-isotropy depends mostly on the Shear wave velocities in the direction parallel with the fault, particularly if the fault zone to country-rock velocity contrast is small. In this low velocity contrast situation, fully isotropic approximations to a transversely isotropic velocity model can be made. (iii) Fault-aligned fractures and/or bedding in the fault zone that cause transverse-isotropy enhance the amplitude and wave-train length of the FR type FZTW. (iv) Moving the source and/or receiver away from the fault zone removes the higher frequencies first, similar to attenuation. (v) In most physically realistic cases, the radial component of the FR type FZTW is significantly smaller in amplitude than the transverse.

Hanging-wall deformation above a normal fault: sequential limit analyses

NASA Astrophysics Data System (ADS)

Yuan, Xiaoping; Leroy, Yves M.; Maillot, Bertrand

2015-04-01

The deformation in the hanging wall above a segmented normal fault is analysed with the sequential limit analysis (SLA). The method combines some predictions on the dip and position of the active fault and axial surface, with geometrical evolution à la Suppe (Groshong, 1989). Two problems are considered. The first followed the prototype proposed by Patton (2005) with a pre-defined convex, segmented fault. The orientation of the upper segment of the normal fault is an unknown in the second problem. The loading in both problems consists of the retreat of the back wall and the sedimentation. This sedimentation starts from the lowest point of the topography and acts at the rate rs relative to the wall retreat rate. For the first problem, the normal fault either has a zero friction or a friction value set to 25o or 30o to fit the experimental results (Patton, 2005). In the zero friction case, a hanging wall anticline develops much like in the experiments. In the 25o friction case, slip on the upper segment is accompanied by rotation of the axial plane producing a broad shear zone rooted at the fault bend. The same observation is made in the 30o case, but without slip on the upper segment. Experimental outcomes show a behaviour in between these two latter cases. For the second problem, mechanics predicts a concave fault bend with an upper segment dip decreasing during extension. The axial surface rooting at the normal fault bend sees its dips increasing during extension resulting in a curved roll-over. Softening on the normal fault leads to a stepwise rotation responsible for strain partitioning into small blocks in the hanging wall. The rotation is due to the subsidence of the topography above the hanging wall. Sedimentation in the lowest region thus reduces the rotations. Note that these rotations predicted by mechanics are not accounted for in most geometrical approaches (Xiao and Suppe, 1992) and are observed in sand box experiments (Egholm et al., 2007, referring to Dahl, 1987). References: Egholm, D. L., M. Sandiford, O. R. Clausen, and S. B. Nielsen (2007), A new strategy for discrete element numerical models: 2. sandbox applications, Journal of Geophysical Research, 112 (B05204), doi:10.1029/2006JB004558. Groshong, R. H. (1989), Half-graben structures: Balanced models of extensional fault-bend folds, Geological Society of America Bulletin, 101 (1), 96-105. Patton, T. L. (2005), Sandbox models of downward-steepening normal faults, AAPG Bulletin, 89 (6), 781-797. Xiao, H.-B., and J. Suppe (1992), Orgin of rollover, AAPG Bulletin, 76 (4), 509-529.

Effects of faults as barriers or conduits to displaced brine flow on a putative CO2 storage site in the Southern North Sea

NASA Astrophysics Data System (ADS)

Hannis, Sarah; Bricker, Stephanie; Williams, John

2013-04-01

The Bunter Sandstone Formation in the Southern North Sea is a potential reservoir being considered for carbon dioxide storage as a climate change mitigation option. A geological model of a putative storage site within this saline aquifer was built from 3D seismic and well data to investigate potential reservoir pressure changes and their effects on fault movement, brine and CO2 migration as a result of CO2 injection. The model is located directly beneath the Dogger Bank Special Area of Conservation, close to the UK-Netherlands median line. Analysis of the seismic data reveals two large fault zones, one in each of the UK and Netherlands sectors, many tens of kilometres in length, extending from reservoir level to the sea bed. Although it has been shown that similar faults compartmentalise gas fields elsewhere in the Netherlands sector, significant uncertainty remains surrounding the properties of the faults in our model area; in particular their cross- and along-fault permeability and geomechanical behaviour. Despite lying outside the anticipated CO2 plume, these faults could provide potential barriers to pore fluid migration and pressure dissipation, until, under elevated pressures, they provide vertical migration pathways for brine. In this case, the faults will act to enhance injectivity, but potential environmental impacts, should the displaced brine be expelled at the sea bed, will require consideration. Pressure gradients deduced from regional leak-off test data have been input into a simple geomechanical model to estimate the threshold pressure gradient at which faults cutting the Mesozoic succession will fail, assuming reactivation of fault segments will cause an increase in vertical permeability. Various 4D scenarios were run using a single-phase groundwater modelling code, calibrated to results from a multi-phase commercial simulator. Possible end-member ranges of fault parameters were input to investigate the pressure change with time and quantify brine flux to the seabed in potentially reactivated sections of each fault zone. Combining the modelled pressure field with the calculated fault failure criterion suggests that only the fault in the Netherlands sector reactivates, allowing brine displacement at a maximum rate of 800 - 900 m3/d. Model results indicate that the extent of brine displacement is most sensitive to the fault reactivation pressure gradient and fault zone thickness. In conclusion, CO2 injection into a saline aquifer results in a significant increase in pore-fluid pressure gradients. In this case, brine displacement along faults acting as pressure relief valves could increase injectivity in a similar manner to pressure management wells, thereby facilitating the storage operation. However, if the faults act as brine migration pathways, an understanding of seabed flux rates and environmental impacts will need to be demonstrated to regulators prior to injection. This study, close to an international border, also highlights the need to inform neighbouring countries authorities of proposed operations and, potentially, to obtain licences to increase reservoir pressure and/or displace brine across international borders.

Tsunamis and splay fault dynamics

USGS Publications Warehouse

Wendt, J.; Oglesby, D.D.; Geist, E.L.

2009-01-01

The geometry of a fault system can have significant effects on tsunami generation, but most tsunami models to date have not investigated the dynamic processes that determine which path rupture will take in a complex fault system. To gain insight into this problem, we use the 3D finite element method to model the dynamics of a plate boundary/splay fault system. We use the resulting ground deformation as a time-dependent boundary condition for a 2D shallow-water hydrodynamic tsunami calculation. We find that if me stress distribution is homogeneous, rupture remains on the plate boundary thrust. When a barrier is introduced along the strike of the plate boundary thrust, rupture propagates to the splay faults, and produces a significantly larger tsunami man in the homogeneous case. The results have implications for the dynamics of megathrust earthquakes, and also suggest mat dynamic earthquake modeling may be a useful tool in tsunami researcn. Copyright 2009 by the American Geophysical Union.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis.

PubMed

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun

2017-07-28

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.

An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis

PubMed Central

Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang

2017-01-01

Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster–Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions. PMID:28788099

Planetary Gearbox Fault Detection Using Vibration Separation Techniques

NASA Technical Reports Server (NTRS)

Lewicki, David G.; LaBerge, Kelsen E.; Ehinger, Ryan T.; Fetty, Jason

2011-01-01

Studies were performed to demonstrate the capability to detect planetary gear and bearing faults in helicopter main-rotor transmissions. The work supported the Operations Support and Sustainment (OSST) program with the U.S. Army Aviation Applied Technology Directorate (AATD) and Bell Helicopter Textron. Vibration data from the OH-58C planetary system were collected on a healthy transmission as well as with various seeded-fault components. Planetary fault detection algorithms were used with the collected data to evaluate fault detection effectiveness. Planet gear tooth cracks and spalls were detectable using the vibration separation techniques. Sun gear tooth cracks were not discernibly detectable from the vibration separation process. Sun gear tooth spall defects were detectable. Ring gear tooth cracks were only clearly detectable by accelerometers located near the crack location or directly across from the crack. Enveloping provided an effective method for planet bearing inner- and outer-race spalling fault detection.

Model uncertainties of the 2002 update of California seismic hazard maps

USGS Publications Warehouse

Cao, T.; Petersen, M.D.; Frankel, A.D.

2005-01-01

In this article we present and explore the source and ground-motion model uncertainty and parametric sensitivity for the 2002 update of the California probabilistic seismic hazard maps. Our approach is to implement a Monte Carlo simulation that allows for independent sampling from fault to fault in each simulation. The source-distance dependent characteristics of the uncertainty maps of seismic hazard are explained by the fundamental uncertainty patterns from four basic test cases, in which the uncertainties from one-fault and two-fault systems are studied in detail. The California coefficient of variation (COV, ratio of the standard deviation to the mean) map for peak ground acceleration (10% of exceedance in 50 years) shows lower values (0.1-0.15) along the San Andreas fault system and other class A faults than along class B faults (0.2-0.3). High COV values (0.4-0.6) are found around the Garlock, Anacapa-Dume, and Palos Verdes faults in southern California and around the Maacama fault and Cascadia subduction zone in northern California.

Effects induced by an earthquake on its fault plane:a boundary element study

NASA Astrophysics Data System (ADS)

Bonafede, Maurizio; Neri, Andrea

2000-04-01

Mechanical effects left by a model earthquake on its fault plane, in the post-seismic phase, are investigated employing the `displacement discontinuity method'. Simple crack models, characterized by the release of a constant, unidirectional shear traction are investigated first. Both slip components-parallel and normal to the traction direction-are found to be non-vanishing and to depend on fault depth, dip, aspect ratio and fault plane geometry. The rake of the slip vector is similarly found to depend on depth and dip. The fault plane is found to suffer some small rotation and bending, which may be responsible for the indentation of a transform tectonic margin, particularly if cumulative effects are considered. Very significant normal stress components are left over the shallow portion of the fault surface after an earthquake: these are tensile for thrust faults, compressive for normal faults and are typically comparable in size to the stress drop. These normal stresses can easily be computed for more realistic seismic source models, in which a variable slip is assigned; normal stresses are induced in these cases too, and positive shear stresses may even be induced on the fault plane in regions of high slip gradient. Several observations can be explained from the present model: low-dip thrust faults and high-dip normal faults are found to be facilitated, according to the Coulomb failure criterion, in repetitive earthquake cycles; the shape of dip-slip faults near the surface is predicted to be upward-concave; and the shallower aftershock activity generally found in the hanging block of a thrust event can be explained by `unclamping' mechanisms.

Applications of Fault Detection in Vibrating Structures

NASA Technical Reports Server (NTRS)

Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

2012-01-01

Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.

Static air-gap eccentricity fault diagnosis using rotor slot harmonics in line neutral voltage of three-phase squirrel cage induction motor

NASA Astrophysics Data System (ADS)

Oumaamar, Mohamed El Kamel; Maouche, Yassine; Boucherma, Mohamed; Khezzar, Abdelmalek

2017-02-01

The mixed eccentricity fault detection in a squirrel cage induction motor has been thoroughly investigated. However, a few papers have been related to pure static eccentricity fault and the authors focused on the RSH harmonics presented in stator current. The main objective of this paper is to present an alternative method based on the analysis of line neutral voltage taking place between the supply and the stator neutrals in order to detect air-gap static eccentricity, and to highlight the classification of all RSH harmonics in line neutral voltage. The model of squirrel cage induction machine relies on the rotor geometry and winding layout. Such developed model is used to analyze the impact of the pure static air-gap eccentricity by predicting the related frequencies in the line neutral voltage spectrum. The results show that the line neutral voltage spectrum are more sensitive to the air-gap static eccentricity fault compared to stator current one. The theoretical analysis and simulated results are confirmed by experiments.

Hybrid Neural-Network: Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics Developed and Demonstrated

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2002-01-01

As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2003-01-01

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

Functional requirements for an intelligent RPC. [remote power controller for spaceborne electrical distribution system

NASA Technical Reports Server (NTRS)

Aucoin, B. M.; Heller, R. P.

1990-01-01

An intelligent remote power controller (RPC) based on microcomputer technology can implement advanced functions for the accurate and secure detection of all types of faults on a spaceborne electrical distribution system. The intelligent RPC will implement conventional protection functions such as overcurrent, under-voltage, and ground fault protection. Advanced functions for the detection of soft faults, which cannot presently be detected, can also be implemented. Adaptive overcurrent protection changes overcurrent settings based on connected load. Incipient and high-impedance fault detection provides early detection of arcing conditions to prevent fires, and to clear and reconfigure circuits before soft faults progress to a hard-fault condition. Power electronics techniques can be used to implement fault current limiting to prevent voltage dips during hard faults. It is concluded that these techniques will enhance the overall safety and reliability of the distribution system.

Model reconstruction using POD method for gray-box fault detection

NASA Technical Reports Server (NTRS)

Park, H. G.; Zak, M.

2003-01-01

This paper describes using Proper Orthogonal Decomposition (POD) method to create low-order dynamical models for the Model Filter component of Beacon-based Exception Analysis for Multi-missions (BEAM).

Fault detection using a two-model test for changes in the parameters of an autoregressive time series

NASA Technical Reports Server (NTRS)

Scholtz, P.; Smyth, P.

1992-01-01

This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.

Fault tree analysis for urban flooding.

PubMed

ten Veldhuis, J A E; Clemens, F H L R; van Gelder, P H A J M

2009-01-01

Traditional methods to evaluate flood risk generally focus on heavy storm events as the principal cause of flooding. Conversely, fault tree analysis is a technique that aims at modelling all potential causes of flooding. It quantifies both overall flood probability and relative contributions of individual causes of flooding. This paper presents a fault model for urban flooding and an application to the case of Haarlem, a city of 147,000 inhabitants. Data from a complaint register, rainfall gauges and hydrodynamic model calculations are used to quantify probabilities of basic events in the fault tree. This results in a flood probability of 0.78/week for Haarlem. It is shown that gully pot blockages contribute to 79% of flood incidents, whereas storm events contribute only 5%. This implies that for this case more efficient gully pot cleaning is a more effective strategy to reduce flood probability than enlarging drainage system capacity. Whether this is also the most cost-effective strategy can only be decided after risk assessment has been complemented with a quantification of consequences of both types of events. To do this will be the next step in this study.

A soft computing scheme incorporating ANN and MOV energy in fault detection, classification and distance estimation of EHV transmission line with FSC.

PubMed

Khadke, Piyush; Patne, Nita; Singh, Arvind; Shinde, Gulab

2016-01-01

In this article, a novel and accurate scheme for fault detection, classification and fault distance estimation for a fixed series compensated transmission line is proposed. The proposed scheme is based on artificial neural network (ANN) and metal oxide varistor (MOV) energy, employing Levenberg-Marquardt training algorithm. The novelty of this scheme is the use of MOV energy signals of fixed series capacitors (FSC) as input to train the ANN. Such approach has never been used in any earlier fault analysis algorithms in the last few decades. Proposed scheme uses only single end measurement energy signals of MOV in all the 3 phases over one cycle duration from the occurrence of a fault. Thereafter, these MOV energy signals are fed as input to ANN for fault distance estimation. Feasibility and reliability of the proposed scheme have been evaluated for all ten types of fault in test power system model at different fault inception angles over numerous fault locations. Real transmission system parameters of 3-phase 400 kV Wardha-Aurangabad transmission line (400 km) with 40 % FSC at Power Grid Wardha Substation, India is considered for this research. Extensive simulation experiments show that the proposed scheme provides quite accurate results which demonstrate complete protection scheme with high accuracy, simplicity and robustness.

Dependence of residual displacements on the width and depth of compliant fault zones: a 3D study

NASA Astrophysics Data System (ADS)

Kang, J.; Duan, B.

2011-12-01

Compliant fault zones have been detected along active faults by seismic investigations (trapped waves and travel time analysis) and InSAR observations. However, the width and depth extent of compliant fault zones are still under debate in the community. Numerical models of dynamic rupture build a bridge between theories and the geological and geophysical observations. Theoretical 2D plane-strain studies of elastic and inelastic response of compliant fault zones to nearby earthquake have been conducted by Duan [2010] and Duan et al [2010]. In this study, we further extend the experiments to 3D with a focus on elastic response. We are specifically interested in how residual displacements depend on the structure and properties of complaint fault zones, in particular on the width and depth extent. We conduct numerical experiments on various types of fault-zone models, including fault zones with a constant width along depth, with decreasing widths along depth, and with Hanning taper profiles of velocity reduction. . Our preliminary results suggest 1) the width of anomalous horizontal residual displacement is only indicative of the width of a fault zone near the surface, and 2) the vertical residual displacement contains information of the depth extent of compliant fault zones.

Development and evaluation of a fault-tolerant multiprocessor (FTMP) computer. Volume 4: FTMP executive summary

NASA Technical Reports Server (NTRS)

Smith, T. B., III; Lala, J. H.

1984-01-01

The FTMP architecture is a high reliability computer concept modeled after a homogeneous multiprocessor architecture. Elements of the FTMP are operated in tight synchronism with one another and hardware fault-detection and fault-masking is provided which is transparent to the software. Operating system design and user software design is thus greatly simplified. Performance of the FTMP is also comparable to that of a simplex equivalent due to the efficiency of fault handling hardware. The FTMP project constructed an engineering module of the FTMP, programmed the machine and extensively tested the architecture through fault injection and other stress testing. This testing confirmed the soundness of the FTMP concepts.

How does damage affect rupture propagation across a fault stepover?

NASA Astrophysics Data System (ADS)

Cooke, M. L.; Savage, H. M.

2011-12-01

We investigate the potential for fault damage to influence earthquake rupture at fault step-overs using a mechanical numerical model that explicitly includes the generation of cracks around faults. We compare the off-fault fracture patterns and slip profiles generated along faults with a variety of frictional slip-weakening distances and step-over geometry. Models with greater damage facilitate the transfer of slip to the second fault. Increasing separation and decreasing the overlap distance reduces the transfer of slip across the step over. This is consistent with observations of rupture stopping at step-over separation greater than 4 km (Wesnousky, 2006). In cases of slip transfer, rupture is often passed to the second fault before the damage zone cracks of the first fault reach the second fault. This implies that stresses from the damage fracture tips are transmitted elastically to the second fault to trigger the onset of slip along the second fault. Consequently, the growth of damage facilitates transfer of rupture from one fault to another across the step-over. In addition, the rupture propagates along the damage-producing fault faster than along the rougher fault that does not produce damage. While this result seems counter to our understanding that damage slows rupture propagation, which is documented in our models with pre-existing damage, these model results are suggesting an additional process. The slip along the newly created damage may unclamp portions of the fault ahead of the rupture and promote faster rupture. We simulate the M7.1 Hector Mine Earthquake and compare the generated fracture patterns to maps of surface damage. Because along with the detailed damage pattern, we also know the stress drop during the earthquake, we may begin to constrain parameters like the slip-weakening distance along portions of the faults that ruptured in the Hector Mine earthquake.

New Fault Recognition Method for Rotary Machinery Based on Information Entropy and a Probabilistic Neural Network.

PubMed

Jiang, Quansheng; Shen, Yehu; Li, Hua; Xu, Fengyu

2018-01-24

Feature recognition and fault diagnosis plays an important role in equipment safety and stable operation of rotating machinery. In order to cope with the complexity problem of the vibration signal of rotating machinery, a feature fusion model based on information entropy and probabilistic neural network is proposed in this paper. The new method first uses information entropy theory to extract three kinds of characteristics entropy in vibration signals, namely, singular spectrum entropy, power spectrum entropy, and approximate entropy. Then the feature fusion model is constructed to classify and diagnose the fault signals. The proposed approach can combine comprehensive information from different aspects and is more sensitive to the fault features. The experimental results on simulated fault signals verified better performances of our proposed approach. In real two-span rotor data, the fault detection accuracy of the new method is more than 10% higher compared with the methods using three kinds of information entropy separately. The new approach is proved to be an effective fault recognition method for rotating machinery.

Strain Accumulation and Release of the Gorkha, Nepal, Earthquake (M w 7.8, 25 April 2015)

NASA Astrophysics Data System (ADS)

Morsut, Federico; Pivetta, Tommaso; Braitenberg, Carla; Poretti, Giorgio

2017-08-01

The near-fault GNSS records of strong-ground movement are the most sensitive for defining the fault rupture. Here, two unpublished GNSS records are studied, a near-fault-strong-motion station (NAGA) and a distant station in a poorly covered area (PYRA). The station NAGA, located above the Gorkha fault, sensed a southward displacement of almost 1.7 m. The PYRA station that is positioned at a distance of about 150 km from the fault, near the Pyramid station in the Everest, showed static displacements in the order of some millimeters. The observed displacements were compared with the calculated displacements of a finite fault model in an elastic halfspace. We evaluated two slips on fault models derived from seismological and geodetic studies: the comparison of the observed and modelled fields reveals that our displacements are in better accordance with the geodetic derived fault model than the seismologic one. Finally, we evaluate the yearly strain rate of four GNSS stations in the area that were recording continuously the deformation field for at least 5 years. The strain rate is then compared with the strain released by the Gorkha earthquake, leading to an interval of 235 years to store a comparable amount of elastic energy. The three near-fault GNSS stations require a slightly wider fault than published, in the case of an equivalent homogeneous rupture, with an average uniform slip of 3.5 m occurring on an area of 150 km × 60 km.

Real time health monitoring and control system methodology for flexible space structures

NASA Astrophysics Data System (ADS)

Jayaram, Sanjay

This dissertation is concerned with the Near Real-time Autonomous Health Monitoring of Flexible Space Structures. The dynamics of multi-body flexible systems is uncertain due to factors such as high non-linearity, consideration of higher modal frequencies, high dimensionality, multiple inputs and outputs, operational constraints, as well as unexpected failures of sensors and/or actuators. Hence a systematic framework of developing a high fidelity, dynamic model of a flexible structural system needs to be understood. The fault detection mechanism that will be an integrated part of an autonomous health monitoring system comprises the detection of abnormalities in the sensors and/or actuators and correcting these detected faults (if possible). Applying the robust control law and the robust measures that are capable of detecting and recovering/replacing the actuators rectifies the actuator faults. The fault tolerant concept applied to the sensors will be in the form of an Extended Kalman Filter (EKF). The EKF is going to weigh the information coming from multiple sensors (redundant sensors used to measure the same information) and automatically identify the faulty sensors and weigh the best estimate from the remaining sensors. The mechanization is comprised of instrumenting flexible deployable panels (solar array) with multiple angular position and rate sensors connected to the data acquisition system. The sensors will give position and rate information of the solar panel in all three axes (i.e. roll, pitch and yaw). The position data corresponds to the steady state response and the rate data will give better insight on the transient response of the system. This is a critical factor for real-time autonomous health monitoring. MATLAB (and/or C++) software will be used for high fidelity modeling and fault tolerant mechanism.

A search in strainmeter data for slow slip associated with triggered and ambient tremor near Parkfield, California

USGS Publications Warehouse

Smith, E.F.; Gomberg, J.

2009-01-01

We test the hypothesis that, as in subduction zones, slow slip facilitates triggered and ambient tremor in the transform boundary setting of California. Our study builds on the study of Peng et al. (2009) of triggered and ambient tremor near Parkfield, California during time intervals surrounding 31, potentially triggering, M ≥ 7.5 teleseismic earthquakes; waves from 10 of these triggered tremor and 29 occurred in periods of ambient tremor activity. We look for transient slow slip during 3-month windows that include 11 of these triggering and nontriggering teleseisms, using continuous strain data recorded on two borehole Gladwin tensor strainmeters (GTSM) located within the distribution of tremor epicenters. We model the GTSM data assuming only tidal and “drift” signals are present and find no detectable slow slip, either ongoing when the teleseismic waves passed or triggered by them. We infer a conservative detection threshold of about 5 nanostrain for abrupt changes and about twice this for slowly evolving signals. This could be lowered slightly by adding analyses of other data types, modeled slow slip signals, and GTSM data calibration. Detection of slow slip also depends on the slipping fault's location and size, which we describe in terms of equivalent earthquake moment magnitude, M. In the best case of the GTSM above a very shallow slipping fault, detectable slip events must exceed M~2, and if the slow slip is beneath the seismogenic zone (below ~15 km depth), even M~5 events are likely to remain hidden.

Synthetic Earthquake Statistics From Physical Fault Models for the Lower Rhine Embayment

NASA Astrophysics Data System (ADS)

Brietzke, G. B.; Hainzl, S.; Zöller, G.

2012-04-01

As of today, seismic risk and hazard estimates mostly use pure empirical, stochastic models of earthquake fault systems tuned specifically to the vulnerable areas of interest. Although such models allow for reasonable risk estimates they fail to provide a link between the observed seismicity and the underlying physical processes. Solving a state-of-the-art fully dynamic description set of all relevant physical processes related to earthquake fault systems is likely not useful since it comes with a large number of degrees of freedom, poor constraints on its model parameters and a huge computational effort. Here, quasi-static and quasi-dynamic physical fault simulators provide a compromise between physical completeness and computational affordability and aim at providing a link between basic physical concepts and statistics of seismicity. Within the framework of quasi-static and quasi-dynamic earthquake simulators we investigate a model of the Lower Rhine Embayment (LRE) that is based upon seismological and geological data. We present and discuss statistics of the spatio-temporal behavior of generated synthetic earthquake catalogs with respect to simplification (e.g. simple two-fault cases) as well as to complication (e.g. hidden faults, geometric complexity, heterogeneities of constitutive parameters).

Development of Hydrologic Characterization Technology of Fault Zones (in Japanese; English)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karasaki, Kenzi; Onishi, Tiemi; Wu, Yu-Shu

2008-03-31

Through an extensive literature survey we find that there is very limited amount of work on fault zone hydrology, particularly in the field using borehole testing. The common elements of a fault include a core, and damage zones. The core usually acts as a barrier to the flow across it, whereas the damage zone controls the flow either parallel to the strike or dip of a fault. In most of cases the damage zone isthe one that is controlling the flow in the fault zone and the surroundings. The permeability of damage zone is in the range of two tomore » three orders of magnitude higher than the protolith. The fault core can have permeability up to seven orders of magnitude lower than the damage zone. The fault types (normal, reverse, and strike-slip) by themselves do not appear to be a clear classifier of the hydrology of fault zones. However, there still remains a possibility that other additional geologic attributes and scaling relationships can be used to predict or bracket the range of hydrologic behavior of fault zones. AMT (Audio frequency Magneto Telluric) and seismic reflection techniques are often used to locate faults. Geochemical signatures and temperature distributions are often used to identify flow domains and/or directions. ALSM (Airborne Laser Swath Mapping) or LIDAR (Light Detection and Ranging) method may prove to be a powerful tool for identifying lineaments in place of the traditional photogrammetry. Nonetheless not much work has been done to characterize the hydrologic properties of faults by directly testing them using pump tests. There are some uncertainties involved in analyzing pressure transients of pump tests: both low permeability and high permeability faults exhibit similar pressure responses. A physically based conceptual and numerical model is presented for simulating fluid and heat flow and solute transport through fractured fault zones using a multiple-continuum medium approach. Data from the Horonobe URL site are analyzed to demonstrate the proposed approach and to examine the flow direction and magnitude on both sides of a suspected fault. We describe a strategy for effective characterization of fault zone hydrology. We recommend conducting a long term pump test followed by a long term buildup test. We do not recommend isolating the borehole into too many intervals. We do recommend ensuring durability and redundancy for long term monitoring.« less

A Hybrid Stochastic-Neuro-Fuzzy Model-Based System for In-Flight Gas Turbine Engine Diagnostics

DTIC Science & Technology

2001-04-05

Margin (ADM) and (ii) Fault Detection Margin (FDM). Key Words: ANFIS, Engine Health Monitoring , Gas Path Analysis, and Stochastic Analysis Adaptive Network...The paper illustrates the application of a hybrid Stochastic- Fuzzy -Inference Model-Based System (StoFIS) to fault diagnostics and prognostics for both...operational history monitored on-line by the engine health management (EHM) system. To capture the complex functional relationships between different

Classification of Aircraft Maneuvers for Fault Detection

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Clancy, Daniel (Technical Monitor)

2002-01-01

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data is a reasonable match to known examples of proper operation. In our domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. We explain where this subsystem fits into our envisioned fault detection system as well its experiments showing the promise of this classification subsystem.

Dynamics of delayed triggering in multi-segmented foreshock sequence: Evidence from the 2016 Kumamoto, Japan, earthquake

NASA Astrophysics Data System (ADS)

Arai, H.; Ando, R.; Aoki, Y.

2017-12-01

The 2016 Kumamoto earthquake sequence hit the SW Japan, from April 14th to 16th and its sequence includes two M6-class foreshocks and the main shock (Mw 7.0). Importantly, the detailed surface displacement caused solely by the two foreshocks could be captured by a SAR observation isolated from the mainshock deformation. The foreshocks ruptured the previously mapped Hinagu fault and their hypocentral locations and the aftershock distribution indicates the involvement of two different subparallel faults. Therefore we assumed that the 1st and the 2nd foreshocks respectively ruptured each of the subparallel faults (faults A and B). One of the interesting points of this earthquake is that the two major foreshocks had a temporal gap of 2.5 hours even though the fault A and B are quite close by each other. This suggests that the stress perturbation due to the 1st foreshock is not large enough to trigger the 2nd one right away but that it's large enough to bring about the following earthquake after a delay time.We aim to reproduce the foreshock sequence such as rupture jumping over the subparallel faults by using dynamic rupture simulations. We employed a spatiotemporal-boundary integral equation method accelerated by the Fast Domain Partitioning Method (Ando, 2016, GJI) since this method allows us to construct a complex fault geometry in 3D media. Our model has two faults and a free ground surface. We conducted rupture simulation with various sets of parameters to identify the optimal condition describing the observation.Our simulation results are roughly categorized into 3 cases with regard to the criticality for the rupture jumping. The case 1 (supercritical case) shows the fault A and B ruptured consecutively without any temporal gap. In the case 2 (nearly critical), the rupture on the fault B started with a temporal gap after the fault A finished rupturing, which is what we expected as a reproduction. In the case 3 (subcritical), only the fault A ruptured and its rupture did not transfer to the fault B. We succeed in reproducing rupture jumping over two faults with a temporal gap due to the nucleation by taking account of a velocity strengthening (direct) effect. With a detailed analysis of the case 2, we can constrain ranges of parameters strictly, and this gives us deeper insights into the physics underlying the delayed foreshock activity.

Effects of Channel Modification on Detection and Dating of Fault Scarps

NASA Astrophysics Data System (ADS)

Sare, R.; Hilley, G. E.

2016-12-01

Template matching of scarp-like features could potentially generate morphologic age estimates for individual scarps over entire regions, but data noise and scarp modification limits detection of fault scarps by this method. Template functions based on diffusion in the cross-scarp direction may fail to accurately date scarps near channel boundaries. Where channels reduce scarp amplitudes, or where cross-scarp noise is significant, signal-to-noise ratios decrease and the scarp may be poorly resolved. In this contribution, we explore the bias in morphologic age of a complex scarp produced by systematic changes in fault scarp curvature. For example, fault scarps may be modified by encroaching channel banks and mass failure, lateral diffusion of material into a channel, or undercutting parallel to the base of a scarp. We quantify such biases on morphologic age estimates using a block offset model subject to two-dimensional linear diffusion. We carry out a synthetic study of the effects of two-dimensional transport on morphologic age calculated using a profile model, and compare these results to a well- studied and constrained site along the San Andreas Fault at Wallace Creek, CA. This study serves as a first step towards defining regions of high confidence in template matching results based on scarp length, channel geometry, and near-scarp topography.

Novel Directional Protection Scheme for the FREEDM Smart Grid System

NASA Astrophysics Data System (ADS)

Sharma, Nitish

This research primarily deals with the design and validation of the protection system for a large scale meshed distribution system. The large scale system simulation (LSSS) is a system level PSCAD model which is used to validate component models for different time-scale platforms, to provide a virtual testing platform for the Future Renewable Electric Energy Delivery and Management (FREEDM) system. It is also used to validate the cases of power system protection, renewable energy integration and storage, and load profiles. The protection of the FREEDM system against any abnormal condition is one of the important tasks. The addition of distributed generation and power electronic based solid state transformer adds to the complexity of the protection. The FREEDM loop system has a fault current limiter and in addition, the Solid State Transformer (SST) limits the fault current at 2.0 per unit. Former students at ASU have developed the protection scheme using fiber-optic cable. However, during the NSF-FREEDM site visit, the National Science Foundation (NSF) team regarded the system incompatible for the long distances. Hence, a new protection scheme with a wireless scheme is presented in this thesis. The use of wireless communication is extended to protect the large scale meshed distributed generation from any fault. The trip signal generated by the pilot protection system is used to trigger the FID (fault isolation device) which is an electronic circuit breaker operation (switched off/opening the FIDs). The trip signal must be received and accepted by the SST, and it must block the SST operation immediately. A comprehensive protection system for the large scale meshed distribution system has been developed in PSCAD with the ability to quickly detect the faults. The validation of the protection system is performed by building a hardware model using commercial relays at the ASU power laboratory.

Along-strike variations of the partitioning of convergence across the Haiyuan fault system detected by InSAR

NASA Astrophysics Data System (ADS)

Daout, S.; Jolivet, R.; Lasserre, C.; Doin, M.-P.; Barbot, S.; Tapponnier, P.; Peltzer, G.; Socquet, A.; Sun, J.

2016-04-01

Oblique convergence across Tibet leads to slip partitioning with the coexistence of strike-slip, normal and thrust motion on major fault systems. A key point is to understand and model how faults interact and accumulate strain at depth. Here, we extract ground deformation across the Haiyuan Fault restraining bend, at the northeastern boundary of the Tibetan plateau, from Envisat radar data spanning the 2001-2011 period. We show that the complexity of the surface displacement field can be explained by the partitioning of a uniform deep-seated convergence. Mountains and sand dunes in the study area make the radar data processing challenging and require the latest developments in processing procedures for Synthetic Aperture Radar interferometry. The processing strategy is based on a small baseline approach. Before unwrapping, we correct for atmospheric phase delays from global atmospheric models and digital elevation model errors. A series of filtering steps is applied to improve the signal-to-noise ratio across high ranges of the Tibetan plateau and the phase unwrapping capability across the fault, required for reliable estimate of fault movement. We then jointly invert our InSAR time-series together with published GPS displacements to test a proposed long-term slip-partitioning model between the Haiyuan and Gulang left-lateral Faults and the Qilian Shan thrusts. We explore the geometry of the fault system at depth and associated slip rates using a Bayesian approach and test the consistency of present-day geodetic surface displacements with a long-term tectonic model. We determine a uniform convergence rate of 10 [8.6-11.5] mm yr-1 with an N89 [81-97]°E across the whole fault system, with a variable partitioning west and east of a major extensional fault-jog (the Tianzhu pull-apart basin). Our 2-D model of two profiles perpendicular to the fault system gives a quantitative understanding of how crustal deformation is accommodated by the various branches of this thrust/strike-slip fault system and demonstrates how the geometry of the Haiyuan fault system controls the partitioning of the deep secular motion.

Improving fault image by determination of optimum seismic survey parameters using ray-based modeling

NASA Astrophysics Data System (ADS)

Saffarzadeh, Sadegh; Javaherian, Abdolrahim; Hasani, Hossein; Talebi, Mohammad Ali

2018-06-01

In complex structures such as faults, salt domes and reefs, specifying the survey parameters is more challenging and critical owing to the complicated wave field behavior involved in such structures. In the petroleum industry, detecting faults has become crucial for reservoir potential where faults can act as traps for hydrocarbon. In this regard, seismic survey modeling is employed to construct a model close to the real structure, and obtain very realistic synthetic seismic data. Seismic modeling software, the velocity model and parameters pre-determined by conventional methods enable a seismic survey designer to run a shot-by-shot virtual survey operation. A reliable velocity model of structures can be constructed by integrating the 2D seismic data, geological reports and the well information. The effects of various survey designs can be investigated by the analysis of illumination maps and flower plots. Also, seismic processing of the synthetic data output can describe the target image using different survey parameters. Therefore, seismic modeling is one of the most economical ways to establish and test the optimum acquisition parameters to obtain the best image when dealing with complex geological structures. The primary objective of this study is to design a proper 3D seismic survey orientation to achieve fault zone structures through ray-tracing seismic modeling. The results prove that a seismic survey designer can enhance the image of fault planes in a seismic section by utilizing the proposed modeling and processing approach.

Sensor fault detection and isolation via high-gain observers: application to a double-pipe heat exchanger.

PubMed

Escobar, R F; Astorga-Zaragoza, C M; Téllez-Anguiano, A C; Juárez-Romero, D; Hernández, J A; Guerrero-Ramírez, G V

2011-07-01

This paper deals with fault detection and isolation (FDI) in sensors applied to a concentric-pipe counter-flow heat exchanger. The proposed FDI is based on the analytical redundancy implementing nonlinear high-gain observers which are used to generate residuals when a sensor fault is presented (as software sensors). By evaluating the generated residual, it is possible to switch between the sensor and the observer when a failure is detected. Experiments in a heat exchanger pilot validate the effectiveness of the approach. The FDI technique is easy to implement allowing the industries to have an excellent alternative tool to keep their heat transfer process under supervision. The main contribution of this work is based on a dynamic model with heat transfer coefficients which depend on temperature and flow used to estimate the output temperatures of a heat exchanger. This model provides a satisfactory approximation of the states of the heat exchanger in order to allow its implementation in a FDI system used to perform supervision tasks. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Development of a Fault Monitoring Technique for Wind Turbines Using a Hidden Markov Model.

PubMed

Shin, Sung-Hwan; Kim, SangRyul; Seo, Yun-Ho

2018-06-02

Regular inspection for the maintenance of the wind turbines is difficult because of their remote locations. For this reason, condition monitoring systems (CMSs) are typically installed to monitor their health condition. The purpose of this study is to propose a fault detection algorithm for the mechanical parts of the wind turbine. To this end, long-term vibration data were collected over two years by a CMS installed on a 3 MW wind turbine. The vibration distribution at a specific rotating speed of main shaft is approximated by the Weibull distribution and its cumulative distribution function is utilized for determining the threshold levels that indicate impending failure of mechanical parts. A Hidden Markov model (HMM) is employed to propose the statistical fault detection algorithm in the time domain and the method whereby the input sequence for HMM is extracted is also introduced by considering the threshold levels and the correlation between the signals. Finally, it was demonstrated that the proposed HMM algorithm achieved a greater than 95% detection success rate by using the long-term signals.

A distributed fault-tolerant signal processor /FTSP/

NASA Astrophysics Data System (ADS)

Bonneau, R. J.; Evett, R. C.; Young, M. J.

1980-01-01

A digital fault-tolerant signal processor (FTSP), an example of a self-repairing programmable system is analyzed. The design configuration is discussed in terms of fault tolerance, system-level fault detection, isolation and common memory. Special attention is given to the FDIR (fault detection isolation and reconfiguration) logic, noting that the reconfiguration decisions are based on configuration, summary status, end-around tests, and north marker/synchro data. Several mechanisms of fault detection are described which initiate reconfiguration at different levels. It is concluded that the reliability of a signal processor can be significantly enhanced by the use of fault-tolerant techniques.

Fiber Bragg grating sensor for fault detection in high voltage overhead transmission lines

NASA Astrophysics Data System (ADS)

Moghadas, Amin

2011-12-01

A fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by fiber Bragg grating (FBG) sensors. The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signals. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG sensors and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system.

Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

PubMed Central

Moghadas, Amin A.; Shadaram, Mehdi

2010-01-01

In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG). The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system. PMID:22163416

Using Fault Trees to Advance Understanding of Diagnostic Errors.

PubMed

Rogith, Deevakar; Iyengar, M Sriram; Singh, Hardeep

2017-11-01

Diagnostic errors annually affect at least 5% of adults in the outpatient setting in the United States. Formal analytic techniques are only infrequently used to understand them, in part because of the complexity of diagnostic processes and clinical work flows involved. In this article, diagnostic errors were modeled using fault tree analysis (FTA), a form of root cause analysis that has been successfully used in other high-complexity, high-risk contexts. How factors contributing to diagnostic errors can be systematically modeled by FTA to inform error understanding and error prevention is demonstrated. A team of three experts reviewed 10 published cases of diagnostic error and constructed fault trees. The fault trees were modeled according to currently available conceptual frameworks characterizing diagnostic error. The 10 trees were then synthesized into a single fault tree to identify common contributing factors and pathways leading to diagnostic error. FTA is a visual, structured, deductive approach that depicts the temporal sequence of events and their interactions in a formal logical hierarchy. The visual FTA enables easier understanding of causative processes and cognitive and system factors, as well as rapid identification of common pathways and interactions in a unified fashion. In addition, it enables calculation of empirical estimates for causative pathways. Thus, fault trees might provide a useful framework for both quantitative and qualitative analysis of diagnostic errors. Future directions include establishing validity and reliability by modeling a wider range of error cases, conducting quantitative evaluations, and undertaking deeper exploration of other FTA capabilities. Copyright © 2017 The Joint Commission. Published by Elsevier Inc. All rights reserved.

Modeling, Monitoring and Fault Diagnosis of Spacecraft Air Contaminants

NASA Technical Reports Server (NTRS)

Ramirez, W. Fred; Skliar, Mikhail; Narayan, Anand; Morgenthaler, George W.; Smith, Gerald J.

1996-01-01

Progress and results in the development of an integrated air quality modeling, monitoring, fault detection, and isolation system are presented. The focus was on development of distributed models of the air contaminants transport, the study of air quality monitoring techniques based on the model of transport process and on-line contaminant concentration measurements, and sensor placement. Different approaches to the modeling of spacecraft air contamination are discussed, and a three-dimensional distributed parameter air contaminant dispersion model applicable to both laminar and turbulent transport is proposed. A two-dimensional approximation of a full scale transport model is also proposed based on the spatial averaging of the three dimensional model over the least important space coordinate. A computer implementation of the transport model is considered and a detailed development of two- and three-dimensional models illustrated by contaminant transport simulation results is presented. The use of a well established Kalman filtering approach is suggested as a method for generating on-line contaminant concentration estimates based on both real time measurements and the model of contaminant transport process. It is shown that high computational requirements of the traditional Kalman filter can render difficult its real-time implementation for high-dimensional transport model and a novel implicit Kalman filtering algorithm is proposed which is shown to lead to an order of magnitude faster computer implementation in the case of air quality monitoring.

Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

PubMed Central

Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2011-01-01

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

The role of elasticity in simulating long-term tectonic extension

NASA Astrophysics Data System (ADS)

Olive, Jean-Arthur; Behn, Mark D.; Mittelstaedt, Eric; Ito, Garrett; Klein, Benjamin Z.

2016-05-01

While elasticity is a defining characteristic of the Earth's lithosphere, it is often ignored in numerical models of long-term tectonic processes in favour of a simpler viscoplastic description. Here we assess the consequences of this assumption on a well-studied geodynamic problem: the growth of normal faults at an extensional plate boundary. We conduct 2-D numerical simulations of extension in elastoplastic and viscoplastic layers using a finite difference, particle-in-cell numerical approach. Our models simulate a range of faulted layer thicknesses and extension rates, allowing us to quantify the role of elasticity on three key observables: fault-induced topography, fault rotation, and fault life span. In agreement with earlier studies, simulations carried out in elastoplastic layers produce rate-independent lithospheric flexure accompanied by rapid fault rotation and an inverse relationship between fault life span and faulted layer thickness. By contrast, models carried out with a viscoplastic lithosphere produce results that may qualitatively resemble the elastoplastic case, but depend strongly on the product of extension rate and layer viscosity U × ηL. When this product is high, fault growth initially generates little deformation of the footwall and hanging wall blocks, resulting in unrealistic, rigid block-offset in topography across the fault. This configuration progressively transitions into a regime where topographic decay associated with flexure is fully accommodated within the numerical domain. In addition, high U × ηL favours the sequential growth of multiple short-offset faults as opposed to a large-offset detachment. We interpret these results by comparing them to an analytical model for the fault-induced flexure of a thin viscous plate. The key to understanding the viscoplastic model results lies in the rate-dependence of the flexural wavelength of a viscous plate, and the strain rate dependence of the force increase associated with footwall and hanging wall bending. This behaviour produces unrealistic deformation patterns that can hinder the geological relevance of long-term rifting models that assume a viscoplastic rheology.

Long term seismic observation using ocean bottom seismographs in Marmara Sea, Turkey

NASA Astrophysics Data System (ADS)

Takahashi, N.; Pinar, A.; Kalafat, D.; Yamamoto, Y.; Citak, S.; Comoglu, M.; Çok, Ö.; Ogutcu, Z.; Suvarikli, M.; Tunc, S.; Gurbuz, C.; Ozel, N.; Kaneda, Y.

2015-12-01

The North Anatolian Fault crosses the Marmara Sea with a direction of E-W. There are many large earthquakes repeatedly along the fault with a linkage each other. Due to recent large eastern Aegean earthquake with M6, the Marmara Sea is the "blank zone". Japan and Turkey have a SATREPS collaborative study to clarify the structural characters, construct fault models, simulate the strong motion and tsunami, evaluate these risks with hazard maps and educate disaster prevention for local governments and residents. Our activity is one of the most basic studies, and the objectives are to clarify hypocenter locations, monitor the move, and construct fault models referring seismic/magnetotelluric structures, geodetic nature and trenching works. The target area is from western Marmara Sea to the off Istanbul area along the north Anatolian Fault. We deployed ten Ocean Bottom Seismographs (OBSs) between the Tekirdag Basin and the Central Basin in September, 2014. Then, we added five Japanese OBSs and deployed them at the western end of the Marmara Sea and the eastern Central Basin to extend observed area in March, 2015. The OBS has a three-component velocity sensor with a natural frequency of 4.5 Hz and a hydrophone. Japanese team have clarified seismicity around Japan using the OBS. The magnitude of the detected events is 1.0-1.5. We retrieved all 15 OBSs in July, 2015 and deployed them again on the same locations after data copy and battery maintenance. We started OBS data analysis combined with land stations data. Now we detect events automatically using these data and succeeded detection of over one thousand around the north Anatolian Fault. The tentative results show heterogeneous seismicity. The western and central basins have relative high seismicity and the seismogenic zone becomes thicker rather than previous estimation. Then we will evaluate hypocenter locations with high resolution and discuss the shape of faults in each segment and their linkage.

An uncertainty-based distributed fault detection mechanism for wireless sensor networks.

PubMed

Yang, Yang; Gao, Zhipeng; Zhou, Hang; Qiu, Xuesong

2014-04-25

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio.

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

NASA Technical Reports Server (NTRS)

2004-01-01

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Automatically generated acceptance test: A software reliability experiment

NASA Technical Reports Server (NTRS)

Protzel, Peter W.

1988-01-01

This study presents results of a software reliability experiment investigating the feasibility of a new error detection method. The method can be used as an acceptance test and is solely based on empirical data about the behavior of internal states of a program. The experimental design uses the existing environment of a multi-version experiment previously conducted at the NASA Langley Research Center, in which the launch interceptor problem is used as a model. This allows the controlled experimental investigation of versions with well-known single and multiple faults, and the availability of an oracle permits the determination of the error detection performance of the test. Fault interaction phenomena are observed that have an amplifying effect on the number of error occurrences. Preliminary results indicate that all faults examined so far are detected by the acceptance test. This shows promise for further investigations, and for the employment of this test method on other applications.

A nonlinear quality-related fault detection approach based on modified kernel partial least squares.

PubMed

Jiao, Jianfang; Zhao, Ning; Wang, Guang; Yin, Shen

2017-01-01

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Uncertainty quantification for evaluating the impacts of fracture zone on pressure build-up and ground surface uplift during geological CO₂ sequestration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bao, Jie; Hou, Zhangshuan; Fang, Yilin

2015-06-01

A series of numerical test cases reflecting broad and realistic ranges of geological formation and preexisting fault properties was developed to systematically evaluate the impacts of preexisting faults on pressure buildup and ground surface uplift during CO₂ injection. Numerical test cases were conducted using a coupled hydro-geomechanical simulator, eSTOMP (extreme-scale Subsurface Transport over Multiple Phases). For efficient sensitivity analysis and reliable construction of a reduced-order model, a quasi-Monte Carlo sampling method was applied to effectively sample a high-dimensional input parameter space to explore uncertainties associated with hydrologic, geologic, and geomechanical properties. The uncertainty quantification results show that the impacts onmore » geomechanical response from the pre-existing faults mainly depend on reservoir and fault permeability. When the fault permeability is two to three orders of magnitude smaller than the reservoir permeability, the fault can be considered as an impermeable block that resists fluid transport in the reservoir, which causes pressure increase near the fault. When the fault permeability is close to the reservoir permeability, or higher than 10⁻¹⁵ m² in this study, the fault can be considered as a conduit that penetrates the caprock, connecting the fluid flow between the reservoir and the upper rock.« less

ITER Side Correction Coil Quench model and analysis

NASA Astrophysics Data System (ADS)

Nicollet, S.; Bessette, D.; Ciazynski, D.; Duchateau, J. L.; Gauthier, F.; Lacroix, B.

2016-12-01

Previous thermohydraulic studies performed for the ITER TF, CS and PF magnet systems have brought some important information on the detection and consequences of a quench as a function of the initial conditions (deposited energy, heated length). Even if the temperature margin of the Correction Coils is high, their behavior during a quench should also be studied since a quench is likely to be triggered by potential anomalies in joints, ground fault on the instrumentation wires, etc. A model has been developed with the SuperMagnet Code (Bagnasco et al., 2010) for a Side Correction Coil (SCC2) with four pancakes cooled in parallel, each of them represented by a Thea module (with the proper Cable In Conduit Conductor characteristics). All the other coils of the PF cooling loop are hydraulically connected in parallel (top/bottom correction coils and six Poloidal Field Coils) are modeled by Flower modules with equivalent hydraulics properties. The model and the analysis results are presented for five quench initiation cases with/without fast discharge: two quenches initiated by a heat input to the innermost turn of one pancake (case 1 and case 2) and two other quenches initiated at the innermost turns of four pancakes (case 3 and case 4). In the 5th case, the quench is initiated at the middle turn of one pancake. The impact on the cooling circuit, e.g. the exceedance of the opening pressure of the quench relief valves, is detailed in case of an undetected quench (i.e. no discharge of the magnet). Particular attention is also paid to a possible secondary quench detection system based on measured thermohydraulic signals (pressure, temperature and/or helium mass flow rate). The maximum cable temperature achieved in case of a fast current discharge (primary detection by voltage) is compared to the design hot spot criterion of 150 K, which includes the contribution of helium and jacket.

Expert System Detects Power-Distribution Faults

NASA Technical Reports Server (NTRS)

Walters, Jerry L.; Quinn, Todd M.

1994-01-01

Autonomous Power Expert (APEX) computer program is prototype expert-system program detecting faults in electrical-power-distribution system. Assists human operators in diagnosing faults and deciding what adjustments or repairs needed for immediate recovery from faults or for maintenance to correct initially nonthreatening conditions that could develop into faults. Written in Lisp.

Erosion controls transpressional wedge kinematics

NASA Astrophysics Data System (ADS)

Leever, K. A.; Oncken, O.

2012-04-01

High resolution digital image analysis of analogue tectonic models reveals that erosion strongly influences the kinematics of brittle transpressional wedges. In the basally-driven experimental setup with low-angle transpression (convergence angle of 20 degrees) and a homogeneous brittle rheology, a doubly vergent wedge develops above the linear basal velocity discontinuity. In the erosive case, the experiment is interrupted and the wedge topography fully removed at displacement increments of ~3/4 the model thickness. The experiments are observed by a stereo pair of high resolution CCD cameras and the incremental displacement field calculated by Digital Particle Image Velocimetry (DPIV). From this dataset, fault slip on individual fault segments - magnitude and angle on the horizontal plane relative to the fault trace - is extracted using the method of Leever et al. (2011). In the non-erosive case, after an initial stage of strain localization, the wedge experiences two transient stages of (1) oblique slip and (2) localized strain partitioning. In the second stage, the fault slip angle on the pro-shear(s) rotates by some 30 degrees from oblique to near-orthogonal. Kinematic steady state is attained in the third stage when a through-going central strike-slip zone develops above the basal velocity discontinuity. In this stage, strain is localized on two main faults (or fault zones) and fully partitioned between plate boundary-parallel displacement on the central strike-slip zone and near-orthogonal reverse faulting at the front (pro-side) of the wedge. The fault slip angle on newly formed pro-shears in this stage is stable at 60-65 degrees (see also Leever et al., 2011). In contrast, in the erosive case, slip remains more oblique on the pro-shears throughout the experiment and a separate central strike-slip zone does not form, i.e. strain partitioning does not fully develop. In addition, more faults are active simultaneously. Definition of stages is based on slip on the retro-side of the wedge. In the first stage, the slip angle on the retro-shear is 27 +/- 12 degrees. In a subsequent stage, slip on the retro-side is partitioned between strike-slip and oblique (~35 degrees) faulting. In the third stage, the slip angle on the retro side stabilizes at ~10 degrees. The pro-shears are characterized by very different kinematics. Two pro-shears tend to be active simultaneously, the extinction of the older fault shortly followed by the initiation of a new one in a forelandward breaking sequence. Throughout the experiment, the fault slip on the pro-shears is 40-60 degrees at their initiation, gradually decreasing to nearly strike-slip at the moment of fault extinction. This is a rotation of similar magnitude but in the reverse direction compared to the non-erosive case. The fault planes themselves do not rotate. Leever, K. A., R. H. Gabrielsen, D. Sokoutis, and E. Willingshofer (2011), The effect of convergence angle on the kinematic evolution of strain partitioning in transpressional brittle wedges: Insight from analog modeling and high-resolution digital image analysis, Tectonics, 30(2), TC2013.

Distributed and localized horizontal tectonic deformation as inferred from drainage network geometry and topology: A case study from Lebanon

NASA Astrophysics Data System (ADS)

Goren, Liran; Castelltort, Sébastien; Klinger, Yann

2016-04-01

Partitioning of horizontal deformation between localized and distributed modes in regions of oblique tectonic convergence is, in many cases, hard to quantify. As a case study, we consider the Dead Sea Fault System that changes its orientation across Lebanon and forms a restraining bend. The oblique deformation along the Lebanese restraining bend is characterized by a complex suite of tectonic structures, among which, the Yammouneh fault, is believed to be the main strand that relays deformation from the southern section to the northern section of the Dead Sea Fault System. However, uncertainties regarding slip rates along the Yammouneh fault and strain partitioning in Lebanon still prevail. In the current work we use the geometry and topology of river basins together with numerical modeling to evaluate modes and rates of the horizontal deformation in Mount Lebanon that is associated with the Arabia-Sinai relative plate motion. We focus on river basins that drain Mount Lebanon to the Mediterranean and originate close to the Yammouneh fault. We quantify a systematic counterclockwise rotation of these basins and evaluate drainage area disequilibrium using an application of the χ mapping technique, which aims at estimating the degree of geometrical and topological disequilibrium in river networks. The analysis indicates a systematic spatial pattern whereby tributaries of the rotated basins appear to experience drainage area loss or gain with respect to channel length. A kinematic model that is informed by river basin geometry reveals that since the late Miocene, about a quarter of the relative plate motion parallel to the plate boundary has been distributed along a wide band of deformation to the west of the Yammouneh fault. Taken together with previous, shorter-term estimates, the model indicates little variation of slip rate along the Yammouneh fault since the late Miocene. Kinematic model results are compatible with late Miocene paleomagnetic rotations in western Mount Lebanon. A numerical landscape evolution experiment demonstrates the emergence of a similar χ pattern of drainage area disequilibrium in response to progressive distributed shear deformation of river basins with relatively minor drainage network reorganization.

Fault detection and isolation for complex system

NASA Astrophysics Data System (ADS)

Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

2017-07-01

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

A Study on Micropipetting Detection Technology of Automatic Enzyme Immunoassay Analyzer.

PubMed

Shang, Zhiwu; Zhou, Xiangping; Li, Cheng; Tsai, Sang-Bing

2018-04-10

In order to improve the accuracy and reliability of micropipetting, a method of micro-pipette detection and calibration combining the dynamic pressure monitoring in pipetting process and quantitative identification of pipette volume in image processing was proposed. Firstly, the normalized pressure model for the pipetting process was established with the kinematic model of the pipetting operation, and the pressure model is corrected by the experimental method. Through the pipetting process pressure and pressure of the first derivative of real-time monitoring, the use of segmentation of the double threshold method as pipetting fault evaluation criteria, and the pressure sensor data are processed by Kalman filtering, the accuracy of fault diagnosis is improved. When there is a fault, the pipette tip image is collected through the camera, extract the boundary of the liquid region by the background contrast method, and obtain the liquid volume in the tip according to the geometric characteristics of the pipette tip. The pipette deviation feedback to the automatic pipetting module and deviation correction is carried out. The titration test results show that the combination of the segmented pipetting kinematic model of the double threshold method of pressure monitoring, can effectively real-time judgment and classification of the pipette fault. The method of closed-loop adjustment of pipetting volume can effectively improve the accuracy and reliability of the pipetting system.

Simultaneous Event-Triggered Fault Detection and Estimation for Stochastic Systems Subject to Deception Attacks.

PubMed

Li, Yunji; Wu, QingE; Peng, Li

2018-01-23

In this paper, a synthesized design of fault-detection filter and fault estimator is considered for a class of discrete-time stochastic systems in the framework of event-triggered transmission scheme subject to unknown disturbances and deception attacks. A random variable obeying the Bernoulli distribution is employed to characterize the phenomena of the randomly occurring deception attacks. To achieve a fault-detection residual is only sensitive to faults while robust to disturbances, a coordinate transformation approach is exploited. This approach can transform the considered system into two subsystems and the unknown disturbances are removed from one of the subsystems. The gain of fault-detection filter is derived by minimizing an upper bound of filter error covariance. Meanwhile, system faults can be reconstructed by the remote fault estimator. An recursive approach is developed to obtain fault estimator gains as well as guarantee the fault estimator performance. Furthermore, the corresponding event-triggered sensor data transmission scheme is also presented for improving working-life of the wireless sensor node when measurement information are aperiodically transmitted. Finally, a scaled version of an industrial system consisting of local PC, remote estimator and wireless sensor node is used to experimentally evaluate the proposed theoretical results. In particular, a novel fault-alarming strategy is proposed so that the real-time capacity of fault-detection is guaranteed when the event condition is triggered.

S&T converging trends in dealing with disaster: A review on AI tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasan, Abu Bakar, E-mail: abakarh@usim.edu.my; Isa, Mohd Hafez Mohd.

Science and Technology (S&T) has been able to help mankind to solve or minimize problems when arise. Different methodologies, techniques and tools were developed or used for specific cases by researchers, engineers, scientists throughout the world, and numerous papers and articles have been written by them. Nine selected cases such as flash flood, earthquakes, workplace accident, fault in aircraft industry, seismic vulnerability, disaster mitigation and management, and early fault detection in nuclear industry have been studied. This paper looked at those cases, and their results showed nearly 60% uses artificial intelligence (AI) as a tool. This paper also did somemore » review that will help young researchers in deciding the types of AI tools to be selected; thus proving the future trends in S&T.« less

S&T converging trends in dealing with disaster: A review on AI tools

NASA Astrophysics Data System (ADS)

Hasan, Abu Bakar; Isa, Mohd. Hafez Mohd.

2016-01-01

Science and Technology (S&T) has been able to help mankind to solve or minimize problems when arise. Different methodologies, techniques and tools were developed or used for specific cases by researchers, engineers, scientists throughout the world, and numerous papers and articles have been written by them. Nine selected cases such as flash flood, earthquakes, workplace accident, fault in aircraft industry, seismic vulnerability, disaster mitigation and management, and early fault detection in nuclear industry have been studied. This paper looked at those cases, and their results showed nearly 60% uses artificial intelligence (AI) as a tool. This paper also did some review that will help young researchers in deciding the types of AI tools to be selected; thus proving the future trends in S&T.

Model-Based Fault Diagnosis for Turboshaft Engines

NASA Technical Reports Server (NTRS)

Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.

1998-01-01

Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

Functional Fault Modeling of a Cryogenic System for Real-Time Fault Detection and Isolation

NASA Technical Reports Server (NTRS)

Ferrell, Bob; Lewis, Mark; Oostdyk, Rebecca; Perotti, Jose

2009-01-01

When setting out to model and/or simulate a complex mechanical or electrical system, a modeler is faced with a vast array of tools, software, equations, algorithms and techniques that may individually or in concert aid in the development of the model. Mature requirements and a well understood purpose for the model may considerably shrink the field of possible tools and algorithms that will suit the modeling solution. Is the model intended to be used in an offline fashion or in real-time? On what platform does it need to execute? How long will the model be allowed to run before it outputs the desired parameters? What resolution is desired? Do the parameters need to be qualitative or quantitative? Is it more important to capture the physics or the function of the system in the model? Does the model need to produce simulated data? All these questions and more will drive the selection of the appropriate tools and algorithms, but the modeler must be diligent to bear in mind the final application throughout the modeling process to ensure the model meets its requirements without needless iterations of the design. The purpose of this paper is to describe the considerations and techniques used in the process of creating a functional fault model of a liquid hydrogen (LH2) system that will be used in a real-time environment to automatically detect and isolate failures.

A fault-tolerant control architecture for unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Drozeski, Graham R.

Research has presented several approaches to achieve varying degrees of fault-tolerance in unmanned aircraft. Approaches in reconfigurable flight control are generally divided into two categories: those which incorporate multiple non-adaptive controllers and switch between them based on the output of a fault detection and identification element, and those that employ a single adaptive controller capable of compensating for a variety of fault modes. Regardless of the approach for reconfigurable flight control, certain fault modes dictate system restructuring in order to prevent a catastrophic failure. System restructuring enables active control of actuation not employed by the nominal system to recover controllability of the aircraft. After system restructuring, continued operation requires the generation of flight paths that adhere to an altered flight envelope. The control architecture developed in this research employs a multi-tiered hierarchy to allow unmanned aircraft to generate and track safe flight paths despite the occurrence of potentially catastrophic faults. The hierarchical architecture increases the level of autonomy of the system by integrating five functionalities with the baseline system: fault detection and identification, active system restructuring, reconfigurable flight control; reconfigurable path planning, and mission adaptation. Fault detection and identification algorithms continually monitor aircraft performance and issue fault declarations. When the severity of a fault exceeds the capability of the baseline flight controller, active system restructuring expands the controllability of the aircraft using unconventional control strategies not exploited by the baseline controller. Each of the reconfigurable flight controllers and the baseline controller employ a proven adaptive neural network control strategy. A reconfigurable path planner employs an adaptive model of the vehicle to re-shape the desired flight path. Generation of the revised flight path is posed as a linear program constrained by the response of the degraded system. Finally, a mission adaptation component estimates limitations on the closed-loop performance of the aircraft and adjusts the aircraft mission accordingly. A combination of simulation and flight test results using two unmanned helicopters validates the utility of the hierarchical architecture.

Measurement and analysis of operating system fault tolerance

NASA Technical Reports Server (NTRS)

Lee, I.; Tang, D.; Iyer, R. K.

1992-01-01

This paper demonstrates a methodology to model and evaluate the fault tolerance characteristics of operational software. The methodology is illustrated through case studies on three different operating systems: the Tandem GUARDIAN fault-tolerant system, the VAX/VMS distributed system, and the IBM/MVS system. Measurements are made on these systems for substantial periods to collect software error and recovery data. In addition to investigating basic dependability characteristics such as major software problems and error distributions, we develop two levels of models to describe error and recovery processes inside an operating system and on multiple instances of an operating system running in a distributed environment. Based on the models, reward analysis is conducted to evaluate the loss of service due to software errors and the effect of the fault-tolerance techniques implemented in the systems. Software error correlation in multicomputer systems is also investigated.

Numerical modeling of fluid flow in a fault zone: a case of study from Majella Mountain (Italy).

NASA Astrophysics Data System (ADS)

Romano, Valentina; Battaglia, Maurizio; Bigi, Sabina; De'Haven Hyman, Jeffrey; Valocchi, Albert J.

2017-04-01

The study of fluid flow in fractured rocks plays a key role in reservoir management, including CO2 sequestration and waste isolation. We present a numerical model of fluid flow in a fault zone, based on field data acquired in Majella Mountain, in the Central Apennines (Italy). This fault zone is considered a good analogue for the massive presence of fluid migration in the form of tar. Faults are mechanical features and cause permeability heterogeneities in the upper crust, so they strongly influence fluid flow. The distribution of the main components (core, damage zone) can lead the fault zone to act as a conduit, a barrier, or a combined conduit-barrier system. We integrated existing information and our own structural surveys of the area to better identify the major fault features (e.g., type of fractures, statistical properties, geometrical and petro-physical characteristics). In our model the damage zones of the fault are described as discretely fractured medium, while the core of the fault as a porous one. Our model utilizes the dfnWorks code, a parallelized computational suite, developed at Los Alamos National Laboratory (LANL), that generates three dimensional Discrete Fracture Network (DFN) of the damage zones of the fault and characterizes its hydraulic parameters. The challenge of the study is the coupling between the discrete domain of the damage zones and the continuum one of the core. The field investigations and the basic computational workflow will be described, along with preliminary results of fluid flow simulation at the scale of the fault.

The electrical resistivity signature of a fault controlling gold mineralization and the implications for Mesozoic mineralization: a case study from the Jiaojia Fault, eastern China

NASA Astrophysics Data System (ADS)

Zhang, Kun; Lü, Qingtian; Yan, Jiayong; Hu, Hao; Fu, GuangMing

2017-08-01

We use 3D audio magnetotelluric method to the south segment of Jiaojia fault belt, and obtain the 3D electrical model of this area. Regional geophysical data were combined in an analysis of strata and major structural distribution in the study area, and included the southern segment of the Jiaojia fault zone transformed into two fault assemblages. Together with the previous studies of the ore-controlling action of the Jiaojia fault belt and deposit characteristics, the two faults are considered to be favorable metallogenic provinces, because some important features coupled with them, such as the subordinate fault intersection zone and several fault assemblages in one fault zone. It was also suggested the control action of later fault with reversed downthrows to the ore distribution. These studies have enabled us to predict the presence of two likely target regions of mineralization, and are prospecting breakthrough in the southern section of Jiaojia in the Shandong Peninsula, China.

Tectonics earthquake distribution pattern analysis based focal mechanisms (Case study Sulawesi Island, 1993–2012)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ismullah M, Muh. Fawzy, E-mail: mallaniung@gmail.com; Lantu,; Aswad, Sabrianto

Indonesia is the meeting zone between three world main plates: Eurasian Plate, Pacific Plate, and Indo – Australia Plate. Therefore, Indonesia has a high seismicity degree. Sulawesi is one of whose high seismicity level. The earthquake centre lies in fault zone so the earthquake data gives tectonic visualization in a certain place. This research purpose is to identify Sulawesi tectonic model by using earthquake data from 1993 to 2012. Data used in this research is the earthquake data which consist of: the origin time, the epicenter coordinate, the depth, the magnitude and the fault parameter (strike, dip and slip). Themore » result of research shows that there are a lot of active structures as a reason of the earthquake in Sulawesi. The active structures are Walannae Fault, Lawanopo Fault, Matano Fault, Palu – Koro Fault, Batui Fault and Moluccas Sea Double Subduction. The focal mechanism also shows that Walannae Fault, Batui Fault and Moluccas Sea Double Subduction are kind of reverse fault. While Lawanopo Fault, Matano Fault and Palu – Koro Fault are kind of strike slip fault.« less

Driving Processes of Earthquake Swarms: Evidence from High Resolution Seismicity

NASA Astrophysics Data System (ADS)

Ellsworth, W. L.; Shelly, D. R.; Hill, D. P.; Hardebeck, J.; Hsieh, P. A.

2017-12-01

Earthquake swarms are transient increases in seismicity deviating from a typical mainshock-aftershock pattern. Swarms are most prevalent in volcanic and hydrothermal areas, yet also occur in other environments, such as extensional fault stepovers. Swarms provide a valuable opportunity to investigate source zone physics, including the causes of their swarm-like behavior. To gain insight into this behavior, we have used waveform-based methods to greatly enhance standard seismic catalogs. Depending on the application, we detect and precisely relocate 2-10x as many events as included in the initial catalog. Recently, we have added characterization of focal mechanisms (applied to a 2014 swarm in Long Valley Caldera, California), addressing a common shortcoming in microseismicity analyses (Shelly et al., JGR, 2016). In analysis of multiple swarms (both within and outside volcanic areas), several features stand out, including: (1) dramatic expansion of the active source region with time, (2) tendency for events to occur on the immediate fringe of prior activity, (3) overall upward migration, and (4) complex faulting structure. Some swarms also show an apparent mismatch between seismicity orientations (as defined by patterns in hypocentral locations) and slip orientations (as inferred from focal mechanisms). These features are largely distinct from those observed in mainshock-aftershock sequences. In combination, these swarm behaviors point to an important role for fluid pressure diffusion. Swarms may in fact be generated by a cascade of fluid pressure diffusion and stress transfer: in cases where faults are critically stressed, an increase in fluid pressure will trigger faulting. Faulting will in turn dramatically increase permeability in the faulted area, allowing rapid equilibration of fluid pressure to the fringe of the rupture zone. This process may perpetuate until fluid pressure perturbations drop and/or stresses become further from failure, such that any perturbation (fluid + stress transfer) is insufficient to generate further faulting. Numerical modeling supports this hypothesis - for example, the main features of the 2014 Long Valley swarm can be reproduced by a relatively simple model incorporating both stress transfer and rupture-aided fluid pressure diffusion (Hsieh et al., AGU FM, 2016).

Linking fault pattern with groundwater flow in crystalline rocks at the Grimsel Test Site (Switzerland)

NASA Astrophysics Data System (ADS)

Schneeberger, Raphael; Berger, Alfons; Mäder, Urs K.; Niklaus Waber, H.; Kober, Florian; Herwegh, Marco

2017-04-01

Water flow across crystalline bedrock is of major interest for deep-seated geothermal energy projects as well as for underground disposal of radioactive waste. In crystalline rocks enhanced fluid flow is related to zones of increased permeability, i.e. to fractures that are associated to fault zones. The flow regime around the Grimsel Test Site (GTS, Central Aar massif) was assessed by establishing a 3D fault zone pattern on a local scale in the GTS underground facility (deca-meter scale) and on a regional scale at the surface (km-scale). The study reveals the existence of a dense fault zone network consisting of several km long and few tens of cm to meter wide, sub-vertically oriented major faults that are connected by tens to hundreds of meters long minor bridging faults. This geometrical information was used as input for the generation of a 3D fault zone network model. The faults originate from ductile shear zones that were reactivated as brittle faults under retrograde conditions during exhumation. Embrittlement and associated dilatancy along the faults provide the pathways for today's groundwater flow. Detection of the actual 3D flow paths is, however, challenging since flow seem to be not planar but rather tube-like. Two strategies are applied to constrain the 3D geometry of the flow tubes: (i) Characterization of the groundwater infiltrating into the GTS (location, yield, hydraulic head, and chemical composition) and (ii) stress modelling on the base of the 3D structural model to unravel potential domains of enhanced fluid flow such as fault plane intersections and domains of dilatancy. At the Grimsel Test Site, hydraulic and structural data demonstrate that the groundwater flow is head-driven from the surface towards the GTS located some 450 m below the surface. The residence time of the groundwater in this surface-near section is >60 years as evidenced by absence of detectable tritium. However, hydraulic heads obtained from interval pressure measurements within boreholes are variable and do not correspond to the overburden above the interval. Underground mapping revealed close spatial relation between water inflow points and faults, major water inflows occur in strongly deformed areas of the GTS. Furthermore, persistent differences in the groundwater chemical composition between infiltration points indicate that connectivity between different water flow paths is poor. Different findings indicate complex flow path geometries. However, domains of enhanced dilatancy and domains with increased number of fault intersections correlate with areas in the underground with 'high' water inflow.

Maneuver Classification for Aircraft Fault Detection

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.

2003-01-01

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, identifying all possible faulty and proper operating modes is clearly impossible. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.

Classification of Aircraft Maneuvers for Fault Detection

NASA Technical Reports Server (NTRS)

Oza, Nikunj; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Koga, Dennis (Technical Monitor)

2002-01-01

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.

An Uncertainty-Based Distributed Fault Detection Mechanism for Wireless Sensor Networks

PubMed Central

Yang, Yang; Gao, Zhipeng; Zhou, Hang; Qiu, Xuesong

2014-01-01

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio. PMID:24776937

Recent Improvements to the Finite-Fault Rupture Detector Algorithm: FinDer II

NASA Astrophysics Data System (ADS)

Smith, D.; Boese, M.; Heaton, T. H.

2015-12-01

Constraining the finite-fault rupture extent and azimuth is crucial for accurately estimating ground-motion in large earthquakes. Detecting and modeling finite-fault ruptures in real-time is thus essential to both earthquake early warning (EEW) and rapid emergency response. Following extensive real-time and offline testing, the finite-fault rupture detector algorithm, FinDer (Böse et al., 2012 & 2015), was successfully integrated into the California-wide ShakeAlert EEW demonstration system. Since April 2015, FinDer has been scanning real-time waveform data from approximately 420 strong-motion stations in California for peak ground acceleration (PGA) patterns indicative of earthquakes. FinDer analyzes strong-motion data by comparing spatial images of observed PGA with theoretical templates modeled from empirical ground-motion prediction equations (GMPEs). If the correlation between the observed and theoretical PGA is sufficiently high, a report is sent to ShakeAlert including the estimated centroid position, length, and strike, and their uncertainties, of an ongoing fault rupture. Rupture estimates are continuously updated as new data arrives. As part of a joint effort between USGS Menlo Park, ETH Zurich, and Caltech, we have rewritten FinDer in C++ to obtain a faster and more flexible implementation. One new feature of FinDer II is that multiple contour lines of high-frequency PGA are computed and correlated with templates, allowing the detection of both large earthquakes and much smaller (~ M3.5) events shortly after their nucleation. Unlike previous EEW algorithms, FinDer II thus provides a modeling approach for both small-magnitude point-source and larger-magnitude finite-fault ruptures with consistent error estimates for the entire event magnitude range.

Modeling Of Spontaneous Multiscale Roughening And Branching of Ruptures Propagating On A Slip-Weakening Frictional Fault

NASA Astrophysics Data System (ADS)

Elbanna, A. E.

2013-12-01

Numerous field and experimental observations suggest that faults surfaces are rough at multiple scales and tend to produce a wide range of branch sizes ranging from micro-branching to large scale secondary faults. The development and evolution of fault roughness and branching is believed to play an important role in rupture dynamics and energy partitioning. Previous work by several groups has succeeded in determining conditions under which a main rupture may branch into a secondary fault. Recently, there great progress has been made in investigating rupture propagation on rough faults with and without off-fault plasticity. Nonetheless, in most of these models the heterogeneity, whether the roughness profile or the secondary faults orientation, was built into the system from the beginning and consequently the final outcome depends strongly on the initial conditions. Here we introduce an adaptive mesh technique for modeling mode-II crack propagation on slip weakening frictional interfaces. We use a Finite Element Framework with random mesh topology that adapts to crack dynamics through element splitting and sequential insertion of frictional interfaces dictated by the failure criterion. This allows the crack path to explore non-planar paths and develop the roughness profile that is most compatible with the dynamical constraints. It also enables crack branching at different scales. We quantify energy dissipation due to the roughening process and small scale branching. We compare the results of our model to a reference case for propagation on a planar fault. We show that the small scale processes of roughening and branching influence many characteristics of the rupture propagation including the energy partitioning, rupture speed and peak slip rates. We also estimate the fracture energy required for propagating a crack on a planar fault that will be required to produce comparable results. We anticipate that this modeling approach provides an attractive methodology that complements the current efforts in modeling off-fault plasticity and damage.

Study of fault tolerant software technology for dynamic systems

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Zacharias, G. L.

1985-01-01

The major aim of this study is to investigate the feasibility of using systems-based failure detection isolation and compensation (FDIC) techniques in building fault-tolerant software and extending them, whenever possible, to the domain of software fault tolerance. First, it is shown that systems-based FDIC methods can be extended to develop software error detection techniques by using system models for software modules. In particular, it is demonstrated that systems-based FDIC techniques can yield consistency checks that are easier to implement than acceptance tests based on software specifications. Next, it is shown that systems-based failure compensation techniques can be generalized to the domain of software fault tolerance in developing software error recovery procedures. Finally, the feasibility of using fault-tolerant software in flight software is investigated. In particular, possible system and version instabilities, and functional performance degradation that may occur in N-Version programming applications to flight software are illustrated. Finally, a comparative analysis of N-Version and recovery block techniques in the context of generic blocks in flight software is presented.

Major Fault Patterns in Zanjan State of Iran Based of GECO Global Geoid Model

NASA Astrophysics Data System (ADS)

Beheshty, Sayyed Amir Hossein; Abrari Vajari, Mohammad; Raoufikelachayeh, SeyedehSusan

2016-04-01

A new Earth Gravitational Model (GECO) to degree 2190 has been developed incorporates EGM2008 and the latest GOCE based satellite solutions. Satellite gradiometry data are more sensitive information of the long- and medium- wavelengths of the gravity field than the conventional satellite tracking data. Hence, by utilizing this new technique, more accurate, reliable and higher degrees/orders of the spherical harmonic expansion of the gravity field can be achieved. Gravity gradients can also be useful in geophysical interpretation and prospecting. We have presented the concept of gravity gradients with some simple interpretations. A MATLAB based computer programs were developed and utilized for determining the gravity and gradient components of the gravity field using the GGMs, followed by a case study in Zanjan State of Iran. Our numerical studies show strong (more than 72%) correlations between gravity anomalies and the diagonal elements of the gradient tensor. Also, strong correlations were revealed between the components of the deflection of vertical and the off-diagonal elements as well as between the horizontal gradient and magnitude of the deflection of vertical. We clearly distinguished two big faults in North and South of Zanjan city based on the current information. Also, several minor faults were detected in the study area. Therefore, the same geophysical interpretation can be stated for gravity gradient components too. Our mathematical derivations support some of these correlations.

Localizing Submarine Earthquakes by Listening to the Water Reverberations

NASA Astrophysics Data System (ADS)

Castillo, J.; Zhan, Z.; Wu, W.

2017-12-01

Mid-Ocean Ridge (MOR) earthquakes generally occur far from any land based station and are of moderate magnitude, making it complicated to detect and in most cases, locate accurately. This limits our understanding of how MOR normal and transform faults move and the manner in which they slip. Different from continental events, seismic records from earthquakes occurring beneath the ocean floor show complex reverberations caused by P-wave energy trapped in the water column that are highly dependent of the source location and the efficiency to which energy propagated to the near-source surface. These later arrivals are commonly considered to be only a nuisance as they might sometimes interfere with the primary arrivals. However, in this study, we take advantage of the wavefield's high sensitivity to small changes in the seafloor topography and the present-day availability of worldwide multi-beam bathymetry to relocate submarine earthquakes by modeling these water column reverberations in teleseismic signals. Using a three-dimensional hybrid method for modeling body wave arrivals, we demonstrate that an accurate hypocentral location of a submarine earthquake (<5 km) can be achieved if the structural complexities near the source region are appropriately accounted for. This presents a novel way of studying earthquake source properties and will serve as a means to explore the influence of physical fault structure on the seismic behavior of transform faults.

A case study on pseudo 3-D Chirp sub-bottom profiler (SBP) survey for the detection of a fault trace in shallow sedimentary layers at gas hydrate site in the Ulleung Basin, East Sea

NASA Astrophysics Data System (ADS)

Kim, Young-Jun; Koo, Nam-Hyung; Cheong, Snons; Kim, Jung-Ki; Chun, Jong-Hwa; Shin, Sung-Ryul; Riedel, Michael; Lee, Ho-Young

2016-10-01

A pseudo 3-D Chirp sub-bottom profiler (SBP) survey was conducted to define the extension of a fault that was previously identified on low-resolution 2-D seismic data with an emphasis on the shallow sedimentary layers and to determine if the fault extends to the seafloor. The geophysical survey was conducted as part of an environmental impact assessment for a proposed gas hydrate production test in the Ulleung Basin, East Sea. The Chirp SBP raw data were acquired over an area of 1 km × 1 km with an average line spacing of 20 m. To produce a 3-D Chirp SBP volume, we developed an optimal processing sequence that was divided into two steps. The first phase of 2-D data processing included a sweep signature estimation, correlation, deconvolution, swell effect correction, and migration. The second phase of 3-D data processing was composed of a bin design, bin gathering of the final processed 2-D data set, amplitude normalization, and residual statics correction. The 3-D Chirp SBP volume provides enhanced imaging especially due to the residual static processing using a moving average method and shows better continuity of the sedimentary layers and consistency of the reflection events than the individual 2-D lines. Deformation of the seafloor as a result of the fault was detected, and the fault offset increases in the deeper sedimentary layers. We also determined that the fault strikes northwest-southeast. However, the shallow sub-seafloor sediments have high porosities and therefore do not exhibit brittle fault-behavior but rather deform continuously due to fault movement.

An Efficient Silent Data Corruption Detection Method with Error-Feedback Control and Even Sampling for HPC Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di, Sheng; Berrocal, Eduardo; Cappello, Franck

The silent data corruption (SDC) problem is attracting more and more attentions because it is expected to have a great impact on exascale HPC applications. SDC faults are hazardous in that they pass unnoticed by hardware and can lead to wrong computation results. In this work, we formulate SDC detection as a runtime one-step-ahead prediction method, leveraging multiple linear prediction methods in order to improve the detection results. The contributions are twofold: (1) we propose an error feedback control model that can reduce the prediction errors for different linear prediction methods, and (2) we propose a spatial-data-based even-sampling method tomore » minimize the detection overheads (including memory and computation cost). We implement our algorithms in the fault tolerance interface, a fault tolerance library with multiple checkpoint levels, such that users can conveniently protect their HPC applications against both SDC errors and fail-stop errors. We evaluate our approach by using large-scale traces from well-known, large-scale HPC applications, as well as by running those HPC applications on a real cluster environment. Experiments show that our error feedback control model can improve detection sensitivity by 34-189% for bit-flip memory errors injected with the bit positions in the range [20,30], without any degradation on detection accuracy. Furthermore, memory size can be reduced by 33% with our spatial-data even-sampling method, with only a slight and graceful degradation in the detection sensitivity.« less

Gyro-based Maximum-Likelihood Thruster Fault Detection and Identification

NASA Technical Reports Server (NTRS)

Wilson, Edward; Lages, Chris; Mah, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

When building smaller, less expensive spacecraft, there is a need for intelligent fault tolerance vs. increased hardware redundancy. If fault tolerance can be achieved using existing navigation sensors, cost and vehicle complexity can be reduced. A maximum likelihood-based approach to thruster fault detection and identification (FDI) for spacecraft is developed here and applied in simulation to the X-38 space vehicle. The system uses only gyro signals to detect and identify hard, abrupt, single and multiple jet on- and off-failures. Faults are detected within one second and identified within one to five accords,

Self-induced seismicity due to fluid circulation along faults

NASA Astrophysics Data System (ADS)

Aochi, Hideo; Poisson, Blanche; Toussaint, Renaud; Rachez, Xavier; Schmittbuhl, Jean

2014-03-01

In this paper, we develop a system of equations describing fluid migration, fault rheology, fault thickness evolution and shear rupture during a seismic cycle, triggered either by tectonic loading or by fluid injection. Assuming that the phenomena predominantly take place on a single fault described as a finite permeable zone of variable width, we are able to project the equations within the volumetric fault core onto the 2-D fault interface. From the basis of this `fault lubrication approximation', we simulate the evolution of seismicity when fluid is injected at one point along the fault to model-induced seismicity during an injection test in a borehole that intercepts the fault. We perform several parametric studies to understand the basic behaviour of the system. Fluid transmissivity and fault rheology are key elements. The simulated seismicity generally tends to rapidly evolve after triggering, independently of the injection history and end when the stationary path of fluid flow is established at the outer boundary of the model. This self-induced seismicity takes place in the case where shear rupturing on a planar fault becomes dominant over the fluid migration process. On the contrary, if healing processes take place, so that the fluid mass is trapped along the fault, rupturing occurs continuously during the injection period. Seismicity and fluid migration are strongly influenced by the injection rate and the heterogeneity.

Dynamic rupture modeling of the transition from thrust to strike-slip motion in the 2002 Denali fault earthquake, Alaska

USGS Publications Warehouse

Aagaard, Brad T.; Anderson, G.; Hudnut, K.W.

2004-01-01

We use three-dimensional dynamic (spontaneous) rupture models to investigate the nearly simultaneous ruptures of the Susitna Glacier thrust fault and the Denali strike-slip fault. With the 1957 Mw 8.3 Gobi-Altay, Mongolia, earthquake as the only other well-documented case of significant, nearly simultaneous rupture of both thrust and strike-slip faults, this feature of the 2002 Denali fault earthquake provides a unique opportunity to investigate the mechanisms responsible for development of these large, complex events. We find that the geometry of the faults and the orientation of the regional stress field caused slip on the Susitna Glacier fault to load the Denali fault. Several different stress orientations with oblique right-lateral motion on the Susitna Glacier fault replicate the triggering of rupture on the Denali fault about 10 sec after the rupture nucleates on the Susitna Glacier fault. However, generating slip directions compatible with measured surface offsets and kinematic source inversions requires perturbing the stress orientation from that determined with focal mechanisms of regional events. Adjusting the vertical component of the principal stress tensor for the regional stress field so that it is more consistent with a mixture of strike-slip and reverse faulting significantly improves the fit of the slip-rake angles to the data. Rotating the maximum horizontal compressive stress direction westward appears to improve the fit even further.

Quantifying Vertical Exhumation in Intracontinental Strike-Slip Faults: the Garlock fault zone, southern California

NASA Astrophysics Data System (ADS)

Chinn, L.; Blythe, A. E.; Fendick, A.

2012-12-01

New apatite fission-track ages show varying rates of vertical exhumation at the eastern terminus of the Garlock fault zone. The Garlock fault zone is a 260 km long east-northeast striking strike-slip fault with as much as 64 km of sinistral offset. The Garlock fault zone terminates in the east in the Avawatz Mountains, at the intersection with the dextral Southern Death Valley fault zone. Although motion along the Garlock fault west of the Avawatz Mountains is considered purely strike-slip, uplift and exhumation of bedrock in the Avawatz Mountains south of the Garlock fault, as recently as 5 Ma, indicates that transpression plays an important role at this location and is perhaps related to a restricting bend as the fault wraps around and terminates southeastward along the Avawatz Mountains. In this study we complement extant thermochronometric ages from within the Avawatz core with new low temperature fission-track ages from samples collected within the adjacent Garlock and Southern Death Valley fault zones. These thermochronometric data indicate that vertical exhumation rates vary within the fault zone. Two Miocene ages (10.2 (+5.0/-3.4) Ma, 9.0 (+2.2/-1.8) Ma) indicate at least ~3.3 km of vertical exhumation at ~0.35 mm/yr, assuming a 30°C/km geothermal gradient, along a 2 km transect parallel and adjacent to the Mule Spring fault. An older Eocene age (42.9 (+8.7/-7.3) Ma) indicates ~3.3 km of vertical exhumation at ~0.08 mm/yr. These results are consistent with published exhumation rates of 0.35 mm/yr between ~7 and ~4 Ma and 0.13 mm/yr between ~15 and ~9 Ma, as determined by apatite fission-track and U-Th/He thermochronometry in the hanging-wall of the Mule Spring fault. Similar exhumation rates on both sides of the Mule Spring fault support three separate models: 1) Thrusting is no longer active along the Mule Spring fault, 2) Faulting is dominantly strike-slip at the sample locations, or 3) Miocene-present uplift and exhumation is below detection levels using apatite fission-track thermochronometry. In model #1 slip on the Mule Spring fault may have propagated towards the range front, and may be responsible for the fault-propagation-folding currently observed along the northern branch of the Southern Death Valley fault zone. Model #2 may serve to determine where faulting has historically included a component of thrust faulting to the east of sample locations. Model #3 would further determine total offset along the Mule Spring fault from Miocene-present. Anticipated fission-track and U-Th/He data will help distinguish between these alternative models.

Subsurface structure identification of active fault based on magnetic anomaly data (Case study: Toru fault in Sumatera fault system)

NASA Astrophysics Data System (ADS)

Simanjuntak, Andrean V. H.; Husni, Muhammad; Syirojudin, Muhammad

2017-07-01

Toru segment, which is one of the active faults and located in the North of Sumatra, broke in 1984 ago on Pahae Jahe's earthquake with a magnitude 6.4 at the northern part of the fault which has a length of 23 km, and also broke again at the same place in 2008. The event of recurrence is very fast, which only 25 years old have repeatedly returned. However, in the elastic rebound theory, it probably happen with a fracture 50 cm and an average of the shear velocity 20 mm/year. The average focus of the earthquake sourced at a depth of 10 km and 23 km along its fracture zones, which can generate enough shaking 7 MMI and could breaking down buildings and create landslides on the cliff. Due to its seismic activity, this study was made to identify the effectiveness of this fault with geophysical methods. Geophysical methods such as gravity, geomagnetic and seismology are powerful tools for detecting subsurface structures of local, regional as well as of global scales. This study used to geophysical methods to discuss about total intensity of the geomagnetic anomaly data, resulted in the distribution of susceptibility values corresponding to the fault movement. The geomagnetic anomalies data was obtained from Geomag, such as total intensity measured by satellite. Data acquisition have been corrected for diurnal variations and reduced by IGRF. The study of earthquake records can be used for differentiating the active and non active fault elements. Modeling has been done using several methods, such as pseudo-gravity, reduce to pole, and upward or downward continuation, which is used to filter the geomagnetic anomaly data because the data has not fully representative of the fault structure. The results indicate that rock layers of 0 - 100 km depth encountered the process of intrusion and are dominated by sedimentary rocks that are paramagnetic, and that the ones of 100 - 150 km depth experienced the activity of subducting slab consisting of basalt and granite which are ferromagnetic and semi-ferromagnetic. This concluded that all the occurences correspond to the high seismicity and seismotectonic condition of Toru fault.

A Non-linear Geodetic Data Inversion Using ABIC for Slip Distribution on a Fault With an Unknown dip Angle

NASA Astrophysics Data System (ADS)

Fukahata, Y.; Wright, T. J.

2006-12-01

We developed a method of geodetic data inversion for slip distribution on a fault with an unknown dip angle. When fault geometry is unknown, the problem of geodetic data inversion is non-linear. A common strategy for obtaining slip distribution is to first determine the fault geometry by minimizing the square misfit under the assumption of a uniform slip on a rectangular fault, and then apply the usual linear inversion technique to estimate a slip distribution on the determined fault. It is not guaranteed, however, that the fault determined under the assumption of a uniform slip gives the best fault geometry for a spatially variable slip distribution. In addition, in obtaining a uniform slip fault model, we have to simultaneously determine the values of the nine mutually dependent parameters, which is a highly non-linear, complicated process. Although the inverse problem is non-linear for cases with unknown fault geometries, the non-linearity of the problems is actually weak, when we can assume the fault surface to be flat. In particular, when a clear fault trace is observed on the EarthOs surface after an earthquake, we can precisely estimate the strike and the location of the fault. In this case only the dip angle has large ambiguity. In geodetic data inversion we usually need to introduce smoothness constraints in order to compromise reciprocal requirements for model resolution and estimation errors in a natural way. Strictly speaking, the inverse problem with smoothness constraints is also non-linear, even if the fault geometry is known. The non-linearity has been dissolved by introducing AkaikeOs Bayesian Information Criterion (ABIC), with which the optimal value of the relative weight of observed data to smoothness constraints is objectively determined. In this study, using ABIC in determining the optimal dip angle, we dissolved the non-linearity of the inverse problem. We applied the method to the InSAR data of the 1995 Dinar, Turkey earthquake and obtained a much shallower dip angle than before.

Comparison of Fault Detection Algorithms for Real-time Diagnosis in Large-Scale System. Appendix E

NASA Technical Reports Server (NTRS)

Kirubarajan, Thiagalingam; Malepati, Venkat; Deb, Somnath; Ying, Jie

2001-01-01

In this paper, we present a review of different real-time capable algorithms to detect and isolate component failures in large-scale systems in the presence of inaccurate test results. A sequence of imperfect test results (as a row vector of I's and O's) are available to the algorithms. In this case, the problem is to recover the uncorrupted test result vector and match it to one of the rows in the test dictionary, which in turn will isolate the faults. In order to recover the uncorrupted test result vector, one needs the accuracy of each test. That is, its detection and false alarm probabilities are required. In this problem, their true values are not known and, therefore, have to be estimated online. Other major aspects in this problem are the large-scale nature and the real-time capability requirement. Test dictionaries of sizes up to 1000 x 1000 are to be handled. That is, results from 1000 tests measuring the state of 1000 components are available. However, at any time, only 10-20% of the test results are available. Then, the objective becomes the real-time fault diagnosis using incomplete and inaccurate test results with online estimation of test accuracies. It should also be noted that the test accuracies can vary with time --- one needs a mechanism to update them after processing each test result vector. Using Qualtech's TEAMS-RT (system simulation and real-time diagnosis tool), we test the performances of 1) TEAMSAT's built-in diagnosis algorithm, 2) Hamming distance based diagnosis, 3) Maximum Likelihood based diagnosis, and 4) HidderMarkov Model based diagnosis.

A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

NASA Astrophysics Data System (ADS)

Khawaja, Taimoor Saleem

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate "possibly" non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

Present-day crustal deformation and strain transfer in northeastern Tibetan Plateau

NASA Astrophysics Data System (ADS)

Li, Yuhang; Liu, Mian; Wang, Qingliang; Cui, Duxin

2018-04-01

The three-dimensional present-day crustal deformation and strain partitioning in northeastern Tibetan Plateau are analyzed using available GPS and precise leveling data. We used the multi-scale wavelet method to analyze strain rates, and the elastic block model to estimate slip rates on the major faults and internal strain within each block. Our results show that shear strain is strongly localized along major strike-slip faults, as expected in the tectonic extrusion model. However, extrusion ends and transfers to crustal contraction near the eastern margin of the Tibetan Plateau. The strain transfer is abrupt along the Haiyuan Fault and diffusive along the East Kunlun Fault. Crustal contraction is spatially correlated with active uplifting. The present-day strain is concentrated along major fault zones; however, within many terranes bounded by these faults, intra-block strain is detectable. Terranes having high intra-block strain rates also show strong seismicity. On average the Ordos and Sichuan blocks show no intra-block strain, but localized strain on the southwestern corner of the Ordos block indicates tectonic encroachment.

Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties

NASA Astrophysics Data System (ADS)

Pourbabaee, Bahareh; Meskin, Nader; Khorasani, Khashayar

2016-08-01

In this paper, a novel robust sensor fault detection and isolation (FDI) strategy using the multiple model-based (MM) approach is proposed that remains robust with respect to both time-varying parameter uncertainties and process and measurement noise in all the channels. The scheme is composed of robust Kalman filters (RKF) that are constructed for multiple piecewise linear (PWL) models that are constructed at various operating points of an uncertain nonlinear system. The parameter uncertainty is modeled by using a time-varying norm bounded admissible structure that affects all the PWL state space matrices. The robust Kalman filter gain matrices are designed by solving two algebraic Riccati equations (AREs) that are expressed as two linear matrix inequality (LMI) feasibility conditions. The proposed multiple RKF-based FDI scheme is simulated for a single spool gas turbine engine to diagnose various sensor faults despite the presence of parameter uncertainties, process and measurement noise. Our comparative studies confirm the superiority of our proposed FDI method when compared to the methods that are available in the literature.

Fault recovery for real-time, multi-tasking computer system

NASA Technical Reports Server (NTRS)

Hess, Richard (Inventor); Kelly, Gerald B. (Inventor); Rogers, Randy (Inventor); Stange, Kent A. (Inventor)

2011-01-01

System and methods for providing a recoverable real time multi-tasking computer system are disclosed. In one embodiment, a system comprises a real time computing environment, wherein the real time computing environment is adapted to execute one or more applications and wherein each application is time and space partitioned. The system further comprises a fault detection system adapted to detect one or more faults affecting the real time computing environment and a fault recovery system, wherein upon the detection of a fault the fault recovery system is adapted to restore a backup set of state variables.

The repetition of large-earthquake ruptures.

PubMed Central

Sieh, K

1996-01-01

This survey of well-documented repeated fault rupture confirms that some faults have exhibited a "characteristic" behavior during repeated large earthquakes--that is, the magnitude, distribution, and style of slip on the fault has repeated during two or more consecutive events. In two cases faults exhibit slip functions that vary little from earthquake to earthquake. In one other well-documented case, however, fault lengths contrast markedly for two consecutive ruptures, but the amount of offset at individual sites was similar. Adjacent individual patches, 10 km or more in length, failed singly during one event and in tandem during the other. More complex cases of repetition may also represent the failure of several distinct patches. The faults of the 1992 Landers earthquake provide an instructive example of such complexity. Together, these examples suggest that large earthquakes commonly result from the failure of one or more patches, each characterized by a slip function that is roughly invariant through consecutive earthquake cycles. The persistence of these slip-patches through two or more large earthquakes indicates that some quasi-invariant physical property controls the pattern and magnitude of slip. These data seem incompatible with theoretical models that produce slip distributions that are highly variable in consecutive large events. Images Fig. 3 Fig. 7 Fig. 9 PMID:11607662

Self-checking self-repairing computer nodes using the mirror processor

NASA Technical Reports Server (NTRS)

Tamir, Yuval

1992-01-01

Circuitry added to fault-tolerant systems for concurrent error deduction usually reduces performance. Using a technique called micro rollback, it is possible to eliminate most of the performance penalty of concurrent error detection. Error detection is performed in parallel with intermodule communication, and erroneous state changes are later undone. The author reports on the design and implementation of a VLSI RISC microprocessor, called the Mirror Processor (MP), which is capable of micro rollback. In order to achieve concurrent error detection, two MP chips operate in lockstep, comparing external signals and a signature of internal signals every clock cycle. If a mismatch is detected, both processors roll back to the beginning of the cycle when the error occurred. In some cases the erroneous state is corrected by copying a value from the fault-free processor to the faulty processor. The architecture, microarchitecture, and VLSI implementation of the MP, emphasizing its error-detection, error-recovery, and self-diagnosis capabilities, are described.

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

NASA Astrophysics Data System (ADS)

Li, De Z.; Wang, Wilson; Ismail, Fathy

2017-11-01

Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air-gap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

Fault Diagnosis for the Heat Exchanger of the Aircraft Environmental Control System Based on the Strong Tracking Filter

PubMed Central

Ma, Jian; Lu, Chen; Liu, Hongmei

2015-01-01

The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system’s efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

PubMed

Ma, Jian; Lu, Chen; Liu, Hongmei

2015-01-01

The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger.

Fault tolerant operation of switched reluctance machine

NASA Astrophysics Data System (ADS)

Wang, Wei

The energy crisis and environmental challenges have driven industry towards more energy efficient solutions. With nearly 60% of electricity consumed by various electric machines in industry sector, advancement in the efficiency of the electric drive system is of vital importance. Adjustable speed drive system (ASDS) provides excellent speed regulation and dynamic performance as well as dramatically improved system efficiency compared with conventional motors without electronics drives. Industry has witnessed tremendous grow in ASDS applications not only as a driving force but also as an electric auxiliary system for replacing bulky and low efficiency auxiliary hydraulic and mechanical systems. With the vast penetration of ASDS, its fault tolerant operation capability is more widely recognized as an important feature of drive performance especially for aerospace, automotive applications and other industrial drive applications demanding high reliability. The Switched Reluctance Machine (SRM), a low cost, highly reliable electric machine with fault tolerant operation capability, has drawn substantial attention in the past three decades. Nevertheless, SRM is not free of fault. Certain faults such as converter faults, sensor faults, winding shorts, eccentricity and position sensor faults are commonly shared among all ASDS. In this dissertation, a thorough understanding of various faults and their influence on transient and steady state performance of SRM is developed via simulation and experimental study, providing necessary knowledge for fault detection and post fault management. Lumped parameter models are established for fast real time simulation and drive control. Based on the behavior of the faults, a fault detection scheme is developed for the purpose of fast and reliable fault diagnosis. In order to improve the SRM power and torque capacity under faults, the maximum torque per ampere excitation are conceptualized and validated through theoretical analysis and experiments. With the proposed optimal waveform, torque production is greatly improved under the same Root Mean Square (RMS) current constraint. Additionally, position sensorless operation methods under phase faults are investigated to account for the combination of physical position sensor and phase winding faults. A comprehensive solution for position sensorless operation under single and multiple phases fault are proposed and validated through experiments. Continuous position sensorless operation with seamless transition between various numbers of phase fault is achieved.

The use of automatic programming techniques for fault tolerant computing systems

NASA Technical Reports Server (NTRS)

Wild, C.

1985-01-01

It is conjectured that the production of software for ultra-reliable computing systems such as required by Space Station, aircraft, nuclear power plants and the like will require a high degree of automation as well as fault tolerance. In this paper, the relationship between automatic programming techniques and fault tolerant computing systems is explored. Initial efforts in the automatic synthesis of code from assertions to be used for error detection as well as the automatic generation of assertions and test cases from abstract data type specifications is outlined. Speculation on the ability to generate truly diverse designs capable of recovery from errors by exploring alternate paths in the program synthesis tree is discussed. Some initial thoughts on the use of knowledge based systems for the global detection of abnormal behavior using expectations and the goal-directed reconfiguration of resources to meet critical mission objectives are given. One of the sources of information for these systems would be the knowledge captured during the automatic programming process.

On the implementation of faults in finite-element glacial isostatic adjustment models

NASA Astrophysics Data System (ADS)

Steffen, Rebekka; Wu, Patrick; Steffen, Holger; Eaton, David W.

2014-01-01

Stresses induced in the crust and mantle by continental-scale ice sheets during glaciation have triggered earthquakes along pre-existing faults, commencing near the end of the deglaciation. In order to get a better understanding of the relationship between glacial loading/unloading and fault movement due to the spatio-temporal evolution of stresses, a commonly used model for glacial isostatic adjustment (GIA) is extended by including a fault structure. Solving this problem is enabled by development of a workflow involving three cascaded finite-element simulations. Each step has identical lithospheric and mantle structure and properties, but evolving stress conditions along the fault. The purpose of the first simulation is to compute the spatio-temporal evolution of rebound stress when the fault is tied together. An ice load with a parabolic profile and simple ice history is applied to represent glacial loading of the Laurentide Ice Sheet. The results of the first step describe the evolution of the stress and displacement induced by the rebound process. The second step in the procedure augments the results of the first, by computing the spatio-temporal evolution of total stress (i.e. rebound stress plus tectonic background stress and overburden pressure) and displacement with reaction forces that can hold the model in equilibrium. The background stress is estimated by assuming that the fault is in frictional equilibrium before glaciation. The third step simulates fault movement induced by the spatio-temporal evolution of total stress by evaluating fault stability in a subroutine. If the fault remains stable, no movement occurs; in case of fault instability, the fault displacement is computed. We show an example of fault motion along a 45°-dipping fault at the ice-sheet centre for a two-dimensional model. Stable conditions along the fault are found during glaciation and the initial part of deglaciation. Before deglaciation ends, the fault starts to move, and fault offsets of up to 22 m are obtained. A fault scarp at the surface of 19.74 m is determined. The fault is stable in the following time steps with a high stress accumulation at the fault tip. Along the upper part of the fault, GIA stresses are released in one earthquake.

Tacholess order-tracking approach for wind turbine gearbox fault detection

NASA Astrophysics Data System (ADS)

Wang, Yi; Xie, Yong; Xu, Guanghua; Zhang, Sicong; Hou, Chenggang

2017-09-01

Monitoring of wind turbines under variable-speed operating conditions has become an important issue in recent years. The gearbox of a wind turbine is the most important transmission unit; it generally exhibits complex vibration signatures due to random variations in operating conditions. Spectral analysis is one of the main approaches in vibration signal processing. However, spectral analysis is based on a stationary assumption and thus inapplicable to the fault diagnosis of wind turbines under variable-speed operating conditions. This constraint limits the application of spectral analysis to wind turbine diagnosis in industrial applications. Although order-tracking methods have been proposed for wind turbine fault detection in recent years, current methods are only applicable to cases in which the instantaneous shaft phase is available. For wind turbines with limited structural spaces, collecting phase signals with tachometers or encoders is difficult. In this study, a tacholess order-tracking method for wind turbines is proposed to overcome the limitations of traditional techniques. The proposed method extracts the instantaneous phase from the vibration signal, resamples the signal at equiangular increments, and calculates the order spectrum for wind turbine fault identification. The effectiveness of the proposed method is experimentally validated with the vibration signals of wind turbines.

Identifiability of Additive Actuator and Sensor Faults by State Augmentation

NASA Technical Reports Server (NTRS)

Joshi, Suresh; Gonzalez, Oscar R.; Upchurch, Jason M.

2014-01-01

A class of fault detection and identification (FDI) methods for bias-type actuator and sensor faults is explored in detail from the point of view of fault identifiability. The methods use state augmentation along with banks of Kalman-Bucy filters for fault detection, fault pattern determination, and fault value estimation. A complete characterization of conditions for identifiability of bias-type actuator faults, sensor faults, and simultaneous actuator and sensor faults is presented. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have unknown biases. The fault identifiability conditions are demonstrated via numerical examples. The analytical and numerical results indicate that caution must be exercised to ensure fault identifiability for different fault patterns when using such methods.

Modeling of time-lapse multi-scale seismic monitoring of CO2 injected into a fault zone to enhance the characterization of permeability in enhanced geothermal systems

NASA Astrophysics Data System (ADS)

Zhang, R.; Borgia, A.; Daley, T. M.; Oldenburg, C. M.; Jung, Y.; Lee, K. J.; Doughty, C.; Altundas, B.; Chugunov, N.; Ramakrishnan, T. S.

2017-12-01

Subsurface permeable faults and fracture networks play a critical role for enhanced geothermal systems (EGS) by providing conduits for fluid flow. Characterization of the permeable flow paths before and after stimulation is necessary to evaluate and optimize energy extraction. To provide insight into the feasibility of using CO2 as a contrast agent to enhance fault characterization by seismic methods, we model seismic monitoring of supercritical CO2 (scCO2) injected into a fault. During the CO2 injection, the original brine is replaced by scCO2, which leads to variations in geophysical properties of the formation. To explore the technical feasibility of the approach, we present modeling results for different time-lapse seismic methods including surface seismic, vertical seismic profiling (VSP), and a cross-well survey. We simulate the injection and production of CO2 into a normal fault in a system based on the Brady's geothermal field and model pressure and saturation variations in the fault zone using TOUGH2-ECO2N. The simulation results provide changing fluid properties during the injection, such as saturation and salinity changes, which allow us to estimate corresponding changes in seismic properties of the fault and the formation. We model the response of the system to active seismic monitoring in time-lapse mode using an anisotropic finite difference method with modifications for fracture compliance. Results to date show that even narrow fault and fracture zones filled with CO2 can be better detected using the VSP and cross-well survey geometry, while it would be difficult to image the CO2 plume by using surface seismic methods.

Linking Europa’s Plume Activity to Tides, Tectonics, and Liquid Water

NASA Astrophysics Data System (ADS)

Rhoden, Alyssa R.; Hurford, Terry; Roth, Lorenz; Retherford, Kurt

2014-11-01

Much of the geologic activity preserved on Europa’s icy surface has been attributed to tidal deformation, mainly due to Europa’s eccentric orbit. Although the surface is geologically young, evidence of ongoing tidally-driven processes has been lacking. However, a recent observation of water vapor near Europa’s south pole suggests that it may be geologically active. Non-detections in previous and follow-up observations indicate a temporal variation in plume visibility and suggests a relationship to Europa’s tidal cycle. Similarly, the Cassini spacecraft has observed plumes emanating from the south pole of Saturn’s moon, Enceladus, and variability in the intensity of eruptions has been linked to its tidal cycle. The inference that a similar mechanism controls plumes at both Europa and Enceladus motivates further analysis of Europa’s plume behavior and the relationship between plumes, tides, and liquid water on these two satellites.We determine the locations and orientations of hypothetical tidally-driven fractures that best match the temporal variability of the plumes observed at Europa. Specifically, we identify model faults that are in tension at the time in Europa’s orbit when a plume was detected and in compression at times when the plume was not detected. We find that tidal stress driven solely by eccentricity is incompatible with the observations unless additional mechanisms are controlling the eruption timing or restricting the longevity of the plumes. In contrast, the addition of obliquity tides, and corresponding precession of the spin pole, can generate a number of model faults that are consistent with the pattern of plume detections. The locations and orientations of the model faults are robust across a broad range of precession rates and spin pole directions. Analysis of the stress variations across model faults suggests that the plumes would be best observed earlier in Europa’s orbit. Our results indicate that Europa’s plumes, if confirmed, differ in many respects from the Enceladean plumes and that either active fractures or volatile sources are rare.

Ares I-X Ground Diagnostic Prototype

NASA Technical Reports Server (NTRS)

Schwabacher, Mark A.; Martin, Rodney Alexander; Waterman, Robert D.; Oostdyk, Rebecca Lynn; Ossenfort, John P.; Matthews, Bryan

2010-01-01

The automation of pre-launch diagnostics for launch vehicles offers three potential benefits: improving safety, reducing cost, and reducing launch delays. The Ares I-X Ground Diagnostic Prototype demonstrated anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage Thrust Vector Control and for the associated ground hydraulics while the vehicle was in the Vehicle Assembly Building at Kennedy Space Center (KSC) and while it was on the launch pad. The prototype combines three existing tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool from Qualtech Systems Inc. for fault isolation and diagnostics. The second tool, SHINE (Spacecraft Health Inference Engine), is a rule-based expert system that was developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification, and used the outputs of SHINE as inputs to TEAMS. The third tool, IMS (Inductive Monitoring System), is an anomaly detection tool that was developed at NASA Ames Research Center. The three tools were integrated and deployed to KSC, where they were interfaced with live data. This paper describes how the prototype performed during the period of time before the launch, including accuracy and computer resource usage. The paper concludes with some of the lessons that we learned from the experience of developing and deploying the prototype.

Automatic Channel Fault Detection on a Small Animal APD-Based Digital PET Scanner

NASA Astrophysics Data System (ADS)

Charest, Jonathan; Beaudoin, Jean-François; Cadorette, Jules; Lecomte, Roger; Brunet, Charles-Antoine; Fontaine, Réjean

2014-10-01

Avalanche photodiode (APD) based positron emission tomography (PET) scanners show enhanced imaging capabilities in terms of spatial resolution and contrast due to the one to one coupling and size of individual crystal-APD detectors. However, to ensure the maximal performance, these PET scanners require proper calibration by qualified scanner operators, which can become a cumbersome task because of the huge number of channels they are made of. An intelligent system (IS) intends to alleviate this workload by enabling a diagnosis of the observational errors of the scanner. The IS can be broken down into four hierarchical blocks: parameter extraction, channel fault detection, prioritization and diagnosis. One of the main activities of the IS consists in analyzing available channel data such as: normalization coincidence counts and single count rates, crystal identification classification data, energy histograms, APD bias and noise thresholds to establish the channel health status that will be used to detect channel faults. This paper focuses on the first two blocks of the IS: parameter extraction and channel fault detection. The purpose of the parameter extraction block is to process available data on individual channels into parameters that are subsequently used by the fault detection block to generate the channel health status. To ensure extensibility, the channel fault detection block is divided into indicators representing different aspects of PET scanner performance: sensitivity, timing, crystal identification and energy. Some experiments on a 8 cm axial length LabPET scanner located at the Sherbrooke Molecular Imaging Center demonstrated an erroneous channel fault detection rate of 10% (with a 95% confidence interval (CI) of [9, 11]) which is considered tolerable. Globally, the IS achieves a channel fault detection efficiency of 96% (CI: [95, 97]), which proves that many faults can be detected automatically. Increased fault detection efficiency would be advantageous but, the achieved results would already benefit scanner operators in their maintenance task.

Bearing faults identification and resonant band demodulation based on wavelet de-noising methods and envelope analysis

NASA Astrophysics Data System (ADS)

Abdelrhman, Ahmed M.; Sei Kien, Yong; Salman Leong, M.; Meng Hee, Lim; Al-Obaidi, Salah M. Ali

2017-07-01

The vibration signals produced by rotating machinery contain useful information for condition monitoring and fault diagnosis. Fault severities assessment is a challenging task. Wavelet Transform (WT) as a multivariate analysis tool is able to compromise between the time and frequency information in the signals and served as a de-noising method. The CWT scaling function gives different resolutions to the discretely signals such as very fine resolution at lower scale but coarser resolution at a higher scale. However, the computational cost increased as it needs to produce different signal resolutions. DWT has better low computation cost as the dilation function allowed the signals to be decomposed through a tree of low and high pass filters and no further analysing the high-frequency components. In this paper, a method for bearing faults identification is presented by combing Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) with envelope analysis for bearing fault diagnosis. The experimental data was sampled by Case Western Reserve University. The analysis result showed that the proposed method is effective in bearing faults detection, identify the exact fault’s location and severity assessment especially for the inner race and outer race faults.

Using marine magnetic survey data to identify a gold ore-controlling fault: a case study in Sanshandao fault, eastern China

NASA Astrophysics Data System (ADS)

Yan, Jiayong; Wang, Zhihui; Wang, Jinhui; Song, Jianhua

2018-06-01

The Jiaodong Peninsula has the greatest concentration of gold ore in China and is characterized by altered tectonite-type gold ore deposits. This type of gold deposit is mainly formed in fracture zones and is strictly controlled by faults. Three major ore-controlling faults occur in the Jiaodong Peninsula—the Jiaojia, Zhaoping and Sanshandao faults; the former two are located on land and the latter is located near Sanshandao and its adjacent offshore area. The discovery of the world’s largest marine gold deposit in northeastern Sanshandao indicates that the shallow offshore area has great potential for gold prospecting. However, as two ends of the Sanshandao fault extend to the Bohai Sea, conventional geological survey methods cannot determine the distribution of the fault and this is constraining the discovery of new gold deposits. To explore the southwestward extension of the Sanshandao fault, we performed a 1:25 000 scale marine magnetic survey in this region and obtained high-quality magnetic survey data covering 170 km2. Multi-scale edge detection and three-dimensional inversion of magnetic anomalies identify the characteristics of the southwestward extension of the Sanshandao fault and the three-dimensional distribution of the main lithologies, providing significant evidence for the deployment of marine gold deposit prospecting in the southern segment of the Sanshandao fault. Moreover, three other faults were identified in the study area and faults F2 and F4 are inferred as ore-controlling faults: there may exist other altered tectonite-type gold ore deposits along these two faults.

Fault recovery characteristics of the fault tolerant multi-processor

NASA Technical Reports Server (NTRS)

Padilla, Peter A.

1990-01-01

The fault handling performance of the fault tolerant multiprocessor (FTMP) was investigated. Fault handling errors detected during fault injection experiments were characterized. In these fault injection experiments, the FTMP disabled a working unit instead of the faulted unit once every 500 faults, on the average. System design weaknesses allow active faults to exercise a part of the fault management software that handles byzantine or lying faults. It is pointed out that these weak areas in the FTMP's design increase the probability that, for any hardware fault, a good LRU (line replaceable unit) is mistakenly disabled by the fault management software. It is concluded that fault injection can help detect and analyze the behavior of a system in the ultra-reliable regime. Although fault injection testing cannot be exhaustive, it has been demonstrated that it provides a unique capability to unmask problems and to characterize the behavior of a fault-tolerant system.

Fault detection and isolation

NASA Technical Reports Server (NTRS)

Bernath, Greg

1994-01-01

In order for a current satellite-based navigation system (such as the Global Positioning System, GPS) to meet integrity requirements, there must be a way of detecting erroneous measurements, without help from outside the system. This process is called Fault Detection and Isolation (FDI). Fault detection requires at least one redundant measurement, and can be done with a parity space algorithm. The best way around the fault isolation problem is not necessarily isolating the bad measurement, but finding a new combination of measurements which excludes it.

A step forward in understanding step-overs: the case of the Dead Sea Fault in northern Israel

NASA Astrophysics Data System (ADS)

Dembo, Neta; Granot, Roi; Hamiel, Yariv

2017-04-01

The rotational deformation field around step-overs between segments of strike-slip faults is poorly resolved. Vertical-axis paleomagnetic rotations can be used to characterize the deformation field, and together with mechanical modeling, can provide constraints on the characteristics of the adjacent fault segments. The northern Dead Sea Fault, a major segmented sinistral transform fault that straddles the boundary between the Arabian Plate and Sinai Subplate, offers an appropriate tectonic setting for our detailed mechanical and paleomagnetic investigation. We examine the paleomagnetic vertical-axis rotations of Neogene-Pleistocene basalt outcrops surrounding a right step-over between two prominent segments of the fault: the Jordan Gorge section and the Hula East Boundary Fault. Results from 20 new paleomagnetic sites reveal significant (>20˚) counterclockwise rotations within the step-over and small clockwise rotations in the vicinity. Sites located further (>2.5 km) away from the step-over generally experience negligible to minor rotations. Finally, we construct a mechanical model guided by the observed rotational field that allows us to characterize the structural, mechanical and kinematic behavior of the Dead Sea Fault in northern Israel.

Nuclear Power Plant Thermocouple Sensor-Fault Detection and Classification Using Deep Learning and Generalized Likelihood Ratio Test

NASA Astrophysics Data System (ADS)

Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.

2017-06-01

In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

NASA Technical Reports Server (NTRS)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.

Application of an improved minimum entropy deconvolution method for railway rolling element bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Cheng, Yao; Zhou, Ning; Zhang, Weihua; Wang, Zhiwei

2018-07-01

Minimum entropy deconvolution is a widely-used tool in machinery fault diagnosis, because it enhances the impulse component of the signal. The filter coefficients that greatly influence the performance of the minimum entropy deconvolution are calculated by an iterative procedure. This paper proposes an improved deconvolution method for the fault detection of rolling element bearings. The proposed method solves the filter coefficients by the standard particle swarm optimization algorithm, assisted by a generalized spherical coordinate transformation. When optimizing the filters performance for enhancing the impulses in fault diagnosis (namely, faulty rolling element bearings), the proposed method outperformed the classical minimum entropy deconvolution method. The proposed method was validated in simulation and experimental signals from railway bearings. In both simulation and experimental studies, the proposed method delivered better deconvolution performance than the classical minimum entropy deconvolution method, especially in the case of low signal-to-noise ratio.

Real-time inversions for finite fault slip models and rupture geometry based on high-rate GPS data

USGS Publications Warehouse

Minson, Sarah E.; Murray, Jessica R.; Langbein, John O.; Gomberg, Joan S.

2015-01-01

We present an inversion strategy capable of using real-time high-rate GPS data to simultaneously solve for a distributed slip model and fault geometry in real time as a rupture unfolds. We employ Bayesian inference to find the optimal fault geometry and the distribution of possible slip models for that geometry using a simple analytical solution. By adopting an analytical Bayesian approach, we can solve this complex inversion problem (including calculating the uncertainties on our results) in real time. Furthermore, since the joint inversion for distributed slip and fault geometry can be computed in real time, the time required to obtain a source model of the earthquake does not depend on the computational cost. Instead, the time required is controlled by the duration of the rupture and the time required for information to propagate from the source to the receivers. We apply our modeling approach, called Bayesian Evidence-based Fault Orientation and Real-time Earthquake Slip, to the 2011 Tohoku-oki earthquake, 2003 Tokachi-oki earthquake, and a simulated Hayward fault earthquake. In all three cases, the inversion recovers the magnitude, spatial distribution of slip, and fault geometry in real time. Since our inversion relies on static offsets estimated from real-time high-rate GPS data, we also present performance tests of various approaches to estimating quasi-static offsets in real time. We find that the raw high-rate time series are the best data to use for determining the moment magnitude of the event, but slightly smoothing the raw time series helps stabilize the inversion for fault geometry.

The 2013, Mw 7.7 Balochistan earthquake, energetic strike-slip reactivation of a thrust fault

NASA Astrophysics Data System (ADS)

Avouac, Jean-Philippe; Ayoub, Francois; Wei, Shengji; Ampuero, Jean-Paul; Meng, Lingsen; Leprince, Sebastien; Jolivet, Romain; Duputel, Zacharie; Helmberger, Don

2014-04-01

We analyse the Mw 7.7 Balochistan earthquake of 09/24/2013 based on ground surface deformation measured from sub-pixel correlation of Landsat-8 images, combined with back-projection and finite source modeling of teleseismic waveforms. The earthquake nucleated south of the Chaman strike-slip fault and propagated southwestward along the Hoshab fault at the front of the Kech Band. The rupture was mostly unilateral, propagated at 3 km/s on average and produced a 200 km surface fault trace with purely strike-slip displacement peaking to 10 m and averaging around 6 m. The finite source model shows that slip was maximum near the surface. Although the Hoshab fault is dipping by 45° to the North, in accordance with its origin as a thrust fault within the Makran accretionary prism, slip was nearly purely strike-slip during that earthquake. Large seismic slip on such a non-optimally oriented fault was enhanced possibly due to the influence of the free surface on dynamic stresses or to particular properties of the fault zone allowing for strong dynamic weakening. Strike-slip faulting on thrust fault within the eastern Makran is interpreted as due to eastward extrusion of the accretionary prism as it bulges out over the Indian plate. Portions of the Makran megathrust, some thrust faults in the Kirthar range and strike-slip faults within the Chaman fault system have been brought closer to failure by this earthquake. Aftershocks cluster within the Chaman fault system north of the epicenter, opposite to the direction of rupture propagation. By contrast, few aftershocks were detected in the area of maximum moment release. In this example, aftershocks cannot be used to infer earthquake characteristics.

Dependability analysis of parallel systems using a simulation-based approach. M.S. Thesis

NASA Technical Reports Server (NTRS)

Sawyer, Darren Charles

1994-01-01

The analysis of dependability in large, complex, parallel systems executing real applications or workloads is examined in this thesis. To effectively demonstrate the wide range of dependability problems that can be analyzed through simulation, the analysis of three case studies is presented. For each case, the organization of the simulation model used is outlined, and the results from simulated fault injection experiments are explained, showing the usefulness of this method in dependability modeling of large parallel systems. The simulation models are constructed using DEPEND and C++. Where possible, methods to increase dependability are derived from the experimental results. Another interesting facet of all three cases is the presence of some kind of workload of application executing in the simulation while faults are injected. This provides a completely new dimension to this type of study, not possible to model accurately with analytical approaches.

Event-Triggered Fault Detection of Nonlinear Networked Systems.

PubMed

Li, Hongyi; Chen, Ziran; Wu, Ligang; Lam, Hak-Keung; Du, Haiping

2017-04-01

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices.

PubMed

Wu, Yunkai; Jiang, Bin; Lu, Ningyun; Yang, Hao; Zhou, Yang

2017-03-01

This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

PubMed

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-06-20

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

PubMed Central

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-01-01

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. PMID:28773035

Robust Fault Diagnosis in Electric Drives Using Machine Learning

DTIC Science & Technology

2004-09-08

detection of fault conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points...were used to generate various fault condition data for machine learning . The technique is viable for accurate, reliable and fast fault detection in electric drives.

Solar system fault detection

DOEpatents

Farrington, R.B.; Pruett, J.C. Jr.

1984-05-14

A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

Solar system fault detection

DOEpatents

Farrington, Robert B.; Pruett, Jr., James C.

1986-01-01

A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

From experiment to design -- Fault characterization and detection in parallel computer systems using computational accelerators

NASA Astrophysics Data System (ADS)

Yim, Keun Soo

This dissertation summarizes experimental validation and co-design studies conducted to optimize the fault detection capabilities and overheads in hybrid computer systems (e.g., using CPUs and Graphics Processing Units, or GPUs), and consequently to improve the scalability of parallel computer systems using computational accelerators. The experimental validation studies were conducted to help us understand the failure characteristics of CPU-GPU hybrid computer systems under various types of hardware faults. The main characterization targets were faults that are difficult to detect and/or recover from, e.g., faults that cause long latency failures (Ch. 3), faults in dynamically allocated resources (Ch. 4), faults in GPUs (Ch. 5), faults in MPI programs (Ch. 6), and microarchitecture-level faults with specific timing features (Ch. 7). The co-design studies were based on the characterization results. One of the co-designed systems has a set of source-to-source translators that customize and strategically place error detectors in the source code of target GPU programs (Ch. 5). Another co-designed system uses an extension card to learn the normal behavioral and semantic execution patterns of message-passing processes executing on CPUs, and to detect abnormal behaviors of those parallel processes (Ch. 6). The third co-designed system is a co-processor that has a set of new instructions in order to support software-implemented fault detection techniques (Ch. 7). The work described in this dissertation gains more importance because heterogeneous processors have become an essential component of state-of-the-art supercomputers. GPUs were used in three of the five fastest supercomputers that were operating in 2011. Our work included comprehensive fault characterization studies in CPU-GPU hybrid computers. In CPUs, we monitored the target systems for a long period of time after injecting faults (a temporally comprehensive experiment), and injected faults into various types of program states that included dynamically allocated memory (to be spatially comprehensive). In GPUs, we used fault injection studies to demonstrate the importance of detecting silent data corruption (SDC) errors that are mainly due to the lack of fine-grained protections and the massive use of fault-insensitive data. This dissertation also presents transparent fault tolerance frameworks and techniques that are directly applicable to hybrid computers built using only commercial off-the-shelf hardware components. This dissertation shows that by developing understanding of the failure characteristics and error propagation paths of target programs, we were able to create fault tolerance frameworks and techniques that can quickly detect and recover from hardware faults with low performance and hardware overheads.

A Sparsity-based Framework for Resolution Enhancement in Optical Fault Analysis of Integrated Circuits

DTIC Science & Technology

2015-01-01

for IC fault detection . This section provides background information on inversion methods. Conventional inversion techniques and their shortcomings are...physical techniques, electron beam imaging/analysis, ion beam techniques, scanning probe techniques. Electrical tests are used to detect faults in 13 an...hand, there is also the second harmonic technique through which duty cycle degradation faults are detected by collecting the magnitude and the phase of

Product quality management based on CNC machine fault prognostics and diagnosis

NASA Astrophysics Data System (ADS)

Kozlov, A. M.; Al-jonid, Kh M.; Kozlov, A. A.; Antar, Sh D.

2018-03-01

This paper presents a new fault classification model and an integrated approach to fault diagnosis which involves the combination of ideas of Neuro-fuzzy Networks (NF), Dynamic Bayesian Networks (DBN) and Particle Filtering (PF) algorithm on a single platform. In the new model, faults are categorized in two aspects, namely first and second degree faults. First degree faults are instantaneous in nature, and second degree faults are evolutional and appear as a developing phenomenon which starts from the initial stage, goes through the development stage and finally ends at the mature stage. These categories of faults have a lifetime which is inversely proportional to a machine tool's life according to the modified version of Taylor’s equation. For fault diagnosis, this framework consists of two phases: the first one is focusing on fault prognosis, which is done online, and the second one is concerned with fault diagnosis which depends on both off-line and on-line modules. In the first phase, a neuro-fuzzy predictor is used to take a decision on whether to embark Conditional Based Maintenance (CBM) or fault diagnosis based on the severity of a fault. The second phase only comes into action when an evolving fault goes beyond a critical threshold limit called a CBM limit for a command to be issued for fault diagnosis. During this phase, DBN and PF techniques are used as an intelligent fault diagnosis system to determine the severity, time and location of the fault. The feasibility of this approach was tested in a simulation environment using the CNC machine as a case study and the results were studied and analyzed.