A distributed fault-detection and diagnosis system using on-line parameter estimation

NASA Technical Reports Server (NTRS)

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

1991-01-01

The development of a model-based fault-detection and diagnosis system (FDD) is reviewed. The system can be used as an integral part of an intelligent control system. It determines the faults of a system from comparison of the measurements of the system with a priori information represented by the model of the system. The method of modeling a complex system is described and a description of diagnosis models which include process faults is presented. There are three distinct classes of fault modes covered by the system performance model equation: actuator faults, sensor faults, and performance degradation. A system equation for a complete model that describes all three classes of faults is given. The strategy for detecting the fault and estimating the fault parameters using a distributed on-line parameter identification scheme is presented. A two-step approach is proposed. The first step is composed of a group of hypothesis testing modules, (HTM) in parallel processing to test each class of faults. The second step is the fault diagnosis module which checks all the information obtained from the HTM level, isolates the fault, and determines its magnitude. The proposed FDD system was demonstrated by applying it to detect actuator and sensor faults added to a simulation of the Space Shuttle Main Engine. The simulation results show that the proposed FDD system can adequately detect the faults and estimate their magnitudes.

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.

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.

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.

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.

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.

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.

Bearing fault diagnosis under unknown variable speed via gear noise cancellation and rotational order sideband identification

NASA Astrophysics Data System (ADS)

Wang, Tianyang; Liang, Ming; Li, Jianyong; Cheng, Weidong; Li, Chuan

2015-10-01

The interfering vibration signals of a gearbox often represent a challenging issue in rolling bearing fault detection and diagnosis, particularly under unknown variable rotational speed conditions. Though some methods have been proposed to remove the gearbox interfering signals based on their discrete frequency nature, such methods may not work well under unknown variable speed conditions. As such, we propose a new approach to address this issue. The new approach consists of three main steps: (a) adaptive gear interference removal, (b) fault characteristic order (FCO) based fault detection, and (c) rotational-order-sideband (ROS) based fault type identification. For gear interference removal, an enhanced adaptive noise cancellation (ANC) algorithm has been developed in this study. The new ANC algorithm does not require an additional accelerometer to provide reference input. Instead, the reference signal is adaptively constructed from signal maxima and instantaneous dominant meshing multiple (IDMM) trend. Key ANC parameters such as filter length and step size have also been tailored to suit the variable speed conditions, The main advantage of using ROS for fault type diagnosis is that it is insusceptible to confusion caused by the co-existence of bearing and gear rotational frequency peaks in the identification of the bearing fault characteristic frequency in the FCO sub-order region. The effectiveness of the proposed method has been demonstrated using both simulation and experimental data. Our experimental study also indicates that the proposed method is applicable regardless whether the bearing and gear rotational speeds are proportional to each other or not.

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.

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

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

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized inmore » the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.« less

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.

Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

NASA Technical Reports Server (NTRS)

Momoh, James A.; Wang, Yanchun; Dolce, James L.

1997-01-01

This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection

NASA Astrophysics Data System (ADS)

Li, Yongbo; Yang, Yuantao; Li, Guoyan; Xu, Minqiang; Huang, Wenhu

2017-07-01

Health condition identification of planetary gearboxes is crucial to reduce the downtime and maximize productivity. This paper aims to develop a novel fault diagnosis method based on modified multi-scale symbolic dynamic entropy (MMSDE) and minimum redundancy maximum relevance (mRMR) to identify the different health conditions of planetary gearbox. MMSDE is proposed to quantify the regularity of time series, which can assess the dynamical characteristics over a range of scales. MMSDE has obvious advantages in the detection of dynamical changes and computation efficiency. Then, the mRMR approach is introduced to refine the fault features. Lastly, the obtained new features are fed into the least square support vector machine (LSSVM) to complete the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault types of planetary gearboxes.

Methodologies for Adaptive Flight Envelope Estimation and Protection

NASA Technical Reports Server (NTRS)

Tang, Liang; Roemer, Michael; Ge, Jianhua; Crassidis, Agamemnon; Prasad, J. V. R.; Belcastro, Christine

2009-01-01

This paper reports the latest development of several techniques for adaptive flight envelope estimation and protection system for aircraft under damage upset conditions. Through the integration of advanced fault detection algorithms, real-time system identification of the damage/faulted aircraft and flight envelop estimation, real-time decision support can be executed autonomously for improving damage tolerance and flight recoverability. Particularly, a bank of adaptive nonlinear fault detection and isolation estimators were developed for flight control actuator faults; a real-time system identification method was developed for assessing the dynamics and performance limitation of impaired aircraft; online learning neural networks were used to approximate selected aircraft dynamics which were then inverted to estimate command margins. As off-line training of network weights is not required, the method has the advantage of adapting to varying flight conditions and different vehicle configurations. The key benefit of the envelope estimation and protection system is that it allows the aircraft to fly close to its limit boundary by constantly updating the controller command limits during flight. The developed techniques were demonstrated on NASA s Generic Transport Model (GTM) simulation environments with simulated actuator faults. Simulation results and remarks on future work are presented.

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.

Fault tolerance in computational grids: perspectives, challenges, and issues.

PubMed

Haider, Sajjad; Nazir, Babar

2016-01-01

Computational grids are established with the intention of providing shared access to hardware and software based resources with special reference to increased computational capabilities. Fault tolerance is one of the most important issues faced by the computational grids. The main contribution of this survey is the creation of an extended classification of problems that incur in the computational grid environments. The proposed classification will help researchers, developers, and maintainers of grids to understand the types of issues to be anticipated. Moreover, different types of problems, such as omission, interaction, and timing related have been identified that need to be handled on various layers of the computational grid. In this survey, an analysis and examination is also performed pertaining to the fault tolerance and fault detection mechanisms. Our conclusion is that a dependable and reliable grid can only be established when more emphasis is on fault identification. Moreover, our survey reveals that adaptive and intelligent fault identification, and tolerance techniques can improve the dependability of grid working environments.

Fault detection in rotor bearing systems using time frequency techniques

NASA Astrophysics Data System (ADS)

Chandra, N. Harish; Sekhar, A. S.

2016-05-01

Faults such as misalignment, rotor cracks and rotor to stator rub can exist collectively in rotor bearing systems. It is an important task for rotor dynamic personnel to monitor and detect faults in rotating machinery. In this paper, the rotor startup vibrations are utilized to solve the fault identification problem using time frequency techniques. Numerical simulations are performed through finite element analysis of the rotor bearing system with individual and collective combinations of faults as mentioned above. Three signal processing tools namely Short Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Hilbert Huang Transform (HHT) are compared to evaluate their detection performance. The effect of addition of Signal to Noise ratio (SNR) on three time frequency techniques is presented. The comparative study is focused towards detecting the least possible level of the fault induced and the computational time consumed. The computation time consumed by HHT is very less when compared to CWT based diagnosis. However, for noisy data CWT is more preferred over HHT. To identify fault characteristics using wavelets a procedure to adjust resolution of the mother wavelet is presented in detail. Experiments are conducted to obtain the run-up data of a rotor bearing setup for diagnosis of shaft misalignment and rotor stator rubbing faults.

Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

NASA Astrophysics Data System (ADS)

Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

2017-12-01

To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

Neural Networks and other Techniques for Fault Identification and Isolation of Aircraft Systems

NASA Technical Reports Server (NTRS)

Innocenti, M.; Napolitano, M.

2003-01-01

Fault identification, isolation, and accomodation have become critical issues in the overall performance of advanced aircraft systems. Neural Networks have shown to be a very attractive alternative to classic adaptation methods for identification and control of non-linear dynamic systems. The purpose of this paper is to show the improvements in neural network applications achievable through the use of learning algorithms more efficient than the classic Back-Propagation, and through the implementation of the neural schemes in parallel hardware. The results of the analysis of a scheme for Sensor Failure, Detection, Identification and Accommodation (SFDIA) using experimental flight data of a research aircraft model are presented. Conventional approaches to the problem are based on observers and Kalman Filters while more recent methods are based on neural approximators. The work described in this paper is based on the use of neural networks (NNs) as on-line learning non-linear approximators. The performances of two different neural architectures were compared. The first architecture is based on a Multi Layer Perceptron (MLP) NN trained with the Extended Back Propagation algorithm (EBPA). The second architecture is based on a Radial Basis Function (RBF) NN trained with the Extended-MRAN (EMRAN) algorithms. In addition, alternative methods for communications links fault detection and accomodation are presented, relative to multiple unmanned aircraft applications.

Built-in-test by signature inspection (bitsi)

DOEpatents

Bergeson, Gary C.; Morneau, Richard A.

1991-01-01

A system and method for fault detection for electronic circuits. A stimulus generator sends a signal to the input of the circuit under test. Signature inspection logic compares the resultant signal from test nodes on the circuit to an expected signal. If the signals do not match, the signature inspection logic sends a signal to the control logic for indication of fault detection in the circuit. A data input multiplexer between the test nodes of the circuit under test and the signature inspection logic can provide for identification of the specific node at fault by the signature inspection logic. Control logic responsive to the signature inspection logic conveys information about fault detection for use in determining the condition of the circuit. When used in conjunction with a system test controller, the built-in test by signature inspection system and method can be used to poll a plurality of circuits automatically and continuous for faults and record the results of such polling in the system test controller.

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.

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.

Smart Sensor for Online Detection of Multiple-Combined Faults in VSD-Fed Induction Motors

PubMed Central

Garcia-Ramirez, Armando G.; Osornio-Rios, Roque A.; Granados-Lieberman, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J.

2012-01-01

Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).

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

Fault Management Metrics

NASA Technical Reports Server (NTRS)

Johnson, Stephen B.; Ghoshal, Sudipto; Haste, Deepak; Moore, Craig

2017-01-01

This paper describes the theory and considerations in the application of metrics to measure the effectiveness of fault management. Fault management refers here to the operational aspect of system health management, and as such is considered as a meta-control loop that operates to preserve or maximize the system's ability to achieve its goals in the face of current or prospective failure. As a suite of control loops, the metrics to estimate and measure the effectiveness of fault management are similar to those of classical control loops in being divided into two major classes: state estimation, and state control. State estimation metrics can be classified into lower-level subdivisions for detection coverage, detection effectiveness, fault isolation and fault identification (diagnostics), and failure prognosis. State control metrics can be classified into response determination effectiveness and response effectiveness. These metrics are applied to each and every fault management control loop in the system, for each failure to which they apply, and probabilistically summed to determine the effectiveness of these fault management control loops to preserve the relevant system goals that they are intended to protect.

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation

PubMed Central

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site. PMID:29370230

Identification of transformer fault based on dissolved gas analysis using hybrid support vector machine-modified evolutionary particle swarm optimisation.

PubMed

Illias, Hazlee Azil; Zhao Liang, Wee

2018-01-01

Early detection of power transformer fault is important because it can reduce the maintenance cost of the transformer and it can ensure continuous electricity supply in power systems. Dissolved Gas Analysis (DGA) technique is commonly used to identify oil-filled power transformer fault type but utilisation of artificial intelligence method with optimisation methods has shown convincing results. In this work, a hybrid support vector machine (SVM) with modified evolutionary particle swarm optimisation (EPSO) algorithm was proposed to determine the transformer fault type. The superiority of the modified PSO technique with SVM was evaluated by comparing the results with the actual fault diagnosis, unoptimised SVM and previous reported works. Data reduction was also applied using stepwise regression prior to the training process of SVM to reduce the training time. It was found that the proposed hybrid SVM-Modified EPSO (MEPSO)-Time Varying Acceleration Coefficient (TVAC) technique results in the highest correct identification percentage of faults in a power transformer compared to other PSO algorithms. Thus, the proposed technique can be one of the potential solutions to identify the transformer fault type based on DGA data on site.

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.

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.

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.

Identification and detection of anomalies through SSME data analysis

NASA Technical Reports Server (NTRS)

Pereira, Lisa; Ali, Moonis

1990-01-01

The goal of the ongoing research described in this paper is to analyze real-time ground test data in order to identify patterns associated with the anomalous engine behavior, and on the basis of this analysis to develop an expert system which detects anomalous engine behavior in the early stages of fault development. A prototype of the expert system has been developed and tested on the high frequency data of two SSME tests, namely Test #901-0516 and Test #904-044. The comparison of our results with the post-test analyses indicates that the expert system detected the presence of the anomalies in a significantly early stage of fault development.

Identification of the Polaris Fault using lidar and shallow geophysical methods

USGS Publications Warehouse

Hunter, Lewis E.; Powers, Michael H.; Burton, Bethany L.

2017-01-01

As part of the U.S. Army Corps of Engineers' (USACE) Dam Safety Assurance Program, Martis Creek Dam near Truckee, CA, is under evaluation for earthquake and seepage hazards. The investigations to date have included LiDAR (Light Detection and Ranging) and a wide range of geophysical surveys. The LiDAR data led to the discovery of an important and previously unknown fault tracing very near and possibly under Martis Creek Dam. The geophysical surveys of the dam foundation area confirm evidence of the fault in the area.

Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Roychoudhury, Indranil

2010-01-01

We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

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.

Neural network application to comprehensive engine diagnostics

NASA Technical Reports Server (NTRS)

Marko, Kenneth A.

1994-01-01

We have previously reported on the use of neural networks for detection and identification of faults in complex microprocessor controlled powertrain systems. The data analyzed in those studies consisted of the full spectrum of signals passing between the engine and the real-time microprocessor controller. The specific task of the classification system was to classify system operation as nominal or abnormal and to identify the fault present. The primary concern in earlier work was the identification of faults, in sensors or actuators in the powertrain system as it was exercised over its full operating range. The use of data from a variety of sources, each contributing some potentially useful information to the classification task, is commonly referred to as sensor fusion and typifies the type of problems successfully addressed using neural networks. In this work we explore the application of neural networks to a different diagnostic problem, the diagnosis of faults in newly manufactured engines and the utility of neural networks for process control.

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.

A benchmark for fault tolerant flight control evaluation

NASA Astrophysics Data System (ADS)

Smaili, H.; Breeman, J.; Lombaerts, T.; Stroosma, O.

2013-12-01

A large transport aircraft simulation benchmark (REconfigurable COntrol for Vehicle Emergency Return - RECOVER) has been developed within the GARTEUR (Group for Aeronautical Research and Technology in Europe) Flight Mechanics Action Group 16 (FM-AG(16)) on Fault Tolerant Control (2004 2008) for the integrated evaluation of fault detection and identification (FDI) and reconfigurable flight control strategies. The benchmark includes a suitable set of assessment criteria and failure cases, based on reconstructed accident scenarios, to assess the potential of new adaptive control strategies to improve aircraft survivability. The application of reconstruction and modeling techniques, based on accident flight data, has resulted in high-fidelity nonlinear aircraft and fault models to evaluate new Fault Tolerant Flight Control (FTFC) concepts and their real-time performance to accommodate in-flight failures.

Fault Diagnosis of Demountable Disk-Drum Aero-Engine Rotor Using Customized Multiwavelet Method.

PubMed

Chen, Jinglong; Wang, Yu; He, Zhengjia; Wang, Xiaodong

2015-10-23

The demountable disk-drum aero-engine rotor is an important piece of equipment that greatly impacts the safe operation of aircraft. However, assembly looseness or crack fault has led to several unscheduled breakdowns and serious accidents. Thus, condition monitoring and fault diagnosis technique are required for identifying abnormal conditions. Customized ensemble multiwavelet method for aero-engine rotor condition identification, using measured vibration data, is developed in this paper. First, customized multiwavelet basis function with strong adaptivity is constructed via symmetric multiwavelet lifting scheme. Then vibration signal is processed by customized ensemble multiwavelet transform. Next, normalized information entropy of multiwavelet decomposition coefficients is computed to directly reflect and evaluate the condition. The proposed approach is first applied to fault detection of an experimental aero-engine rotor. Finally, the proposed approach is used in an engineering application, where it successfully identified the crack fault of a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in fault detection of aero-engine rotor. Moreover, the robustness of the multiwavelet method against noise is also tested and verified by simulation and field experiments.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input-output direct feed-through term.

PubMed

Tsai, Jason S-H; Hsu, Wen-Teng; Lin, Long-Guei; Guo, Shu-Mei; Tann, Joseph W

2014-01-01

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input-output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

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.

An algorithm to diagnose ball bearing faults in servomotors running arbitrary motion profiles

NASA Astrophysics Data System (ADS)

Cocconcelli, Marco; Bassi, Luca; Secchi, Cristian; Fantuzzi, Cesare; Rubini, Riccardo

2012-02-01

This paper describes a procedure to extend the scope of classical methods to detect ball bearing faults (based on envelope analysis and fault frequencies identification) beyond their usual area of application. The objective of this procedure is to allow condition-based monitoring of such bearings in servomotor applications, where typically the motor in its normal mode of operation has to follow a non-constant angular velocity profile that may contain motion inversions. After describing and analyzing the algorithm from a theoretical point of view, experimental results obtained on a real industrial application are presented and commented.

Identification of propulsion systems

NASA Technical Reports Server (NTRS)

Merrill, Walter; Guo, Ten-Huei; Duyar, Ahmet

1991-01-01

This paper presents a tutorial on the use of model identification techniques for the identification of propulsion system models. These models are important for control design, simulation, parameter estimation, and fault detection. Propulsion system identification is defined in the context of the classical description of identification as a four step process that is unique because of special considerations of data and error sources. Propulsion system models are described along with the dependence of system operation on the environment. Propulsion system simulation approaches are discussed as well as approaches to propulsion system identification with examples for both air breathing and rocket systems.

Practical Issues in Implementing Software Reliability Measurement

NASA Technical Reports Server (NTRS)

Nikora, Allen P.; Schneidewind, Norman F.; Everett, William W.; Munson, John C.; Vouk, Mladen A.; Musa, John D.

1999-01-01

Many ways of estimating software systems' reliability, or reliability-related quantities, have been developed over the past several years. Of particular interest are methods that can be used to estimate a software system's fault content prior to test, or to discriminate between components that are fault-prone and those that are not. The results of these methods can be used to: 1) More accurately focus scarce fault identification resources on those portions of a software system most in need of it. 2) Estimate and forecast the risk of exposure to residual faults in a software system during operation, and develop risk and safety criteria to guide the release of a software system to fielded use. 3) Estimate the efficiency of test suites in detecting residual faults. 4) Estimate the stability of the software maintenance process.

Identification of a new fault and associated lineament features in Oregon's Summer Lake Valley using high-resolution LiDAR data

NASA Astrophysics Data System (ADS)

Bennett, L.; Madin, I.

2012-12-01

In 2012, the Oregon Department of Geology and Mineral Industries (DOGAMI) contracted WSI to co-acquire airborne Light Detecting and Ranging (LiDAR) and Thermal Infrared Imagery (TIR) data within the region surrounding Summer Lake, Oregon. The objective of this project was to detect surficial expressions of geothermal activity and associated geologic features. An analysis of the LiDAR data revealed one newly identified fault and several accompanying lineaments that strike northwest, similar to the trend of the Ana River, Brothers, and Eugene-Denio Fault Zones in Central Oregon. The age of the Ana River Fault Zone and Summer Lake bed is known to be within the Holocene epoch. Apparent scarp height observed from the LiDAR is up to 8 meters. While detailed analysis is ongoing, the data illustrated the effectiveness of using high resolution remote sensing data for surficial analysis of geologic displacement. This presentation will focus on direct visual detection of features in the Summer Lake, Oregon landscape using LiDAR data.

Motion-Based System Identification and Fault Detection and Isolation Technologies for Thruster Controlled Spacecraft

NASA Technical Reports Server (NTRS)

Wilson, Edward; Sutter, David W.; Berkovitz, Dustin; Betts, Bradley J.; Kong, Edmund; delMundo, Rommel; Lages, Christopher R.; Mah, Robert W.; Papasin, Richard

2003-01-01

By analyzing the motions of a thruster-controlled spacecraft, it is possible to provide on-line (1) thruster fault detection and isolation (FDI), and (2) vehicle mass- and thruster-property identification (ID). Technologies developed recently at NASA Ames have significantly improved the speed and accuracy of these ID and FDI capabilities, making them feasible for application to a broad class of spacecraft. Since these technologies use existing sensors, the improved system robustness and performance that comes with the thruster fault tolerance and system ID can be achieved through a software-only implementation. This contrasts with the added cost, mass, and hardware complexity commonly required by FDI. Originally developed in partnership with NASA - Johnson Space Center to provide thruster FDI capability for the X-38 during re-entry, these technologies are most recently being applied to the MIT SPHERES experimental spacecraft to fly on the International Space Station in 2004. The model-based FDI uses a maximum-likelihood calculation at its core, while the ID is based upon recursive least squares estimation. Flight test results from the SPHERES implementation, as flown aboard the NASA KC-1 35A 0-g simulator aircraft in November 2003 are presented.

ARGES: an Expert System for Fault Diagnosis Within Space-Based ECLS Systems

NASA Technical Reports Server (NTRS)

Pachura, David W.; Suleiman, Salem A.; Mendler, Andrew P.

1988-01-01

ARGES (Atmospheric Revitalization Group Expert System) is a demonstration prototype expert system for fault management for the Solid Amine, Water Desorbed (SAWD) CO2 removal assembly, associated with the Environmental Control and Life Support (ECLS) System. ARGES monitors and reduces data in real time from either the SAWD controller or a simulation of the SAWD assembly. It can detect gradual degradations or predict failures. This allows graceful shutdown and scheduled maintenance, which reduces crew maintenance overhead. Status and fault information is presented in a user interface that simulates what would be seen by a crewperson. The user interface employs animated color graphics and an object oriented approach to provide detailed status information, fault identification, and explanation of reasoning in a rapidly assimulated manner. In addition, ARGES recommends possible courses of action for predicted and actual faults. ARGES is seen as a forerunner of AI-based fault management systems for manned space systems.

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.

Fault Diagnosis of Demountable Disk-Drum Aero-Engine Rotor Using Customized Multiwavelet Method

PubMed Central

Chen, Jinglong; Wang, Yu; He, Zhengjia; Wang, Xiaodong

2015-01-01

The demountable disk-drum aero-engine rotor is an important piece of equipment that greatly impacts the safe operation of aircraft. However, assembly looseness or crack fault has led to several unscheduled breakdowns and serious accidents. Thus, condition monitoring and fault diagnosis technique are required for identifying abnormal conditions. Customized ensemble multiwavelet method for aero-engine rotor condition identification, using measured vibration data, is developed in this paper. First, customized multiwavelet basis function with strong adaptivity is constructed via symmetric multiwavelet lifting scheme. Then vibration signal is processed by customized ensemble multiwavelet transform. Next, normalized information entropy of multiwavelet decomposition coefficients is computed to directly reflect and evaluate the condition. The proposed approach is first applied to fault detection of an experimental aero-engine rotor. Finally, the proposed approach is used in an engineering application, where it successfully identified the crack fault of a demountable disk-drum aero-engine rotor. The results show that the proposed method possesses excellent performance in fault detection of aero-engine rotor. Moreover, the robustness of the multiwavelet method against noise is also tested and verified by simulation and field experiments. PMID:26512668

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.

SSME fault monitoring and diagnosis expert system

NASA Technical Reports Server (NTRS)

Ali, Moonis; Norman, Arnold M.; Gupta, U. K.

1989-01-01

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

A System for Fault Management and Fault Consequences Analysis for NASA's Deep Space Habitat

NASA Technical Reports Server (NTRS)

Colombano, Silvano; Spirkovska, Liljana; Baskaran, Vijaykumar; Aaseng, Gordon; McCann, Robert S.; Ossenfort, John; Smith, Irene; Iverson, David L.; Schwabacher, Mark

2013-01-01

NASA's exploration program envisions the utilization of a Deep Space Habitat (DSH) for human exploration of the space environment in the vicinity of Mars and/or asteroids. Communication latencies with ground control of as long as 20+ minutes make it imperative that DSH operations be highly autonomous, as any telemetry-based detection of a systems problem on Earth could well occur too late to assist the crew with the problem. A DSH-based development program has been initiated to develop and test the automation technologies necessary to support highly autonomous DSH operations. One such technology is a fault management tool to support performance monitoring of vehicle systems operations and to assist with real-time decision making in connection with operational anomalies and failures. Toward that end, we are developing Advanced Caution and Warning System (ACAWS), a tool that combines dynamic and interactive graphical representations of spacecraft systems, systems modeling, automated diagnostic analysis and root cause identification, system and mission impact assessment, and mitigation procedure identification to help spacecraft operators (both flight controllers and crew) understand and respond to anomalies more effectively. In this paper, we describe four major architecture elements of ACAWS: Anomaly Detection, Fault Isolation, System Effects Analysis, and Graphic User Interface (GUI), and how these elements work in concert with each other and with other tools to provide fault management support to both the controllers and crew. We then describe recent evaluations and tests of ACAWS on the DSH testbed. The results of these tests support the feasibility and strength of our approach to failure management automation and enhanced operational autonomy

Asymmetry identification in rigid rotating bodies—Theory and experiment

NASA Astrophysics Data System (ADS)

Bucher, Izhak; Shomer, Ofer

2013-12-01

Asymmetry and anisotropy are important parameters in rotating devices that can cause instability; indicate a manufacturing defect or a developing fault. The present paper discusses an identification method capable of detecting minute levels of asymmetry by exploiting the unique dynamics of parametric excitation caused by asymmetry and rotation. The detection relies on rigid body dynamics without resorting to nonlinear vibration analysis, and the natural dynamics of elastically supported systems is exploited in order to increase the sensitivity to asymmetry. It is possible to isolate asymmetry from other rotation-induced phenomena like unbalance. An asymmetry detection machine which was built in the laboratory demonstrates the method alongside theoretical analysis.

Quantitative Index and Abnormal Alarm Strategy Using Sensor-Dependent Vibration Data for Blade Crack Identification in Centrifugal Booster Fans

PubMed Central

Chen, Jinglong; Sun, Hailiang; Wang, Shuai; He, Zhengjia

2016-01-01

Centrifugal booster fans are important equipment used to recover blast furnace gas (BFG) for generating electricity, but blade crack faults (BCFs) in centrifugal booster fans can lead to unscheduled breakdowns and potentially serious accidents, so in this work quantitative fault identification and an abnormal alarm strategy based on acquired historical sensor-dependent vibration data is proposed for implementing condition-based maintenance for this type of equipment. Firstly, three group dependent sensors are installed to acquire running condition data. Then a discrete spectrum interpolation method and short time Fourier transform (STFT) are applied to preliminarily identify the running data in the sensor-dependent vibration data. As a result a quantitative identification and abnormal alarm strategy based on compound indexes including the largest Lyapunov exponent and relative energy ratio at the second harmonic frequency component is proposed. Then for validation the proposed blade crack quantitative identification and abnormality alarm strategy is applied to analyze acquired experimental data for centrifugal booster fans and it has successfully identified incipient blade crack faults. In addition, the related mathematical modelling work is also introduced to investigate the effects of mistuning and cracks on the vibration features of centrifugal impellers and to explore effective techniques for crack detection. PMID:27171083

Improved chemical identification from sensor arrays using intelligent algorithms

NASA Astrophysics Data System (ADS)

Roppel, Thaddeus A.; Wilson, Denise M.

2001-02-01

Intelligent signal processing algorithms are shown to improve identification rates significantly in chemical sensor arrays. This paper focuses on the use of independently derived sensor status information to modify the processing of sensor array data by using a fast, easily-implemented "best-match" approach to filling in missing sensor data. Most fault conditions of interest (e.g., stuck high, stuck low, sudden jumps, excess noise, etc.) can be detected relatively simply by adjunct data processing, or by on-board circuitry. The objective then is to devise, implement, and test methods for using this information to improve the identification rates in the presence of faulted sensors. In one typical example studied, utilizing separately derived, a-priori knowledge about the health of the sensors in the array improved the chemical identification rate by an artificial neural network from below 10 percent correct to over 99 percent correct. While this study focuses experimentally on chemical sensor arrays, the results are readily extensible to other types of sensor platforms.

Aircraft Flight Envelope Determination using Upset Detection and Physical Modeling Methods

NASA Technical Reports Server (NTRS)

Keller, Jeffrey D.; McKillip, Robert M. Jr.; Kim, Singwan

2009-01-01

The development of flight control systems to enhance aircraft safety during periods of vehicle impairment or degraded operations has been the focus of extensive work in recent years. Conditions adversely affecting aircraft flight operations and safety may result from a number of causes, including environmental disturbances, degraded flight operations, and aerodynamic upsets. To enhance the effectiveness of adaptive and envelope limiting controls systems, it is desirable to examine methods for identifying the occurrence of anomalous conditions and for assessing the impact of these conditions on the aircraft operational limits. This paper describes initial work performed toward this end, examining the use of fault detection methods applied to the aircraft for aerodynamic performance degradation identification and model-based methods for envelope prediction. Results are presented in which a model-based fault detection filter is applied to the identification of aircraft control surface and stall departure failures/upsets. This application is supported by a distributed loading aerodynamics formulation for the flight dynamics system reference model. Extensions for estimating the flight envelope due to generalized aerodynamic performance degradation are also described.

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.

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.

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.

Closed-Loop Evaluation of an Integrated Failure Identification and Fault Tolerant Control System for a Transport Aircraft

NASA Technical Reports Server (NTRS)

Shin, Jong-Yeob; Belcastro, Christine; Khong, thuan

2006-01-01

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems developed for failure detection, identification, and reconfiguration, as well as upset recovery, need to be evaluated over broad regions of the flight envelope or under extreme flight conditions, and should include various sources of uncertainty. To apply formal robustness analysis, formulation of linear fractional transformation (LFT) models of complex parameter-dependent systems is required, which represent system uncertainty due to parameter uncertainty and actuator faults. This paper describes a detailed LFT model formulation procedure from the nonlinear model of a transport aircraft by using a preliminary LFT modeling software tool developed at the NASA Langley Research Center, which utilizes a matrix-based computational approach. The closed-loop system is evaluated over the entire flight envelope based on the generated LFT model which can cover nonlinear dynamics. The robustness analysis results of the closed-loop fault tolerant control system of a transport aircraft are presented. A reliable flight envelope (safe flight regime) is also calculated from the robust performance analysis results, over which the closed-loop system can achieve the desired performance of command tracking and failure detection.

A structural model decomposition framework for systems health management

NASA Astrophysics Data System (ADS)

Roychoudhury, I.; Daigle, M.; Bregon, A.; Pulido, B.

Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.

A Structural Model Decomposition Framework for Systems Health Management

NASA Technical Reports Server (NTRS)

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

2013-01-01

Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.

Fault detection in rotating machines with beamforming: Spatial visualization of diagnosis features

NASA Astrophysics Data System (ADS)

Cardenas Cabada, E.; Leclere, Q.; Antoni, J.; Hamzaoui, N.

2017-12-01

Rotating machines diagnosis is conventionally related to vibration analysis. Sensors are usually placed on the machine to gather information about its components. The recorded signals are then processed through a fault detection algorithm allowing the identification of the failing part. This paper proposes an acoustic-based diagnosis method. A microphone array is used to record the acoustic field radiated by the machine. The main advantage over vibration-based diagnosis is that the contact between the sensors and the machine is no longer required. Moreover, the application of acoustic imaging makes possible the identification of the sources of acoustic radiation on the machine surface. The display of information is then spatially continuous while the accelerometers only give it discrete. Beamforming provides the time-varying signals radiated by the machine as a function of space. Any fault detection tool can be applied to the beamforming output. Spectral kurtosis, which highlights the impulsiveness of a signal as function of frequency, is used in this study. The combination of spectral kurtosis with acoustic imaging makes possible the mapping of the impulsiveness as a function of space and frequency. The efficiency of this approach lays on the source separation in the spatial and frequency domains. These mappings make possible the localization of such impulsive sources. The faulty components of the machine have an impulsive behavior and thus will be highlighted on the mappings. The study presents experimental validations of the method on rotating machines.

A Diagnostic Approach for Electro-Mechanical Actuators in Aerospace Systems

NASA Technical Reports Server (NTRS)

Balaban, Edward; Saxena, Abhinav; Bansal, Prasun; Goebel, Kai Frank; Stoelting, Paul; Curran, Simon

2009-01-01

Electro-mechanical actuators (EMA) are finding increasing use in aerospace applications, especially with the trend towards all all-electric aircraft and spacecraft designs. However, electro-mechanical actuators still lack the knowledge base accumulated for other fielded actuator types, particularly with regard to fault detection and characterization. This paper presents a thorough analysis of some of the critical failure modes documented for EMAs and describes experiments conducted on detecting and isolating a subset of them. The list of failures has been prepared through an extensive Failure Modes and Criticality Analysis (FMECA) reference, literature review, and accessible industry experience. Methods for data acquisition and validation of algorithms on EMA test stands are described. A variety of condition indicators were developed that enabled detection, identification, and isolation among the various fault modes. A diagnostic algorithm based on an artificial neural network is shown to operate successfully using these condition indicators and furthermore, robustness of these diagnostic routines to sensor faults is demonstrated by showing their ability to distinguish between them and component failures. The paper concludes with a roadmap leading from this effort towards developing successful prognostic algorithms for electromechanical actuators.

Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2006-01-01

A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor or actuator fault occurs, large estimation errors are generated by all filters except the one using the correct hypothesis. By monitoring the residual output of each filter, the specific fault that has occurred can be detected and isolated on the basis of the decision rules. A set of parameters that indicate the performance of the engine components is estimated by the "correct" Kalman filter for use in detecting component faults. To reduce the loss of diagnostic accuracy and sensitivity in the face of aging, the FDI system accepts information from a steady-state-condition-monitoring system. This information is used to update the Kalman filters and a data bank of trim values representative of the current aging condition.

A System for Fault Management for NASA's Deep Space Habitat

NASA Technical Reports Server (NTRS)

Colombano, Silvano P.; Spirkovska, Liljana; Aaseng, Gordon B.; Mccann, Robert S.; Baskaran, Vijayakumar; Ossenfort, John P.; Smith, Irene Skupniewicz; Iverson, David L.; Schwabacher, Mark A.

2013-01-01

NASA's exploration program envisions the utilization of a Deep Space Habitat (DSH) for human exploration of the space environment in the vicinity of Mars and/or asteroids. Communication latencies with ground control of as long as 20+ minutes make it imperative that DSH operations be highly autonomous, as any telemetry-based detection of a systems problem on Earth could well occur too late to assist the crew with the problem. A DSH-based development program has been initiated to develop and test the automation technologies necessary to support highly autonomous DSH operations. One such technology is a fault management tool to support performance monitoring of vehicle systems operations and to assist with real-time decision making in connection with operational anomalies and failures. Toward that end, we are developing Advanced Caution and Warning System (ACAWS), a tool that combines dynamic and interactive graphical representations of spacecraft systems, systems modeling, automated diagnostic analysis and root cause identification, system and mission impact assessment, and mitigation procedure identification to help spacecraft operators (both flight controllers and crew) understand and respond to anomalies more effectively. In this paper, we describe four major architecture elements of ACAWS: Anomaly Detection, Fault Isolation, System Effects Analysis, and Graphic User Interface (GUI), and how these elements work in concert with each other and with other tools to provide fault management support to both the controllers and crew. We then describe recent evaluations and tests of ACAWS on the DSH testbed. The results of these tests support the feasibility and strength of our approach to failure management automation and enhanced operational autonomy.

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.

Intelligent Engine Systems Work Element 1.3: Sub System Health Management

NASA Technical Reports Server (NTRS)

Ashby, Malcolm; Simpson, Jeffrey; Singh, Anant; Ferguson, Emily; Frontera, mark

2005-01-01

The objectives of this program were to develop health monitoring systems and physics-based fault detection models for engine sub-systems including the start, lubrication, and fuel. These models will ultimately be used to provide more effective sub-system fault identification and isolation to reduce engine maintenance costs and engine down-time. Additionally, the bearing sub-system health is addressed in this program through identification of sensing requirements, a review of available technologies and a demonstration of a demonstration of a conceptual monitoring system for a differential roller bearing. This report is divided into four sections; one for each of the subtasks. The start system subtask is documented in section 2.0, the oil system is covered in section 3.0, bearing in section 4.0, and the fuel system is presented in section 5.0.

Development and Evaluation of Fault-Tolerant Flight Control Systems

NASA Technical Reports Server (NTRS)

Song, Yong D.; Gupta, Kajal (Technical Monitor)

2004-01-01

The research is concerned with developing a new approach to enhancing fault tolerance of flight control systems. The original motivation for fault-tolerant control comes from the need for safe operation of control elements (e.g. actuators) in the event of hardware failures in high reliability systems. One such example is modem space vehicle subjected to actuator/sensor impairments. A major task in flight control is to revise the control policy to balance impairment detectability and to achieve sufficient robustness. This involves careful selection of types and parameters of the controllers and the impairment detecting filters used. It also involves a decision, upon the identification of some failures, on whether and how a control reconfiguration should take place in order to maintain a certain system performance level. In this project new flight dynamic model under uncertain flight conditions is considered, in which the effects of both ramp and jump faults are reflected. Stabilization algorithms based on neural network and adaptive method are derived. The control algorithms are shown to be effective in dealing with uncertain dynamics due to external disturbances and unpredictable faults. The overall strategy is easy to set up and the computation involved is much less as compared with other strategies. Computer simulation software is developed. A serious of simulation studies have been conducted with varying flight conditions.

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Feature Detection in SAR Interferograms With Missing Data Displays Fault Slip Near El Mayor-Cucapah and South Napa Earthquakes

NASA Astrophysics Data System (ADS)

Parker, J. W.; Donnellan, A.; Glasscoe, M. T.; Stough, T.

2015-12-01

Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification, demonstrated in 2010, was not robust: for best results, it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Proper treatment of missing data, bursting noise patches, and tiny noise differences at short distances apart from bursts are essential to creating an acceptably reliable method sensitive to small near-surface fractures. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features: hand-crafted intervention will not do. Effective methods of identifying, removing and filling in bad pixels reveal significant features of surface fractures. A rich network of edges (probably fractures and subsidence) in difference images spanning the South Napa earthquake give way to a simple set of postseismically slipping faults. Coseismic El Mayor-Cucapah interferograms compared to post-seismic difference images show nearly disjoint patterns of surface fractures in California's Sonoran Desert; the combined pattern reveals a network of near-perpendicular, probably conjugate faults not mapped before the earthquake. The current algorithms for UAVSAR interferogram edge detections are shown to be effective in difficult environments, including agricultural (Napa, Imperial Valley) and difficult urban areas (Orange County.).

The Identification of Software Failure Regions

DTIC Science & Technology

1990-06-01

be used to detect non-obviously redundant test cases. A preliminary examination of the manual analysis method is performed with a set of programs ...failure regions are defined and a method of failure region analysis is described in detail. The thesis describes how this analysis may be used to detect...is the termination of the ability of a functional unit to perform its required function. (Glossary, 1983) The presence of faults in program code

Eigenstructure Assignment for Fault Tolerant Flight Control Design

NASA Technical Reports Server (NTRS)

Sobel, Kenneth; Joshi, Suresh (Technical Monitor)

2002-01-01

In recent years, fault tolerant flight control systems have gained an increased interest for high performance military aircraft as well as civil aircraft. Fault tolerant control systems can be described as either active or passive. An active fault tolerant control system has to either reconfigure or adapt the controller in response to a failure. One approach is to reconfigure the controller based upon detection and identification of the failure. Another approach is to use direct adaptive control to adjust the controller without explicitly identifying the failure. In contrast, a passive fault tolerant control system uses a fixed controller which achieves acceptable performance for a presumed set of failures. We have obtained a passive fault tolerant flight control law for the F/A-18 aircraft which achieves acceptable handling qualities for a class of control surface failures. The class of failures includes the symmetric failure of any one control surface being stuck at its trim value. A comparison was made of an eigenstructure assignment gain designed for the unfailed aircraft with a fault tolerant multiobjective optimization gain. We have shown that time responses for the unfailed aircraft using the eigenstructure assignment gain and the fault tolerant gain are identical. Furthermore, the fault tolerant gain achieves MIL-F-8785C specifications for all failure conditions.

An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines.

PubMed

Amozegar, M; Khorasani, K

2016-04-01

In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

The reflection of evolving bearing faults in the stator current's extended park vector approach for induction machines

NASA Astrophysics Data System (ADS)

Corne, Bram; Vervisch, Bram; Derammelaere, Stijn; Knockaert, Jos; Desmet, Jan

2018-07-01

Stator current analysis has the potential of becoming the most cost-effective condition monitoring technology regarding electric rotating machinery. Since both electrical and mechanical faults are detected by inexpensive and robust current-sensors, measuring current is advantageous on other techniques such as vibration, acoustic or temperature analysis. However, this technology is struggling to breach into the market of condition monitoring as the electrical interpretation of mechanical machine-problems is highly complicated. Recently, the authors built a test-rig which facilitates the emulation of several representative mechanical faults on an 11 kW induction machine with high accuracy and reproducibility. Operating this test-rig, the stator current of the induction machine under test can be analyzed while mechanical faults are emulated. Furthermore, while emulating, the fault-severity can be manipulated adaptively under controllable environmental conditions. This creates the opportunity of examining the relation between the magnitude of the well-known current fault components and the corresponding fault-severity. This paper presents the emulation of evolving bearing faults and their reflection in the Extended Park Vector Approach for the 11 kW induction machine under test. The results confirm the strong relation between the bearing faults and the stator current fault components in both identification and fault-severity. Conclusively, stator current analysis increases reliability in the application as a complete, robust, on-line condition monitoring technology.

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.

Wavelet subspace decomposition of thermal infrared images for defect detection in artworks

NASA Astrophysics Data System (ADS)

Ahmad, M. Z.; Khan, A. A.; Mezghani, S.; Perrin, E.; Mouhoubi, K.; Bodnar, J. L.; Vrabie, V.

2016-07-01

Health of ancient artworks must be routinely monitored for their adequate preservation. Faults in these artworks may develop over time and must be identified as precisely as possible. The classical acoustic testing techniques, being invasive, risk causing permanent damage during periodic inspections. Infrared thermometry offers a promising solution to map faults in artworks. It involves heating the artwork and recording its thermal response using infrared camera. A novel strategy based on pseudo-random binary excitation principle is used in this work to suppress the risks associated with prolonged heating. The objective of this work is to develop an automatic scheme for detecting faults in the captured images. An efficient scheme based on wavelet based subspace decomposition is developed which favors identification of, the otherwise invisible, weaker faults. Two major problems addressed in this work are the selection of the optimal wavelet basis and the subspace level selection. A novel criterion based on regional mutual information is proposed for the latter. The approach is successfully tested on a laboratory based sample as well as real artworks. A new contrast enhancement metric is developed to demonstrate the quantitative efficiency of the algorithm. The algorithm is successfully deployed for both laboratory based and real artworks.

Fault zone identification in the eastern part of the Persian Gulf based on combined seismic attributes

NASA Astrophysics Data System (ADS)

Mirkamali, M. S.; Keshavarz FK, N.; Bakhtiari, M. R.

2013-02-01

Faults, as main pathways for fluids, play a critical role in creating regions of high porosity and permeability, in cutting cap rock and in the migration of hydrocarbons into the reservoir. Therefore, accurate identification of fault zones is very important in maximizing production from petroleum traps. Image processing and modern visualization techniques are provided for better mapping of objects of interest. In this study, the application of fault mapping in the identification of fault zones within the Mishan and Aghajari formations above the Guri base unconformity surface in the eastern part of Persian Gulf is investigated. Seismic single- and multi-trace attribute analyses are employed separately to determine faults in a vertical section, but different kinds of geological objects cannot be identified using individual attributes only. A mapping model is utilized to improve the identification of the faults, giving more accurate results. This method is based on combinations of all individual relevant attributes using a neural network system to create combined attributes, which gives an optimal view of the object of interest. Firstly, a set of relevant attributes were separately calculated on the vertical section. Then, at interpreted positions, some example training locations were manually selected in each fault and non-fault class by an interpreter. A neural network was trained on combinations of the attributes extracted at the example training locations to generate an optimized fault cube. Finally, the results of the fault and nonfault probability cube were estimated, which the neural network applied to the entire data set. The fault probability cube was obtained with higher mapping accuracy and greater contrast, and with fewer disturbances in comparison with individual attributes. The computed results of this study can support better understanding of the data, providing fault zone mapping with reliable results.

LiDAR-Assisted identification of an active fault near Truckee, California

USGS Publications Warehouse

Hunter, L.E.; Howle, J.F.; Rose, R.S.; Bawden, G.W.

2011-01-01

We use high-resolution (1.5-2.4 points/m2) bare-earth airborne Light Detection and Ranging (LiDAR) imagery to identify, map, constrain, and visualize fault-related geomorphology in densely vegetated terrain surrounding Martis Creek Dam near Truckee, California. Bare-earth LiDAR imagery reveals a previously unrecognized and apparently youthful right-lateral strike-slip fault that exhibits laterally continuous tectonic geomorphic features over a 35-km-long zone. If these interpretations are correct, the fault, herein named the Polaris fault, may represent a significant seismic hazard to the greater Truckee-Lake Tahoe and Reno-Carson City regions. Three-dimensional modeling of an offset late Quaternary terrace riser indicates a minimum tectonic slip rate of 0.4 ?? 0.1 mm/yr.Mapped fault patterns are fairly typical of regional patterns elsewhere in the northern Walker Lane and are in strong coherence with moderate magnitude historical seismicity of the immediate area, as well as the current regional stress regime. Based on a range of surface-rupture lengths and depths to the base of the seismogenic zone, we estimate a maximum earthquake magnitude (M) for the Polaris fault to be between 6.4 and 6.9.

A fuzzy Petri-net-based mode identification algorithm for fault diagnosis of complex systems

NASA Astrophysics Data System (ADS)

Propes, Nicholas C.; Vachtsevanos, George

2003-08-01

Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or "shock" operating conditions, which tend to severely impact fault diagnosis or the progression of a fault leading to a failure. Fault conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.

21 CFR 880.5725 - Infusion pump.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Infusion pump. 880.5725 Section 880.5725 Food and... Infusion pump. (a) Identification. An infusion pump is a device used in a health care facility to pump... means to detect a fault condition, such as air in, or blockage of, the infusion line and to activate an...

21 CFR 880.5725 - Infusion pump.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Infusion pump. 880.5725 Section 880.5725 Food and... Infusion pump. (a) Identification. An infusion pump is a device used in a health care facility to pump... means to detect a fault condition, such as air in, or blockage of, the infusion line and to activate an...

Dynamic characteristics of a 20 kHz resonant power system - Fault identification and fault recovery

NASA Technical Reports Server (NTRS)

Wasynczuk, O.

1988-01-01

A detailed simulation of a dc inductor resonant driver and receiver is used to demonstrate the transient characteristics of a 20 kHz resonant power system during fault and overload conditions. The simulated system consists of a dc inductor resonant inverter (driver), a 50-meter transmission cable, and a dc inductor resonant receiver load. Of particular interest are the driver and receiver performance during fault and overload conditions and on the recovery characteristics following removal of the fault. The information gained from these studies sets the stage for further work in fault identification and autonomous power system control.

Joint University Program for Air Transportation Research, 1990-1991

NASA Technical Reports Server (NTRS)

Morrell, Frederick R. (Compiler)

1991-01-01

The goals of this program are consistent with the interests of both NASA and the FAA in furthering the safety and efficiency of the National Airspace System. Research carried out at the Massachusetts Institute of Technology (MIT), Ohio University, and Princeton University are covered. Topics studied include passive infrared ice detection for helicopters, the cockpit display of hazardous windshear information, fault detection and isolation for multisensor navigation systems, neural networks for aircraft system identification, and intelligent failure tolerant control.

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.

Tidal triggering of earthquakes suggests poroelastic behavior on the San Andreas Fault

DOE PAGES

Delorey, Andrew A.; van der Elst, Nicholas J.; Johnson, Paul Allan

2016-12-28

Tidal triggering of earthquakes is hypothesized to provide quantitative information regarding the fault's stress state, poroelastic properties, and may be significant for our understanding of seismic hazard. To date, studies of regional or global earthquake catalogs have had only modest successes in identifying tidal triggering. We posit that the smallest events that may provide additional evidence of triggering go unidentified and thus we developed a technique to improve the identification of very small magnitude events. We identify events applying a method known as inter-station seismic coherence where we prioritize detection and discrimination over characterization. Here we show tidal triggering ofmore » earthquakes on the San Andreas Fault. We find the complex interaction of semi-diurnal and fortnightly tidal periods exposes both stress threshold and critical state behavior. Lastly, our findings reveal earthquake nucleation processes and pore pressure conditions – properties of faults that are difficult to measure, yet extremely important for characterizing earthquake physics and seismic hazards.« less

Tidal triggering of earthquakes suggests poroelastic behavior on the San Andreas Fault

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

Delorey, Andrew A.; van der Elst, Nicholas J.; Johnson, Paul Allan

Tidal triggering of earthquakes is hypothesized to provide quantitative information regarding the fault's stress state, poroelastic properties, and may be significant for our understanding of seismic hazard. To date, studies of regional or global earthquake catalogs have had only modest successes in identifying tidal triggering. We posit that the smallest events that may provide additional evidence of triggering go unidentified and thus we developed a technique to improve the identification of very small magnitude events. We identify events applying a method known as inter-station seismic coherence where we prioritize detection and discrimination over characterization. Here we show tidal triggering ofmore » earthquakes on the San Andreas Fault. We find the complex interaction of semi-diurnal and fortnightly tidal periods exposes both stress threshold and critical state behavior. Lastly, our findings reveal earthquake nucleation processes and pore pressure conditions – properties of faults that are difficult to measure, yet extremely important for characterizing earthquake physics and seismic hazards.« less

Tidal triggering of earthquakes suggests poroelastic behavior on the San Andreas Fault

USGS Publications Warehouse

Delorey, Andrew; Van Der Elst, Nicholas; Johnson, Paul

2017-01-01

Tidal triggering of earthquakes is hypothesized to provide quantitative information regarding the fault's stress state, poroelastic properties, and may be significant for our understanding of seismic hazard. To date, studies of regional or global earthquake catalogs have had only modest successes in identifying tidal triggering. We posit that the smallest events that may provide additional evidence of triggering go unidentified and thus we developed a technique to improve the identification of very small magnitude events. We identify events applying a method known as inter-station seismic coherence where we prioritize detection and discrimination over characterization. Here we show tidal triggering of earthquakes on the San Andreas Fault. We find the complex interaction of semi-diurnal and fortnightly tidal periods exposes both stress threshold and critical state behavior. Our findings reveal earthquake nucleation processes and pore pressure conditions – properties of faults that are difficult to measure, yet extremely important for characterizing earthquake physics and seismic hazards.

Data-driven simultaneous fault diagnosis for solid oxide fuel cell system using multi-label pattern identification

NASA Astrophysics Data System (ADS)

Li, Shuanghong; Cao, Hongliang; Yang, Yupu

2018-02-01

Fault diagnosis is a key process for the reliability and safety of solid oxide fuel cell (SOFC) systems. However, it is difficult to rapidly and accurately identify faults for complicated SOFC systems, especially when simultaneous faults appear. In this research, a data-driven Multi-Label (ML) pattern identification approach is proposed to address the simultaneous fault diagnosis of SOFC systems. The framework of the simultaneous-fault diagnosis primarily includes two components: feature extraction and ML-SVM classifier. The simultaneous-fault diagnosis approach can be trained to diagnose simultaneous SOFC faults, such as fuel leakage, air leakage in different positions in the SOFC system, by just using simple training data sets consisting only single fault and not demanding simultaneous faults data. The experimental result shows the proposed framework can diagnose the simultaneous SOFC system faults with high accuracy requiring small number training data and low computational burden. In addition, Fault Inference Tree Analysis (FITA) is employed to identify the correlations among possible faults and their corresponding symptoms at the system component level.

Gain-Scheduled Fault Tolerance Control Under False Identification

NASA Technical Reports Server (NTRS)

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

2006-01-01

An active fault tolerant control (FTC) law is generally sensitive to false identification since the control gain is reconfigured for fault occurrence. In the conventional FTC law design procedure, dynamic variations due to false identification are not considered. In this paper, an FTC synthesis method is developed in order to consider possible variations of closed-loop dynamics under false identification into the control design procedure. An active FTC synthesis problem is formulated into an LMI optimization problem to minimize the upper bound of the induced-L2 norm which can represent the worst-case performance degradation due to false identification. The developed synthesis method is applied for control of the longitudinal motions of FASER (Free-flying Airplane for Subscale Experimental Research). The designed FTC law of the airplane is simulated for pitch angle command tracking under a false identification case.

Application of power spectrum, cepstrum, higher order spectrum and neural network analyses for induction motor fault diagnosis

NASA Astrophysics Data System (ADS)

Liang, B.; Iwnicki, S. D.; Zhao, Y.

2013-08-01

The power spectrum is defined as the square of the magnitude of the Fourier transform (FT) of a signal. The advantage of FT analysis is that it allows the decomposition of a signal into individual periodic frequency components and establishes the relative intensity of each component. It is the most commonly used signal processing technique today. If the same principle is applied for the detection of periodicity components in a Fourier spectrum, the process is called the cepstrum analysis. Cepstrum analysis is a very useful tool for detection families of harmonics with uniform spacing or the families of sidebands commonly found in gearbox, bearing and engine vibration fault spectra. Higher order spectra (HOS) (also known as polyspectra) consist of higher order moment of spectra which are able to detect non-linear interactions between frequency components. For HOS, the most commonly used is the bispectrum. The bispectrum is the third-order frequency domain measure, which contains information that standard power spectral analysis techniques cannot provide. It is well known that neural networks can represent complex non-linear relationships, and therefore they are extremely useful for fault identification and classification. This paper presents an application of power spectrum, cepstrum, bispectrum and neural network for fault pattern extraction of induction motors. The potential for using the power spectrum, cepstrum, bispectrum and neural network as a means for differentiating between healthy and faulty induction motor operation is examined. A series of experiments is done and the advantages and disadvantages between them are discussed. It has been found that a combination of power spectrum, cepstrum and bispectrum plus neural network analyses could be a very useful tool for condition monitoring and fault diagnosis of induction motors.

Health monitoring system for transmission shafts based on adaptive parameter identification

NASA Astrophysics Data System (ADS)

Souflas, I.; Pezouvanis, A.; Ebrahimi, K. M.

2018-05-01

A health monitoring system for a transmission shaft is proposed. The solution is based on the real-time identification of the physical characteristics of the transmission shaft i.e. stiffness and damping coefficients, by using a physical oriented model and linear recursive identification. The efficacy of the suggested condition monitoring system is demonstrated on a prototype transient engine testing facility equipped with a transmission shaft capable of varying its physical properties. Simulation studies reveal that coupling shaft faults can be detected and isolated using the proposed condition monitoring system. Besides, the performance of various recursive identification algorithms is addressed. The results of this work recommend that the health status of engine dynamometer shafts can be monitored using a simple lumped-parameter shaft model and a linear recursive identification algorithm which makes the concept practically viable.

Software For Fault-Tree Diagnosis Of A System

NASA Technical Reports Server (NTRS)

Iverson, Dave; Patterson-Hine, Ann; Liao, Jack

1993-01-01

Fault Tree Diagnosis System (FTDS) computer program is automated-diagnostic-system program identifying likely causes of specified failure on basis of information represented in system-reliability mathematical models known as fault trees. Is modified implementation of failure-cause-identification phase of Narayanan's and Viswanadham's methodology for acquisition of knowledge and reasoning in analyzing failures of systems. Knowledge base of if/then rules replaced with object-oriented fault-tree representation. Enhancement yields more-efficient identification of causes of failures and enables dynamic updating of knowledge base. Written in C language, C++, and Common LISP.

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.

On Restructurable Control System Theory

NASA Technical Reports Server (NTRS)

Athans, M.

1983-01-01

The state of stochastic system and control theory as it impacts restructurable control issues is addressed. The multivariable characteristics of the control problem are addressed. The failure detection/identification problem is discussed as a multi-hypothesis testing problem. Control strategy reconfiguration, static multivariable controls, static failure hypothesis testing, dynamic multivariable controls, fault-tolerant control theory, dynamic hypothesis testing, generalized likelihood ratio (GLR) methods, and adaptive control are discussed.

Comparing Different Fault Identification Algorithms in Distributed Power System

NASA Astrophysics Data System (ADS)

Alkaabi, Salim

A power system is a huge complex system that delivers the electrical power from the generation units to the consumers. As the demand for electrical power increases, distributed power generation was introduced to the power system. Faults may occur in the power system at any time in different locations. These faults cause a huge damage to the system as they might lead to full failure of the power system. Using distributed generation in the power system made it even harder to identify the location of the faults in the system. The main objective of this work is to test the different fault location identification algorithms while tested on a power system with the different amount of power injected using distributed generators. As faults may lead the system to full failure, this is an important area for research. In this thesis different fault location identification algorithms have been tested and compared while the different amount of power is injected from distributed generators. The algorithms were tested on IEEE 34 node test feeder using MATLAB and the results were compared to find when these algorithms might fail and the reliability of these methods.

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.

Rocket Engine Health Management: Early Definition of Critical Flight Measurements

NASA Technical Reports Server (NTRS)

Christenson, Rick L.; Nelson, Michael A.; Butas, John P.

2003-01-01

The NASA led Space Launch Initiative (SLI) program has established key requirements related to safety, reliability, launch availability and operations cost to be met by the next generation of reusable launch vehicles. Key to meeting these requirements will be an integrated vehicle health management ( M) system that includes sensors, harnesses, software, memory, and processors. Such a system must be integrated across all the vehicle subsystems and meet component, subsystem, and system requirements relative to fault detection, fault isolation, and false alarm rate. The purpose of this activity is to evolve techniques for defining critical flight engine system measurements-early within the definition of an engine health management system (EHMS). Two approaches, performance-based and failure mode-based, are integrated to provide a proposed set of measurements to be collected. This integrated approach is applied to MSFC s MC-1 engine. Early identification of measurements supports early identification of candidate sensor systems whose design and impacts to the engine components must be considered in engine design.

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.

Identification and interpretation of tectonic features from Skylab imagery. [Mojave Desert and San Bernardino Mountains, California

NASA Technical Reports Server (NTRS)

Abdel-Gawad, M. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Surface indications of recent faulting are generally observed in EREP S190B photographs. Comparison of secondary roads (asphalt and dirt) registered in EREP and U-2 photographs together with field measurements shows that dirt and asphalt roads about seven meters wide can be detected in EREP S190B photographs where sufficient contrast exists between the tone of the road surface and surrounding terrain. In low contrast cases, roads more than ten meters wide could not be detected.

Application of Subspace Detection to the 6 November 2011 M5.6 Prague, Oklahoma Aftershock Sequence

NASA Astrophysics Data System (ADS)

McMahon, N. D.; Benz, H.; Johnson, C. E.; Aster, R. C.; McNamara, D. E.

2015-12-01

Subspace detection is a powerful tool for the identification of small seismic events. Subspace detectors improve upon single-event matched filtering techniques by using multiple orthogonal waveform templates whose linear combinations characterize a range of observed signals from previously identified earthquakes. Subspace detectors running on multiple stations can significantly increasing the number of locatable events, lowering the catalog's magnitude of completeness and thus providing extraordinary detail on the kinematics of the aftershock process. The 6 November 2011 M5.6 earthquake near Prague, Oklahoma is the largest earthquake instrumentally recorded in Oklahoma history and the largest earthquake resultant from deep wastewater injection. A M4.8 foreshock on 5 November 2011 and the M5.6 mainshock triggered tens of thousands of detectable aftershocks along a 20 km splay of the Wilzetta Fault Zone known as the Meeker-Prague fault. In response to this unprecedented earthquake, 21 temporary seismic stations were deployed surrounding the seismic activity. We utilized a catalog of 767 previously located aftershocks to construct subspace detectors for the 21 temporary and 10 closest permanent seismic stations. Subspace detection identified more than 500,000 new arrival-time observations, which associated into more than 20,000 locatable earthquakes. The associated earthquakes were relocated using the Bayesloc multiple-event locator, resulting in ~7,000 earthquakes with hypocentral uncertainties of less than 500 m. The relocated seismicity provides unique insight into the spatio-temporal evolution of the aftershock sequence along the Wilzetta Fault Zone and its associated structures. We find that the crystalline basement and overlying sedimentary Arbuckle formation accommodate the majority of aftershocks. While we observe aftershocks along the entire 20 km length of the Meeker-Prague fault, the vast majority of earthquakes were confined to a 9 km wide by 9 km deep surface striking N54°E and dipping 83° to the northwest near the junction of the splay with the main Wilzetta fault structure. Relocated seismicity shows off-fault stress-related interaction to distances of 10 km or more from the mainshock, including clustered seismicity to the northwest and southeast of the mainshock.

Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain fault diagnosis

NASA Astrophysics Data System (ADS)

Pan, Jun; Chen, Jinglong; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

2016-12-01

It is significant to perform condition monitoring and fault diagnosis on rolling mills in steel-making plant to ensure economic benefit. However, timely fault identification of key parts in a complicated industrial system under operating condition is still a challenging task since acquired condition signals are usually multi-modulated and inevitably mixed with strong noise. Therefore, a new data-driven mono-component identification method is proposed in this paper for diagnostic purpose. First, the modified nonlocal means algorithm (NLmeans) is proposed to reduce noise in vibration signals without destroying its original Fourier spectrum structure. During the modified NLmeans, two modifications are investigated and performed to improve denoising effect. Then, the modified empirical wavelet transform (MEWT) is applied on the de-noised signal to adaptively extract empirical mono-component modes. Finally, the modes are analyzed for mechanical fault identification based on Hilbert transform. The results show that the proposed data-driven method owns superior performance during system operation compared with the MEWT method.

A new time-frequency method for identification and classification of ball bearing faults

NASA Astrophysics Data System (ADS)

Attoui, Issam; Fergani, Nadir; Boutasseta, Nadir; Oudjani, Brahim; Deliou, Adel

2017-06-01

In order to fault diagnosis of ball bearing that is one of the most critical components of rotating machinery, this paper presents a time-frequency procedure incorporating a new feature extraction step that combines the classical wavelet packet decomposition energy distribution technique and a new feature extraction technique based on the selection of the most impulsive frequency bands. In the proposed procedure, firstly, as a pre-processing step, the most impulsive frequency bands are selected at different bearing conditions using a combination between Fast-Fourier-Transform FFT and Short-Frequency Energy SFE algorithms. Secondly, once the most impulsive frequency bands are selected, the measured machinery vibration signals are decomposed into different frequency sub-bands by using discrete Wavelet Packet Decomposition WPD technique to maximize the detection of their frequency contents and subsequently the most useful sub-bands are represented in the time-frequency domain by using Short Time Fourier transform STFT algorithm for knowing exactly what the frequency components presented in those frequency sub-bands are. Once the proposed feature vector is obtained, three feature dimensionality reduction techniques are employed using Linear Discriminant Analysis LDA, a feedback wrapper method and Locality Sensitive Discriminant Analysis LSDA. Lastly, the Adaptive Neuro-Fuzzy Inference System ANFIS algorithm is used for instantaneous identification and classification of bearing faults. In order to evaluate the performances of the proposed method, different testing data set to the trained ANFIS model by using different conditions of healthy and faulty bearings under various load levels, fault severities and rotating speed. The conclusion resulting from this paper is highlighted by experimental results which prove that the proposed method can serve as an intelligent bearing fault diagnosis system.

Fault Management Architectures and the Challenges of Providing Software Assurance

NASA Technical Reports Server (NTRS)

Savarino, Shirley; Fitz, Rhonda; Fesq, Lorraine; Whitman, Gerek

2015-01-01

The satellite systems Fault Management (FM) is focused on safety, the preservation of assets, and maintaining the desired functionality of the system. How FM is implemented varies among missions. Common to most is system complexity due to a need to establish a multi-dimensional structure across hardware, software and operations. This structure is necessary to identify and respond to system faults, mitigate technical risks and ensure operational continuity. These architecture, implementation and software assurance efforts increase with mission complexity. Because FM is a systems engineering discipline with a distributed implementation, providing efficient and effective verification and validation (VV) is challenging. A breakout session at the 2012 NASA Independent Verification Validation (IVV) Annual Workshop titled VV of Fault Management: Challenges and Successes exposed these issues in terms of VV for a representative set of architectures. NASA's IVV is funded by NASA's Software Assurance Research Program (SARP) in partnership with NASA's Jet Propulsion Laboratory (JPL) to extend the work performed at the Workshop session. NASA IVV will extract FM architectures across the IVV portfolio and evaluate the data set for robustness, assess visibility for validation and test, and define software assurance methods that could be applied to the various architectures and designs. This work focuses efforts on FM architectures from critical and complex projects within NASA. The identification of particular FM architectures, visibility, and associated VVIVV techniques provides a data set that can enable higher assurance that a satellite system will adequately detect and respond to adverse conditions. Ultimately, results from this activity will be incorporated into the NASA Fault Management Handbook providing dissemination across NASA, other agencies and the satellite community. This paper discusses the approach taken to perform the evaluations and preliminary findings from the research including identification of FM architectures, visibility observations, and methods utilized for VVIVV.

Interrogation of electrical connector faults using miniaturized UWB sources

NASA Astrophysics Data System (ADS)

Tokgöz, Çaǧata; Dardona, Sameh

2017-01-01

A diagnostic method for the detection, identification, and characterization of precursors of faults due to partial insertion of pin-socket contacts within electrical connectors commonly used in avionics systems is presented. It is demonstrated that a miniaturized ultrawideband (UWB) source and a minispectrum analyzer can be employed to measure resonant frequency shifts in connector S parameters as a small and low-cost alternative to a large and expensive network analyzer. The transfer function of an electrical connector is represented as a ratio of the spectra measured using the spectrum analyzer with and without the connector. Alternatively, the transfer function is derived in terms of the connector S parameters and the reflection coefficients at both ports of the connector. The transfer function data obtained using this derivation agreed well with its representation as a measured spectral ratio. The derivation enabled the extraction of the connector S parameters from the measured transfer function data as a function of the insertion depth of a pin-socket contact within the connector. In comparison with the S parameters measured directly using a network analyzer at multiple insertion depths, the S parameters extracted from the measured transfer function showed consistent and reliable representation of the electrical connector fault. The results demonstrate the potential of integrating a low-cost miniaturized UWB device into a connector harness for real-time detection of precursors to partially inserted connector faults.

Study of observed microearthquakes at Masada Deep Borehole

NASA Astrophysics Data System (ADS)

Hofstetter, A.; Malin, P. E.

2017-12-01

Seismological measurements, conducted at great depths of several hundred of meters or even a few km, can provide useful information that one cannot get while conducting the measurements on the surface. We take advantage of Masada Deep borehole (MDBI), an abandoned oil well, for the installation of a seismometer at a large depth of 1,256 m (1,516 bsl). The station is located in the near vicinity of the East Masada fault, part of the Western Boundary Fault of the Dead Sea basin. We present seismic observations of microearthquakes which occurred along the Dead Sea fault (DSF). Many of them were not recorded by the Israel Seismic Network (ISN). The quiet site of the station has an obvious advantage in detection and identification of earthquakes and explosions. For example, the station detects about 30% more quarry explosions as compared to observations of the ISN. We demonstrate that borehole seismograms are clearer than the on-surface observations of nearby seismometer. We lowered the magnitude scale of observed events down to about M≈-3. Many of the earthquakes, sometimes clusters, occurred underneath the MDBI at depths of 10-25 km, having special signature. Using the cross-correlation technique we present several series of seismic activity either underneath the station or along the DSF. Frequency-magnitude relationship, known also as Gutenberg-Richter relationship, is somewhat higher than the determined value for the whole Dead Sea Fault.

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.

Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang

2016-09-01

For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.

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.

New Airborne LiDAR Survey of the Hayward Fault, Northern California

NASA Astrophysics Data System (ADS)

Brocher, T. M.; Prentice, C. S.; Phillips, D. A.; Bevis, M.; Shrestha, R. L.

2007-12-01

We present a digital elevation model (DEM) constructed from newly acquired high-resolution LIght Detection and Ranging (LIDAR) data along the Hayward Fault in Northern California. The data were acquired by the National Center for Airborne Laser Mapping (NCALM) in the spring of 2007 in conjunction with a larger regional airborne LIDAR survey of the major crustal faults in northern California coordinated by UNAVCO and funded by the National Science Foundation as part of GeoEarthScope. A consortium composed of the U. S. Geological Survey, Pacific Gas & Electric Company, the San Francisco Public Utilities Commission, and the City of Berkeley separately funded the LIDAR acquisition along the Hayward Fault. Airborne LIDAR data were collected within a 106-km long by 1-km wide swath encompassing the Hayward Fault that extended from San Pablo Bay on the north to the southern end of its restraining stepover with the Calaveras Fault on the south. The Hayward Fault is among the most urbanized faults in the nation. With its most recent major rupture in 1868, it is well within the time window for its next large earthquake, making it an excellent candidate for a "before the earthquake" DEM image. After the next large Hayward Fault event, this DEM can be compared to a post-earthquake LIDAR DEM to provide a means for a detailed analysis of fault slip. In order to minimize location errors, temporary GPS ground control stations were deployed by Ohio State University, UNAVCO, and student volunteers from local universities to augment the available continuous GPS arrays operated in the study area by the Bay Area Regional Deformation (BARD) Network and the Plate Boundary Observatory (PBO). The vegetation cover varies along the fault zone: most of the vegetation is non-native species. Photographs from the 1860s show very little tall vegetation along the fault zone. A number of interesting geomorphic features are associated with the Hayward Fault, even in urbanized areas. Sag ponds and push up ridges can easily be followed along the fault zone, as well as more subtle features. Landslides along the western flanks of the East Bay Hills were also imaged. We expect that these new LIDAR images will allow us to detect subtle geomorphic features associated with active faulting that may reveal previously undetected active strands or better delineate active strands in areas of pervasive landsliding (as well as better mapping of the landslides themselves). We also anticipate that they will aid in land use planning and identification of new paleoseismic sites. The LIDAR data are freely available at www.earthscope.org.

Assessment Study of the State of the Art in Adaptive Control and its Applications to Aircraft Control

NASA Technical Reports Server (NTRS)

Kaufman, Howard

1998-01-01

Many papers relevant to reconfigurable flight control have appeared over the past fifteen years. In general these have consisted of theoretical issues, simulation experiments, and in some cases, actual flight tests. Results indicate that reconfiguration of flight controls is certainly feasible for a wide class of failures. However many of the proposed procedures although quite attractive, need further analytical and experimental studies for meaningful validation. Many procedures assume the availability of failure detection and identification logic that will supply adequately fast, the dynamics corresponding to the failed aircraft. This in general implies that the failure detection and fault identification logic must have access to all possible anticipated faults and the corresponding dynamical equations of motion. Unless some sort of explicit on line parameter identification is included, the computational demands could possibly be too excessive. This suggests the need for some form of adaptive control, either by itself as the prime procedure for control reconfiguration or in conjunction with the failure detection logic. If explicit or indirect adaptive control is used, then it is important that the identified models be such that the corresponding computed controls deliver adequate performance to the actual aircraft. Unknown changes in trim should be modelled, and parameter identification needs to be adequately insensitive to noise and at the same time capable of tracking abrupt changes. If however, both failure detection and system parameter identification turn out to be too time consuming in an emergency situation, then the concepts of direct adaptive control should be considered. If direct model reference adaptive control is to be used (on a linear model) with stability assurances, then a positive real or passivity condition needs to be satisfied for all possible configurations. This condition is often satisfied with a feedforward compensator around the plant. This compensator must be robustly designed such that the compensated plant satisfies the required positive real conditions over all expected parameter values. Furthermore, with the feedforward only around the plant, a nonzero (but bounded error) will exist in steady state between the plant and model outputs. This error can be removed by placing the compensator also in the reference model. Design of such a compensator should not be too difficult a problem since for flight control it is generally possible to feedback all the system states.

GIS-based identification of active lineaments within the Krasnokamensk Area, Transbaikalia, Russia

NASA Astrophysics Data System (ADS)

Petrov, V. A.; Lespinasse, M.; Ustinov, S. A.; Cialec, C.

2017-07-01

Lineament analysis was carried out using detailed digital elevation models (DEM) of the Krasnokamensk Area, southeastern Transbaikalia (Russia). The results of this research confirm the presence of already known faults, but also identify unknown fault zones. The primary focus was identifying small discontinuities and their relationship with extended fault zones. The developed technique allowed construction and identification of the active lineaments with their orientation of the compression and expansion axes in the horizontal plane, their direction of shear movement (right or left), and their geodynamic setting of formation (compression or stretching). The results of active faults identification and definition of their kinematics on digital elevation models were confirmed by measuring the velocities and directions of modern horizontal surface motions using a geodesic GPS, as well as identifying the principal stress axes directions of the modern stress field using modern-day earthquake data. The obtained results are deemed necessary for proper rational environmental management decisions.

Model-based fault detection and isolation for intermittently active faults with application to motion-based thruster fault detection and isolation for spacecraft

NASA Technical Reports Server (NTRS)

Wilson, Edward (Inventor)

2008-01-01

The present invention is a method for detecting and isolating fault modes in a system having a model describing its behavior and regularly sampled measurements. The models are used to calculate past and present deviations from measurements that would result with no faults present, as well as with one or more potential fault modes present. Algorithms that calculate and store these deviations, along with memory of when said faults, if present, would have an effect on the said actual measurements, are used to detect when a fault is present. Related algorithms are used to exonerate false fault modes and finally to isolate the true fault mode. This invention is presented with application to detection and isolation of thruster faults for a thruster-controlled spacecraft. As a supporting aspect of the invention, a novel, effective, and efficient filtering method for estimating the derivative of a noisy signal is presented.

Smart intimation and location of faults in distribution system

NASA Astrophysics Data System (ADS)

Hari Krishna, K.; Srinivasa Rao, B.

2018-04-01

Location of faults in the distribution system is one of the most complicated problems that we are facing today. Identification of fault location and severity of fault within a short time is required to provide continuous power supply but fault identification and information transfer to the operator is the biggest challenge in the distribution network. This paper proposes a fault location method in the distribution system based on Arduino nano and GSM module with flame sensor. The main idea is to locate the fault in the distribution transformer by sensing the arc coming out from the fuse element. The biggest challenge in the distribution network is to identify the location and the severity of faults under different conditions. Well operated transmission and distribution systems will play a key role for uninterrupted power supply. Whenever fault occurs in the distribution system the time taken to locate and eliminate the fault has to be reduced. The proposed design was achieved with flame sensor and GSM module. Under faulty condition, the system will automatically send an alert message to the operator in the distribution system, about the abnormal conditions near the transformer, site code and its exact location for possible power restoration.

A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery.

PubMed

Xue, Xiaoming; Zhou, Jianzhong

2017-01-01

To make further improvement in the diagnosis accuracy and efficiency, a mixed-domain state features data based hybrid fault diagnosis approach, which systematically blends both the statistical analysis approach and the artificial intelligence technology, is proposed in this work for rolling element bearings. For simplifying the fault diagnosis problems, the execution of the proposed method is divided into three steps, i.e., fault preliminary detection, fault type recognition and fault degree identification. In the first step, a preliminary judgment about the health status of the equipment can be evaluated by the statistical analysis method based on the permutation entropy theory. If fault exists, the following two processes based on the artificial intelligence approach are performed to further recognize the fault type and then identify the fault degree. For the two subsequent steps, mixed-domain state features containing time-domain, frequency-domain and multi-scale features are extracted to represent the fault peculiarity under different working conditions. As a powerful time-frequency analysis method, the fast EEMD method was employed to obtain multi-scale features. Furthermore, due to the information redundancy and the submergence of original feature space, a novel manifold learning method (modified LGPCA) is introduced to realize the low-dimensional representations for high-dimensional feature space. Finally, two cases with 12 working conditions respectively have been employed to evaluate the performance of the proposed method, where vibration signals were measured from an experimental bench of rolling element bearing. The analysis results showed the effectiveness and the superiority of the proposed method of which the diagnosis thought is more suitable for practical application. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

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.

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.

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.

FDI based on Artificial Neural Network for Low-Voltage-Ride-Through in DFIG-based Wind Turbine.

PubMed

Adouni, Amel; Chariag, Dhia; Diallo, Demba; Ben Hamed, Mouna; Sbita, Lassaâd

2016-09-01

As per modern electrical grid rules, Wind Turbine needs to operate continually even in presence severe grid faults as Low Voltage Ride Through (LVRT). Hence, a new LVRT Fault Detection and Identification (FDI) procedure has been developed to take the appropriate decision in order to develop the convenient control strategy. To obtain much better decision and enhanced FDI during grid fault, the proposed procedure is based on voltage indicators analysis using a new Artificial Neural Network architecture (ANN). In fact, two features are extracted (the amplitude and the angle phase). It is divided into two steps. The first is fault indicators generation and the second is indicators analysis for fault diagnosis. The first step is composed of six ANNs which are dedicated to describe the three phases of the grid (three amplitudes and three angle phases). Regarding to the second step, it is composed of a single ANN which analysis the indicators and generates a decision signal that describes the function mode (healthy or faulty). On other hand, the decision signal identifies the fault type. It allows distinguishing between the four faulty types. The diagnosis procedure is tested in simulation and experimental prototype. The obtained results confirm and approve its efficiency, rapidity, robustness and immunity to the noise and unknown inputs. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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 Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

PubMed

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

Apparatus for and method of testing an electrical ground fault circuit interrupt device

DOEpatents

Andrews, L.B.

1998-08-18

An apparatus for testing a ground fault circuit interrupt device includes a processor, an input device connected to the processor for receiving input from an operator, a storage media connected to the processor for storing test data, an output device connected to the processor for outputting information corresponding to the test data to the operator, and a calibrated variable load circuit connected between the processor and the ground fault circuit interrupt device. The ground fault circuit interrupt device is configured to trip a corresponding circuit breaker. The processor is configured to receive signals from the calibrated variable load circuit and to process the signals to determine a trip threshold current and/or a trip time. A method of testing the ground fault circuit interrupt device includes a first step of providing an identification for the ground fault circuit interrupt device. Test data is then recorded in accordance with the identification. By comparing test data from an initial test with test data from a subsequent test, a trend of performance for the ground fault circuit interrupt device is determined. 17 figs.

Apparatus for and method of testing an electrical ground fault circuit interrupt device

DOEpatents

Andrews, Lowell B.

1998-01-01

An apparatus for testing a ground fault circuit interrupt device includes a processor, an input device connected to the processor for receiving input from an operator, a storage media connected to the processor for storing test data, an output device connected to the processor for outputting information corresponding to the test data to the operator, and a calibrated variable load circuit connected between the processor and the ground fault circuit interrupt device. The ground fault circuit interrupt device is configured to trip a corresponding circuit breaker. The processor is configured to receive signals from the calibrated variable load circuit and to process the signals to determine a trip threshold current and/or a trip time. A method of testing the ground fault circuit interrupt device includes a first step of providing an identification for the ground fault circuit interrupt device. Test data is then recorded in accordance with the identification. By comparing test data from an initial test with test data from a subsequent test, a trend of performance for the ground fault circuit interrupt device is determined.

Identification and interpretation of tectonic features from ERTS-1 imagery

NASA Technical Reports Server (NTRS)

Abdel-Gawad, M. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Many criteria of active or recent fault movements are observable in the imagery. It was observed that where earthquake epicenter clusters occur, evidence of recent fault movements is generally observed. The opposite was not necessarily true as there are areas where evidence of recent faulting is observed, often along major known faults which are peculiarly devoid of significant seismicity. A tentative conclusion is that the seismicity pattern alone can often be a misleading criteria for potential earthquake hazards. The feasibility of recognizing geomorphic criteria of recent fault movement from ERTS-1 imagery suggests that ERTS imagery should be used to map potentially active faults and utilize this data to develop better criteria for the identification of areas prone to future earthquakes. An apparent correlation was observed between the distribution of mercury deposits in the California Coast Range Province and transverse fault zones trending west-northwest oblique to the trend of the San Andreas system. The significance of this correlation and the full extent of its implication on mercury exploration is under study.

Study on vibration characteristics and fault diagnosis method of oil-immersed flat wave reactor in Arctic area converter station

NASA Astrophysics Data System (ADS)

Lai, Wenqing; Wang, Yuandong; Li, Wenpeng; Sun, Guang; Qu, Guomin; Cui, Shigang; Li, Mengke; Wang, Yongqiang

2017-10-01

Based on long term vibration monitoring of the No.2 oil-immersed fat wave reactor in the ±500kV converter station in East Mongolia, the vibration signals in normal state and in core loose fault state were saved. Through the time-frequency analysis of the signals, the vibration characteristics of the core loose fault were obtained, and a fault diagnosis method based on the dual tree complex wavelet (DT-CWT) and support vector machine (SVM) was proposed. The vibration signals were analyzed by DT-CWT, and the energy entropy of the vibration signals were taken as the feature vector; the support vector machine was used to train and test the feature vector, and the accurate identification of the core loose fault of the flat wave reactor was realized. Through the identification of many groups of normal and core loose fault state vibration signals, the diagnostic accuracy of the result reached 97.36%. The effectiveness and accuracy of the method in the fault diagnosis of the flat wave reactor core is verified.

Usability Studies in Virtual and Traditional Computer Aided Design Environments for Fault Identification

DTIC Science & Technology

2017-08-08

Usability Studies In Virtual And Traditional Computer Aided Design Environments For Fault Identification Dr. Syed Adeel Ahmed, Xavier University...virtual environment with wand interfaces compared directly with a workstation non-stereoscopic traditional CAD interface with keyboard and mouse. In...the differences in interaction when compared with traditional human computer interfaces. This paper provides analysis via usability study methods

Fault Identification Based on Nlpca in Complex Electrical Engineering

NASA Astrophysics Data System (ADS)

Zhang, Yagang; Wang, Zengping; Zhang, Jinfang

2012-07-01

The fault is inevitable in any complex systems engineering. Electric power system is essentially a typically nonlinear system. It is also one of the most complex artificial systems in this world. In our researches, based on the real-time measurements of phasor measurement unit, under the influence of white Gaussian noise (suppose the standard deviation is 0.01, and the mean error is 0), we used mainly nonlinear principal component analysis theory (NLPCA) to resolve fault identification problem in complex electrical engineering. The simulation results show that the fault in complex electrical engineering is usually corresponding to the variable with the maximum absolute value coefficient in the first principal component. These researches will have significant theoretical value and engineering practical significance.

Fault Identification by Unsupervised Learning Algorithm

NASA Astrophysics Data System (ADS)

Nandan, S.; Mannu, U.

2012-12-01

Contemporary fault identification techniques predominantly rely on the surface expression of the fault. This biased observation is inadequate to yield detailed fault structures in areas with surface cover like cities deserts vegetation etc and the changes in fault patterns with depth. Furthermore it is difficult to estimate faults structure which do not generate any surface rupture. Many disastrous events have been attributed to these blind faults. Faults and earthquakes are very closely related as earthquakes occur on faults and faults grow by accumulation of coseismic rupture. For a better seismic risk evaluation it is imperative to recognize and map these faults. We implement a novel approach to identify seismically active fault planes from three dimensional hypocenter distribution by making use of unsupervised learning algorithms. We employ K-means clustering algorithm and Expectation Maximization (EM) algorithm modified to identify planar structures in spatial distribution of hypocenter after filtering out isolated events. We examine difference in the faults reconstructed by deterministic assignment in K- means and probabilistic assignment in EM algorithm. The method is conceptually identical to methodologies developed by Ouillion et al (2008, 2010) and has been extensively tested on synthetic data. We determined the sensitivity of the methodology to uncertainties in hypocenter location, density of clustering and cross cutting fault structures. The method has been applied to datasets from two contrasting regions. While Kumaon Himalaya is a convergent plate boundary, Koyna-Warna lies in middle of the Indian Plate but has a history of triggered seismicity. The reconstructed faults were validated by examining the fault orientation of mapped faults and the focal mechanism of these events determined through waveform inversion. The reconstructed faults could be used to solve the fault plane ambiguity in focal mechanism determination and constrain the fault orientations for finite source inversions. The faults produced by the method exhibited good correlation with the fault planes obtained by focal mechanism solutions and previously mapped faults.

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.

Integrated analysis of differential interferometric synthetic aperture radar (DInSAR) and geological data for measuring deformation movement of Kaligarang fault, Semarang-Indonesia

NASA Astrophysics Data System (ADS)

Prasetyo, Y.; Fakhrudin, Warasambi, S. M.

2016-05-01

Semarang is one of the densely populated city in Central Java which is has Kaligarang's fault. It is lie in Kaligarang River and across several dense urban settlement. The position of Kaligarang's river itself divides in the direction nearly north-south city of Semarang. The impact of the fault can be seen in severals indication such as a land subsidence phenomenon in Tinjomoyo village area which is make impact to house and road destruction. In this research, we have used combination methods between InSAR, DinSAR and geomorphology (geology data) where is this techniques used to identity the fault area and estimate Kaligarang's fault movement velocity. In fault movement velocity observation, we only compute the movement in vertical with neglect horizontal movement. The data used in this study of one pair ALOS PALSAR level 1.0 which was acquired on June 8, 2007and 10 of September 2009. Besides that third ALOS PALSAR earlier, also used data of SRTM DEM 4th version, is used for the correction of the topography. The use of the three methods already mentioned earlier have different functions. For the lnSAR method used for the establishment of a digital model in Semarang. After getting high models digital city of Semarang, the identification process can be done layout, length, width and area of the Kaligarang fault using geomorphology. Results of such identification can be calculated using the rate of deformation and fault movement. From the result generated DinSAR method of land subsidence rate between 3 em to II em. To know the truth measurement that used DinSAR method, is performed with the decline of validation that measured using GPS. After validating obtained standard deviation of 3,073 em. To estimate the Kaligarang's fault pattern and direction is using the geomorphology method. The results that Kaligarang's is an active fault that has fault strike slip as fault pattern. It makes this research is useful because could be used as an inquick assessment in fault identification and deformation movement observation.

Validity of active fault identification through magnetic anomalous using earthquake mechanism, microgravity and topography structure analysis in Cisolok area

NASA Astrophysics Data System (ADS)

Setyonegoro, Wiko; Kurniawan, Telly; Ahadi, Suaidi; Rohadi, Supriyanto; Hardy, Thomas; Prayogo, Angga S.

2017-07-01

Research was conducted to determine the value of the magnetic anomalies to identify anomalous value standard fault, down or up with the type of Meratus trending northeast-southwest Cisolok, Sukabumi. Data collection was performed by setting the measurement grid at intervals of 5 meters distance measurement using a Precision Proton Magnetometer (PPM) -GSM-19T. To identification the active fault using magnetic is needed another parameter. The purpose of this study is to identification active fault using magnetic Anomaly in related with subsurface structure through the validation analysis of earthquake mechanism, microgravity and with Topography Structure in Java Island. Qualitative interpretation is done by analyzing the residual anomaly that has been reduced to the pole while the quantitative interpretation is done by analyzing the pattern of residual anomalies through computation. The results of quantitative interpretation, an anomalous value reduction to the pole magnetic field is at -700 nT to 700 nT while the results of the qualitative interpretation of the modeling of the path AA', BB' and CC' shows the magnetic anomaly at coordinates liquefaction resources with a value of 1028.04, 1416.21, - 1565, -1686.91. The measurement results obtained in Cisolok magnetic anomalies that indicate a high content of alumina (Al) and iron (Fe) which be identified appears through the fault gap towards the northeast through Rajamandala Lembang Fault related to the mechanism in the form of a normal fault with slip rate of 2 mm / year.

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.

Identifiability of Additive, Time-Varying Actuator and Sensor Faults by State Augmentation

NASA Technical Reports Server (NTRS)

Upchurch, Jason M.; Gonzalez, Oscar R.; Joshi, Suresh M.

2014-01-01

Recent work has provided a set of necessary and sucient conditions for identifiability of additive step faults (e.g., lock-in-place actuator faults, constant bias in the sensors) using state augmentation. This paper extends these results to an important class of faults which may affect linear, time-invariant systems. In particular, the faults under consideration are those which vary with time and affect the system dynamics additively. Such faults may manifest themselves in aircraft as, for example, control surface oscillations, control surface runaway, and sensor drift. The set of necessary and sucient conditions presented in this paper are general, and apply when a class of time-varying faults affects arbitrary combinations of actuators and sensors. The results in the main theorems are illustrated by two case studies, which provide some insight into how the conditions may be used to check the theoretical identifiability of fault configurations of interest for a given system. It is shown that while state augmentation can be used to identify certain fault configurations, other fault configurations are theoretically impossible to identify using state augmentation, giving practitioners valuable insight into such situations. That is, the limitations of state augmentation for a given system and configuration of faults are made explicit. Another limitation of model-based methods is that there can be large numbers of fault configurations, thus making identification of all possible configurations impractical. However, the theoretical identifiability of known, credible fault configurations can be tested using the theorems presented in this paper, which can then assist the efforts of fault identification practitioners.

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

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.

Improvement of kurtosis-guided-grams via Gini index for bearing fault feature identification

NASA Astrophysics Data System (ADS)

Miao, Yonghao; Zhao, Ming; Lin, Jing

2017-12-01

A group of kurtosis-guided-grams, such as Kurtogram, Protrugram and SKRgram, is designed to detect the resonance band excited by faults based on the sparsity index. However, a common issue associated with these methods is that they tend to choose the frequency band with individual impulses rather than the desired fault impulses. This may be attributed to the selection of the sparsity index, kurtosis, which is vulnerable to impulsive noise. In this paper, to solve the problem, a sparsity index, called the Gini index, is introduced as an alternative estimator for the selection of the resonance band. It has been found that the sparsity index is still able to provide guidelines for the selection of the fault band without prior information of the fault period. More importantly, the Gini index has unique performance in random-impulse resistance, which renders the improved methods using the index free from the random impulse caused by external knocks on the bearing housing, or electromagnetic interference. By virtue of these advantages, the improved methods using the Gini index not only overcome the shortcomings but are more effective under harsh working conditions, even in the complex structure. Finally, the comparison between the kurtosis-guided-grams and the improved methods using the Gini index is made using the simulated and experimental data. The results verify the effectiveness of the improvement by both the fixed-axis bearing and planetary bearing fault signals.

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.

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.

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.

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.

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.

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.

Real-time spatio-temporal coherence estimation for autonomous mode identification and invariance tracking

NASA Technical Reports Server (NTRS)

Park, Han G. (Inventor); Zak, Michail (Inventor); James, Mark L. (Inventor); Mackey, Ryan M. E. (Inventor)

2003-01-01

A general method of anomaly detection from time-correlated sensor data is disclosed. Multiple time-correlated signals are received. Their cross-signal behavior is compared against a fixed library of invariants. The library is constructed during a training process, which is itself data-driven using the same time-correlated signals. The method is applicable to a broad class of problems and is designed to respond to any departure from normal operation, including faults or events that lie outside the training envelope.

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.

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

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.

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

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.

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.

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.

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.

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.

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

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

NASA Technical Reports Server (NTRS)

Sowers, T. Shane; Kopasakis, George; Simon, Donald L.

2008-01-01

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance, there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight, and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics

NASA Technical Reports Server (NTRS)

Sowers, T. Shane; Kopasakis, George; Simon, Donald L.

2008-01-01

The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.

An efficient recursive least square-based condition monitoring approach for a rail vehicle suspension system

NASA Astrophysics Data System (ADS)

Liu, X. Y.; Alfi, S.; Bruni, S.

2016-06-01

A model-based condition monitoring strategy for the railway vehicle suspension is proposed in this paper. This approach is based on recursive least square (RLS) algorithm focusing on the deterministic 'input-output' model. RLS has Kalman filtering feature and is able to identify the unknown parameters from a noisy dynamic system by memorising the correlation properties of variables. The identification of suspension parameter is achieved by machine learning of the relationship between excitation and response in a vehicle dynamic system. A fault detection method for the vertical primary suspension is illustrated as an instance of this condition monitoring scheme. Simulation results from the rail vehicle dynamics software 'ADTreS' are utilised as 'virtual measurements' considering a trailer car of Italian ETR500 high-speed train. The field test data from an E464 locomotive are also employed to validate the feasibility of this strategy for the real application. Results of the parameter identification performed indicate that estimated suspension parameters are consistent or approximate with the reference values. These results provide the supporting evidence that this fault diagnosis technique is capable of paving the way for the future vehicle condition monitoring system.

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.

Driver landmark and traffic sign identification in early Alzheimer's disease.

PubMed

Uc, E Y; Rizzo, M; Anderson, S W; Shi, Q; Dawson, J D

2005-06-01

To assess visual search and recognition of roadside targets and safety errors during a landmark and traffic sign identification task in drivers with Alzheimer's disease. 33 drivers with probable Alzheimer's disease of mild severity and 137 neurologically normal older adults underwent a battery of visual and cognitive tests and were asked to report detection of specific landmarks and traffic signs along a segment of an experimental drive. The drivers with mild Alzheimer's disease identified significantly fewer landmarks and traffic signs and made more at-fault safety errors during the task than control subjects. Roadside target identification performance and safety errors were predicted by scores on standardised tests of visual and cognitive function. Drivers with Alzheimer's disease are impaired in a task of visual search and recognition of roadside targets; the demands of these targets on visual perception, attention, executive functions, and memory probably increase the cognitive load, worsening driving safety.

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.

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

Characterization of Model-Based Reasoning Strategies for Use in IVHM Architectures

NASA Technical Reports Server (NTRS)

Poll, Scott; Iverson, David; Patterson-Hine, Ann

2003-01-01

Open architectures are gaining popularity for Integrated Vehicle Health Management (IVHM) applications due to the diversity of subsystem health monitoring strategies in use and the need to integrate a variety of techniques at the system health management level. The basic concept of an open architecture suggests that whatever monitoring or reasoning strategy a subsystem wishes to deploy, the system architecture will support the needs of that subsystem and will be capable of transmitting subsystem health status across subsystem boundaries and up to the system level for system-wide fault identification and diagnosis. There is a need to understand the capabilities of various reasoning engines and how they, coupled with intelligent monitoring techniques, can support fault detection and system level fault management. Researchers in IVHM at NASA Ames Research Center are supporting the development of an IVHM system for liquefying-fuel hybrid rockets. In the initial stage of this project, a few readily available reasoning engines were studied to assess candidate technologies for application in next generation launch systems. Three tools representing the spectrum of model-based reasoning approaches, from a quantitative simulation based approach to a graph-based fault propagation technique, were applied to model the behavior of the Hybrid Combustion Facility testbed at Ames. This paper summarizes the characterization of the modeling process for each of the techniques.

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.

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.

An automatic modular procedure to generate high-resolution earthquake catalogues: application to the Alto Tiberina Near Fault Observatory (TABOO), Italy.

NASA Astrophysics Data System (ADS)

Di Stefano, R.; Chiaraluce, L.; Valoroso, L.; Waldhauser, F.; Latorre, D.; Piccinini, D.; Tinti, E.

2014-12-01

The Alto Tiberina Near Fault Observatory (TABOO) in the upper Tiber Valley (northern Appennines) is a INGV research infrastructure devoted to the study of preparatory processes and deformation characteristics of the Alto Tiberina Fault (ATF), a 60 km long, low-angle normal fault active since the Quaternary. The TABOO seismic network, covering an area of 120 × 120 km, consists of 60 permanent surface and 250 m deep borehole stations equipped with 3-components, 0.5s to 120s velocimeters, and strong motion sensors. Continuous seismic recordings are transmitted in real-time to the INGV, where we set up an automatic procedure that produces high-resolution earthquakes catalogues (location, magnitudes, 1st motion polarities) in near-real-time. A sensitive event detection engine running on the continuous data stream is followed by advanced phase identification, arrival-time picking, and quality assessment algorithms (MPX). Pick weights are determined from a statistical analysis of a set of predictors designed to correctly apply an a-priori chosen weighting scheme. The MPX results are used to routinely update earthquakes catalogues based on a variety of (1D and 3D) velocity models and location techniques. We are also applying the DD-RT procedure which uses cross-correlation and double-difference methods in real-time to relocate events with high precision relative to a high-resolution background catalog. P- and S-onset and location information are used to automatically compute focal mechanisms, VP/VS variations in space and time, and periodically update 3D VP and VP/VS tomographic models. We present results from four years of operation, during which this monitoring system analyzed over 1.2 million detections and recovered ~60,000 earthquakes at a detection threshold of ML 0.5. The high-resolution information is being used to study changes in seismicity patterns and fault and rock properties along the ATF in space and time, and to elaborate ground shaking scenarios adopting diverse slip distributions and rupture directivity models.

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

Creating an automated chiller fault detection and diagnostics tool using a data fault library.

PubMed

Bailey, Margaret B; Kreider, Jan F

2003-07-01

Reliable, automated detection and diagnosis of abnormal behavior within vapor compression refrigeration cycle (VCRC) equipment is extremely desirable for equipment owners and operators. The specific type of VCRC equipment studied in this paper is a 70-ton helical rotary, air-cooled chiller. The fault detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects faults through recognizing trends or patterns existing within the data. The FDD method incorporates a neural network (NN) classifier to infer the current state given a vector of observables. Therefore the FDD method relies upon the availability of normal and fault empirical data for training purposes and therefore a fault library of empirical data is assembled. This paper presents procedures for conducting sophisticated fault experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related faults. The experimental processes described here are not well documented in literature and therefore will provide the interested reader with a useful guide. In addition, the authors provide evidence, based on both thermodynamics and empirical data analysis, that chiller performance is significantly degraded during fault operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section.

Automatic identification of fault zone head waves and direct P waves and its application in the Parkfield section of the San Andreas Fault, California

NASA Astrophysics Data System (ADS)

Li, Zefeng; Peng, Zhigang

2016-06-01

Fault zone head waves (FZHWs) are observed along major strike-slip faults and can provide high-resolution imaging of fault interface properties at seismogenic depth. In this paper, we present a new method to automatically detect FZHWs and pick direct P waves secondary arrivals (DWSAs). The algorithm identifies FZHWs by computing the amplitude ratios between the potential FZHWs and DSWAs. The polarities, polarizations and characteristic periods of FZHWs and DSWAs are then used to refine the picks or evaluate the pick quality. We apply the method to the Parkfield section of the San Andreas Fault where FZHWs have been identified before by manual picks. We compare results from automatically and manually picked arrivals and find general agreement between them. The obtained velocity contrast at Parkfield is generally 5-10 per cent near Middle Mountain while it decreases below 5 per cent near Gold Hill. We also find many FZHWs recorded by the stations within 1 km of the background seismicity (i.e. the Southwest Fracture Zone) that have not been reported before. These FZHWs could be generated within a relatively wide low velocity zone sandwiched between the fast Salinian block on the southwest side and the slow Franciscan Mélange on the northeast side. Station FROB on the southwest (fast) side also recorded a small portion of weak precursory signals before sharp P waves. However, the polarities of weak signals are consistent with the right-lateral strike-slip mechanisms, suggesting that they are unlikely genuine FZHW signals.

Arc burst pattern analysis fault detection system

NASA Technical Reports Server (NTRS)

Russell, B. Don (Inventor); Aucoin, B. Michael (Inventor); Benner, Carl L. (Inventor)

1997-01-01

A method and apparatus are provided for detecting an arcing fault on a power line carrying a load current. Parameters indicative of power flow and possible fault events on the line, such as voltage and load current, are monitored and analyzed for an arc burst pattern exhibited by arcing faults in a power system. These arcing faults are detected by identifying bursts of each half-cycle of the fundamental current. Bursts occurring at or near a voltage peak indicate arcing on that phase. Once a faulted phase line is identified, a comparison of the current and voltage reveals whether the fault is located in a downstream direction of power flow toward customers, or upstream toward a generation station. If the fault is located downstream, the line is de-energized, and if located upstream, the line may remain energized to prevent unnecessary power outages.

Identification of significant intrinsic mode functions for the diagnosis of induction motor fault.

PubMed

Cho, Sangjin; Shahriar, Md Rifat; Chong, Uipil

2014-08-01

For the analysis of non-stationary signals generated by a non-linear process like fault of an induction motor, empirical mode decomposition (EMD) is the best choice as it decomposes the signal into its natural oscillatory modes known as intrinsic mode functions (IMFs). However, some of these oscillatory modes obtained from a fault signal are not significant as they do not bear any fault signature and can cause misclassification of the fault instance. To solve this issue, a novel IMF selection algorithm is proposed in this work.

Multiple fault separation and detection by joint subspace learning for the health assessment of wind turbine gearboxes

NASA Astrophysics Data System (ADS)

Du, Zhaohui; Chen, Xuefeng; Zhang, Han; Zi, Yanyang; Yan, Ruqiang

2017-09-01

The gearbox of a wind turbine (WT) has dominant failure rates and highest downtime loss among all WT subsystems. Thus, gearbox health assessment for maintenance cost reduction is of paramount importance. The concurrence of multiple faults in gearbox components is a common phenomenon due to fault induction mechanism. This problem should be considered before planning to replace the components of the WT gearbox. Therefore, the key fault patterns should be reliably identified from noisy observation data for the development of an effective maintenance strategy. However, most of the existing studies focusing on multiple fault diagnosis always suffer from inappropriate division of fault information in order to satisfy various rigorous decomposition principles or statistical assumptions, such as the smooth envelope principle of ensemble empirical mode decomposition and the mutual independence assumption of independent component analysis. Thus, this paper presents a joint subspace learning-based multiple fault detection (JSL-MFD) technique to construct different subspaces adaptively for different fault patterns. Its main advantage is its capability to learn multiple fault subspaces directly from the observation signal itself. It can also sparsely concentrate the feature information into a few dominant subspace coefficients. Furthermore, it can eliminate noise by simply performing coefficient shrinkage operations. Consequently, multiple fault patterns are reliably identified by utilizing the maximum fault information criterion. The superiority of JSL-MFD in multiple fault separation and detection is comprehensively investigated and verified by the analysis of a data set of a 750 kW WT gearbox. Results show that JSL-MFD is superior to a state-of-the-art technique in detecting hidden fault patterns and enhancing detection accuracy.

Advanced power system protection and incipient fault detection and protection of spaceborne power systems

NASA Technical Reports Server (NTRS)

Russell, B. Don

1989-01-01

This research concentrated on the application of advanced signal processing, expert system, and digital technologies for the detection and control of low grade, incipient faults on spaceborne power systems. The researchers have considerable experience in the application of advanced digital technologies and the protection of terrestrial power systems. This experience was used in the current contracts to develop new approaches for protecting the electrical distribution system in spaceborne applications. The project was divided into three distinct areas: (1) investigate the applicability of fault detection algorithms developed for terrestrial power systems to the detection of faults in spaceborne systems; (2) investigate the digital hardware and architectures required to monitor and control spaceborne power systems with full capability to implement new detection and diagnostic algorithms; and (3) develop a real-time expert operating system for implementing diagnostic and protection algorithms. Significant progress has been made in each of the above areas. Several terrestrial fault detection algorithms were modified to better adapt to spaceborne power system environments. Several digital architectures were developed and evaluated in light of the fault detection algorithms.

Fault tolerant filtering and fault detection for quantum systems driven by fields in single photon states

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

Gao, Qing, E-mail: qing.gao.chance@gmail.com; Dong, Daoyi, E-mail: daoyidong@gmail.com; Petersen, Ian R., E-mail: i.r.petersen@gmai.com

The purpose of this paper is to solve the fault tolerant filtering and fault detection problem for a class of open quantum systems driven by a continuous-mode bosonic input field in single photon states when the systems are subject to stochastic faults. Optimal estimates of both the system observables and the fault process are simultaneously calculated and characterized by a set of coupled recursive quantum stochastic differential equations.

Application of a Subspace-Based Fault Detection Method to Industrial Structures

NASA Astrophysics Data System (ADS)

Mevel, L.; Hermans, L.; van der Auweraer, H.

1999-11-01

Early detection and localization of damage allow increased expectations of reliability, safety and reduction of the maintenance cost. This paper deals with the industrial validation of a technique to monitor the health of a structure in operating conditions (e.g. rotating machinery, civil constructions subject to ambient excitations, etc.) and to detect slight deviations in a modal model derived from in-operation measured data. In this paper, a statistical local approach based on covariance-driven stochastic subspace identification is proposed. The capabilities and limitations of the method with respect to health monitoring and damage detection are discussed and it is explained how the method can be practically used in industrial environments. After the successful validation of the proposed method on a few laboratory structures, its application to a sports car is discussed. The example illustrates that the method allows the early detection of a vibration-induced fatigue problem of a sports car.

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.

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.

Advanced information processing system: Authentication protocols for network communication

NASA Technical Reports Server (NTRS)

Harper, Richard E.; Adams, Stuart J.; Babikyan, Carol A.; Butler, Bryan P.; Clark, Anne L.; Lala, Jaynarayan H.

1994-01-01

In safety critical I/O and intercomputer communication networks, reliable message transmission is an important concern. Difficulties of communication and fault identification in networks arise primarily because the sender of a transmission cannot be identified with certainty, an intermediate node can corrupt a message without certainty of detection, and a babbling node cannot be identified and silenced without lengthy diagnosis and reconfiguration . Authentication protocols use digital signature techniques to verify the authenticity of messages with high probability. Such protocols appear to provide an efficient solution to many of these problems. The objective of this program is to develop, demonstrate, and evaluate intercomputer communication architectures which employ authentication. As a context for the evaluation, the authentication protocol-based communication concept was demonstrated under this program by hosting a real-time flight critical guidance, navigation and control algorithm on a distributed, heterogeneous, mixed redundancy system of workstations and embedded fault-tolerant computers.

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.

Abnormal fault-recovery characteristics of the fault-tolerant multiprocessor uncovered using a new fault-injection methodology

NASA Technical Reports Server (NTRS)

Padilla, Peter A.

1991-01-01

An investigation was made in AIRLAB of the fault handling performance of the Fault Tolerant MultiProcessor (FTMP). 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 in 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. Byzantine faults behave such that the faulted unit points to a working unit as the source of errors. The design's problems involve: (1) the design and interface between the simplex error detection hardware and the error processing software, (2) the functional capabilities of the FTMP system bus, and (3) the communication requirements of a multiprocessor architecture. These weak areas in the FTMP's design increase the probability that, for any hardware fault, a good line replacement unit (LRU) is mistakenly disabled by the fault management software.

Virtual Sensor for Failure Detection, Identification and Recovery in the Transition Phase of a Morphing Aircraft

PubMed Central

Heredia, Guillermo; Ollero, Aníbal

2010-01-01

The Helicopter Adaptive Aircraft (HADA) is a morphing aircraft which is able to take-off as a helicopter and, when in forward flight, unfold the wings that are hidden under the fuselage, and transfer the power from the main rotor to a propeller, thus morphing from a helicopter to an airplane. In this process, the reliable folding and unfolding of the wings is critical, since a failure may determine the ability to perform a mission, and may even be catastrophic. This paper proposes a virtual sensor based Fault Detection, Identification and Recovery (FDIR) system to increase the reliability of the HADA aircraft. The virtual sensor is able to capture the nonlinear interaction between the folding/unfolding wings aerodynamics and the HADA airframe using the navigation sensor measurements. The proposed FDIR system has been validated using a simulation model of the HADA aircraft, which includes real phenomena as sensor noise and sampling characteristics and turbulence and wind perturbations. PMID:22294922

Virtual sensor for failure detection, identification and recovery in the transition phase of a morphing aircraft.

PubMed

Heredia, Guillermo; Ollero, Aníbal

2010-01-01

The Helicopter Adaptive Aircraft (HADA) is a morphing aircraft which is able to take-off as a helicopter and, when in forward flight, unfold the wings that are hidden under the fuselage, and transfer the power from the main rotor to a propeller, thus morphing from a helicopter to an airplane. In this process, the reliable folding and unfolding of the wings is critical, since a failure may determine the ability to perform a mission, and may even be catastrophic. This paper proposes a virtual sensor based Fault Detection, Identification and Recovery (FDIR) system to increase the reliability of the HADA aircraft. The virtual sensor is able to capture the nonlinear interaction between the folding/unfolding wings aerodynamics and the HADA airframe using the navigation sensor measurements. The proposed FDIR system has been validated using a simulation model of the HADA aircraft, which includes real phenomena as sensor noise and sampling characteristics and turbulence and wind perturbations.

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.

System for detecting and limiting electrical ground faults within electrical devices

DOEpatents

Gaubatz, Donald C.

1990-01-01

An electrical ground fault detection and limitation system for employment with a nuclear reactor utilizing a liquid metal coolant. Elongate electromagnetic pumps submerged within the liquid metal coolant and electrical support equipment experiencing an insulation breakdown occasion the development of electrical ground fault current. Without some form of detection and control, these currents may build to damaging power levels to expose the pump drive components to liquid metal coolant such as sodium with resultant undesirable secondary effects. Such electrical ground fault currents are detected and controlled through the employment of an isolated power input to the pumps and with the use of a ground fault control conductor providing a direct return path from the affected components to the power source. By incorporating a resistance arrangement with the ground fault control conductor, the amount of fault current permitted to flow may be regulated to the extent that the reactor may remain in operation until maintenance may be performed, notwithstanding the existence of the fault. Monitors such as synchronous demodulators may be employed to identify and evaluate fault currents for each phase of a polyphase power, and control input to the submerged pump and associated support equipment.

Detection and diagnosis of bearing and cutting tool faults using hidden Markov models

NASA Astrophysics Data System (ADS)

Boutros, Tony; Liang, Ming

2011-08-01

Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.

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.

Robust Fault Detection for Switched Fuzzy Systems With Unknown Input.

PubMed

Han, Jian; Zhang, Huaguang; Wang, Yingchun; Sun, Xun

2017-10-03

This paper investigates the fault detection problem for a class of switched nonlinear systems in the T-S fuzzy framework. The unknown input is considered in the systems. A novel fault detection unknown input observer design method is proposed. Based on the proposed observer, the unknown input can be removed from the fault detection residual. The weighted H∞ performance level is considered to ensure the robustness. In addition, the weighted H₋ performance level is introduced, which can increase the sensibility of the proposed detection method. To verify the proposed scheme, a numerical simulation example and an electromechanical system simulation example are provided at the end of this paper.

Development of a Test Facility for Air Revitalization Technology Evaluation

NASA Technical Reports Server (NTRS)

Lu, Sao-Dung; Lin, Amy; Campbell, Melissa; Smith, Frederick

2006-01-01

An active fault tolerant control (FTC) law is generally sensitive to false identification since the control gain is reconfigured for fault occurrence. In the conventional FTC law design procedure, dynamic variations due to false identification are not considered. In this paper, an FTC synthesis method is developed in order to consider possible variations of closed-loop dynamics under false identification into the control design procedure. An active FTC synthesis problem is formulated into an LMI optimization problem to minimize the upper bound of the induced-L2 norm which can represent the worst-case performance degradation due to false identification. The developed synthesis method is applied for control of the longitudinal motions of FASER (Free-flying Airplane for Subscale Experimental Research). The designed FTC law of the airplane is simulated for pitch angle command tracking under a false identification case.

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

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.

A Vibration-Based Approach for Stator Winding Fault Diagnosis of Induction Motors: Application of Envelope Analysis

DTIC Science & Technology

2014-10-02

takes it either as auxiliary to magnetic flux, or is not able to detect the winding faults unless severity is already quite significant. This paper...different loads, speeds and severity levels. The experimental results show that the proposed method was able to detect inter-turn faults in the...maintenance strategy requires the technologies of: (a) on- line condition monitoring, (b) fault detection and diagnosis, and (c) prognostics. Figure 1

Multi-thresholds for fault isolation in the presence of uncertainties.

PubMed

Touati, Youcef; Mellal, Mohamed Arezki; Benazzouz, Djamel

2016-05-01

Monitoring of the faults is an important task in mechatronics. It involves the detection and isolation of faults which are performed by using the residuals. These residuals represent numerical values that define certain intervals called thresholds. In fact, the fault is detected if the residuals exceed the thresholds. In addition, each considered fault must activate a unique set of residuals to be isolated. However, in the presence of uncertainties, false decisions can occur due to the low sensitivity of certain residuals towards faults. In this paper, an efficient approach to make decision on fault isolation in the presence of uncertainties is proposed. Based on the bond graph tool, the approach is developed in order to generate systematically the relations between residuals and faults. The generated relations allow the estimation of the minimum detectable and isolable fault values. The latter is used to calculate the thresholds of isolation for each residual. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A review on data-driven fault severity assessment in rolling bearings

NASA Astrophysics Data System (ADS)

Cerrada, Mariela; Sánchez, René-Vinicio; Li, Chuan; Pacheco, Fannia; Cabrera, Diego; Valente de Oliveira, José; Vásquez, Rafael E.

2018-01-01

Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in industrial processes. In particular, bearings are mechanical components used in most rotating devices and they represent the main source of faults in such equipments; reason for which research activities on detecting and diagnosing their faults have increased. Fault detection aims at identifying whether the device is or not in a fault condition, and diagnosis is commonly oriented towards identifying the fault mode of the device, after detection. An important step after fault detection and diagnosis is the analysis of the magnitude or the degradation level of the fault, because this represents a support to the decision-making process in condition based-maintenance. However, no extensive works are devoted to analyse this problem, or some works tackle it from the fault diagnosis point of view. In a rough manner, fault severity is associated with the magnitude of the fault. In bearings, fault severity can be related to the physical size of fault or a general degradation of the component. Due to literature regarding the severity assessment of bearing damages is limited, this paper aims at discussing the recent methods and techniques used to achieve the fault severity evaluation in the main components of the rolling bearings, such as inner race, outer race, and ball. The review is mainly focused on data-driven approaches such as signal processing for extracting the proper fault signatures associated with the damage degradation, and learning approaches that are used to identify degradation patterns with regards to health conditions. Finally, new challenges are highlighted in order to develop new contributions in this field.

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

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.

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.

Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants

NASA Astrophysics Data System (ADS)

Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

2013-02-01

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants.

PubMed

Shao, Renping; Li, Jing; Hu, Wentao; Dong, Feifei

2013-02-01

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 r/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *.m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system.

Negative Selection Algorithm for Aircraft Fault Detection

NASA Technical Reports Server (NTRS)

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

2004-01-01

We investigated a real-valued Negative Selection Algorithm (NSA) for fault detection in man-in-the-loop aircraft operation. The detection algorithm uses body-axes angular rate sensory data exhibiting the normal flight behavior patterns, to generate probabilistically a set of fault detectors that can detect any abnormalities (including faults and damages) in the behavior pattern of the aircraft flight. We performed experiments with datasets (collected under normal and various simulated failure conditions) using the NASA Ames man-in-the-loop high-fidelity C-17 flight simulator. The paper provides results of experiments with different datasets representing various failure conditions.

Identification and interpretation of tectonic features from Skylab imagery. [California to Arizona

NASA Technical Reports Server (NTRS)

Abdel-Gawad, M. (Principal Investigator)

1974-01-01

The author has identified the following significant results. S190-B imagery confirmed previous conclusions from S190-A that the Garlock fault does not extend eastward beyond its known termination near the southern end of Death Valley. In the Avawatz Mountains, California, two faults related to the Garlock fault zone (Mule Spring fault and Leach Spring fault) show evidence of recent activity. There is evidence that faulting related to Death Valley fault zone extends southeastward across the Old Dad Mountains. There, the Old Dad fault shows evidence of recent activity. A significant fault lineament has been identified from McCullough Range, California southeastward to Eagle Tail Mountains in southwestern Arizona. The lineament appears to control tertiary and possible cretaceous intrusives. Considerable right lateral shear is suspected to have taken place along parts of this lineament.

Earthquake epicenters and fault intersections in central and southern California

NASA Technical Reports Server (NTRS)

Abdel-Gawad, M. (Principal Investigator); Silverstein, J.

1972-01-01

The author has identifed the following significant results. ERTS-1 imagery provided evidence for the existence of short transverse fault segments lodged between faults of the San Andreas system in the Coast Ranges, California. They indicate that an early episode of transverse shear has affected the Coast Ranges prior to the establishment of the present San Andreas fault. The fault has been offset by transverse faults of the Transverse Ranges. It appears feasible to identify from ERTS-1 imagery geomorphic criteria of recent fault movements. Plots of historic earthquakes in the Coast Ranges and western Transverse Ranges show clusters in areas where structures are complicated by interaction of tow active fault systems. A fault lineament apparently not previously mapped was identified in the Uinta Mountains, Utah. Part of the lineament show evidence of recent faulting which corresponds to a moderate earthquake cluster.

Rule-based fault diagnosis of hall sensors and fault-tolerant control of PMSM

NASA Astrophysics Data System (ADS)

Song, Ziyou; Li, Jianqiu; Ouyang, Minggao; Gu, Jing; Feng, Xuning; Lu, Dongbin

2013-07-01

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

Verification of an IGBT Fusing Switch for Over-current Protection of the SNS HVCM

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

Benwell, Andrew; Kemp, Mark; Burkhart, Craig

2010-06-11

An IGBT based over-current protection system has been developed to detect faults and limit the damage caused by faults in high voltage converter modulators. During normal operation, an IGBT enables energy to be transferred from storage capacitors to a H-bridge. When a fault occurs, the over-current protection system detects the fault, limits the fault current and opens the IGBT to isolate the remaining stored energy from the fault. This paper presents an experimental verification of the over-current protection system under applicable conditions.

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

Integral Sensor Fault Detection and Isolation for Railway Traction Drive.

PubMed

Garramiola, Fernando; Del Olmo, Jon; Poza, Javier; Madina, Patxi; Almandoz, Gaizka

2018-05-13

Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, in this article an integral diagnosis strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the fault detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the diagnosis is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral fault diagnosis solution is provided, to detect and isolate faults in all the sensors installed in the traction drive.

Integral Sensor Fault Detection and Isolation for Railway Traction Drive

PubMed Central

del Olmo, Jon; Poza, Javier; Madina, Patxi; Almandoz, Gaizka

2018-01-01

Due to the increasing importance of reliability and availability of electric traction drives in Railway applications, early detection of faults has become an important key for Railway traction drive manufacturers. Sensor faults are important sources of failures. Among the different fault diagnosis approaches, in this article an integral diagnosis strategy for sensors in traction drives is presented. Such strategy is composed of an observer-based approach for direct current (DC)-link voltage and catenary current sensors, a frequency analysis approach for motor current phase sensors and a hardware redundancy solution for speed sensors. None of them requires any hardware change requirement in the actual traction drive. All the fault detection and isolation approaches have been validated in a Hardware-in-the-loop platform comprising a Real Time Simulator and a commercial Traction Control Unit for a tram. In comparison to safety-critical systems in Aerospace applications, Railway applications do not need instantaneous detection, and the diagnosis is validated in a short time period for reliable decision. Combining the different approaches and existing hardware redundancy, an integral fault diagnosis solution is provided, to detect and isolate faults in all the sensors installed in the traction drive. PMID:29757251

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.

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.

Fault detection in mechanical systems with friction phenomena: an online neural approximation approach.

PubMed

Papadimitropoulos, Adam; Rovithakis, George A; Parisini, Thomas

2007-07-01

In this paper, the problem of fault detection in mechanical systems performing linear motion, under the action of friction phenomena is addressed. The friction effects are modeled through the dynamic LuGre model. The proposed architecture is built upon an online neural network (NN) approximator, which requires only system's position and velocity. The friction internal state is not assumed to be available for measurement. The neural fault detection methodology is analyzed with respect to its robustness and sensitivity properties. Rigorous fault detectability conditions and upper bounds for the detection time are also derived. Extensive simulation results showing the effectiveness of the proposed methodology are provided, including a real case study on an industrial actuator.

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.

On-line early fault detection and diagnosis of municipal solid waste incinerators

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

Zhao Jinsong; Huang Jianchao; Sun Wei

A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows thatmore » automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI.« less

An autonomous fault detection, isolation, and recovery system for a 20-kHz electric power distribution test bed

NASA Technical Reports Server (NTRS)

Quinn, Todd M.; Walters, Jerry L.

1991-01-01

Future space explorations will require long term human presence in space. Space environments that provide working and living quarters for manned missions are becoming increasingly larger and more sophisticated. Monitor and control of the space environment subsystems by expert system software, which emulate human reasoning processes, could maintain the health of the subsystems and help reduce the human workload. The autonomous power expert (APEX) system was developed to emulate a human expert's reasoning processes used to diagnose fault conditions in the domain of space power distribution. APEX is a fault detection, isolation, and recovery (FDIR) system, capable of autonomous monitoring and control of the power distribution system. APEX consists of a knowledge base, a data base, an inference engine, and various support and interface software. APEX provides the user with an easy-to-use interactive interface. When a fault is detected, APEX will inform the user of the detection. The user can direct APEX to isolate the probable cause of the fault. Once a fault has been isolated, the user can ask APEX to justify its fault isolation and to recommend actions to correct the fault. APEX implementation and capabilities are discussed.

Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

PubMed

Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

2016-06-01

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Bearing damage assessment using Jensen-Rényi Divergence based on EEMD

NASA Astrophysics Data System (ADS)

Singh, Jaskaran; Darpe, A. K.; Singh, S. P.

2017-03-01

An Ensemble Empirical Mode Decomposition (EEMD) and Jensen Rényi divergence (JRD) based methodology is proposed for the degradation assessment of rolling element bearings using vibration data. The EEMD decomposes vibration signals into a set of intrinsic mode functions (IMFs). A systematic methodology to select IMFs that are sensitive and closely related to the fault is proposed in the paper. The change in probability distribution of the energies of the sensitive IMFs is measured through JRD which acts as a damage identification parameter. Evaluation of JRD with sensitive IMFs makes it largely unaffected by change/fluctuations in operating conditions. Further, an algorithm based on Chebyshev's inequality is applied to JRD to identify exact points of change in bearing health and remove outliers. The identified change points are investigated for fault classification as possible locations where specific defect initiation could have taken place. For fault classification, two new parameters are proposed: 'α value' and Probable Fault Index, which together classify the fault. To standardize the degradation process, a Confidence Value parameter is proposed to quantify the bearing degradation value in a range of zero to unity. A simulation study is first carried out to demonstrate the robustness of the proposed JRD parameter under variable operating conditions of load and speed. The proposed methodology is then validated on experimental data (seeded defect data and accelerated bearing life test data). The first validation on two different vibration datasets (inner/outer) obtained from seeded defect experiments demonstrate the effectiveness of JRD parameter in detecting a change in health state as the severity of fault changes. The second validation is on two accelerated life tests. The results demonstrate the proposed approach as a potential tool for bearing performance degradation assessment.

Fault detection and diagnosis of diesel engine valve trains

NASA Astrophysics Data System (ADS)

Flett, Justin; Bone, Gary M.

2016-05-01

This paper presents the development of a fault detection and diagnosis (FDD) system for use with a diesel internal combustion engine (ICE) valve train. A novel feature is generated for each of the valve closing and combustion impacts. Deformed valve spring faults and abnormal valve clearance faults were seeded on a diesel engine instrumented with one accelerometer. Five classification methods were implemented experimentally and compared. The FDD system using the Naïve-Bayes classification method produced the best overall performance, with a lowest detection accuracy (DA) of 99.95% and a lowest classification accuracy (CA) of 99.95% for the spring faults occurring on individual valves. The lowest DA and CA values for multiple faults occurring simultaneously were 99.95% and 92.45%, respectively. The DA and CA results demonstrate the accuracy of our FDD system for diesel ICE valve train fault scenarios not previously addressed in the literature.

System and method for bearing fault detection using stator current noise cancellation

DOEpatents

Zhou, Wei; Lu, Bin; Habetler, Thomas G.; Harley, Ronald G.; Theisen, Peter J.

2010-08-17

A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to repeatedly receive real-time operating current data from the operating motor and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

Autonomous power expert system advanced development

NASA Technical Reports Server (NTRS)

Quinn, Todd M.; Walters, Jerry L.

1991-01-01

The autonomous power expert (APEX) system is being developed at Lewis Research Center to function as a fault diagnosis advisor for a space power distribution test bed. APEX is a rule-based system capable of detecting faults and isolating the probable causes. APEX also has a justification facility to provide natural language explanations about conclusions reached during fault isolation. To help maintain the health of the power distribution system, additional capabilities were added to APEX. These capabilities allow detection and isolation of incipient faults and enable the expert system to recommend actions/procedure to correct the suspected fault conditions. New capabilities for incipient fault detection consist of storage and analysis of historical data and new user interface displays. After the cause of a fault is determined, appropriate recommended actions are selected by rule-based inferencing which provides corrective/extended test procedures. Color graphics displays and improved mouse-selectable menus were also added to provide a friendlier user interface. A discussion of APEX in general and a more detailed description of the incipient detection, recommended actions, and user interface developments during the last year are presented.

Fault detection and fault tolerance in robotics

NASA Technical Reports Server (NTRS)

Visinsky, Monica; Walker, Ian D.; Cavallaro, Joseph R.

1992-01-01

Robots are used in inaccessible or hazardous environments in order to alleviate some of the time, cost and risk involved in preparing men to endure these conditions. In order to perform their expected tasks, the robots are often quite complex, thus increasing their potential for failures. If men must be sent into these environments to repair each component failure in the robot, the advantages of using the robot are quickly lost. Fault tolerant robots are needed which can effectively cope with failures and continue their tasks until repairs can be realistically scheduled. Before fault tolerant capabilities can be created, methods of detecting and pinpointing failures must be perfected. This paper develops a basic fault tree analysis of a robot in order to obtain a better understanding of where failures can occur and how they contribute to other failures in the robot. The resulting failure flow chart can also be used to analyze the resiliency of the robot in the presence of specific faults. By simulating robot failures and fault detection schemes, the problems involved in detecting failures for robots are explored in more depth.

Arc Fault Detection & Localization by Electromagnetic-Acoustic Remote Sensing

NASA Astrophysics Data System (ADS)

Vasile, C.; Ioana, C.

2017-05-01

Electrical arc faults that occur in photovoltaic systems represent a danger due to their economic impact on production and distribution. In this paper we propose a complete system, with focus on the methodology, that enables the detection and localization of the arc fault, by the use of an electromagnetic-acoustic sensing system. By exploiting the multiple emissions of the arc fault, in conjunction with a real-time detection signal processing method, we ensure accurate detection and localization. In its final form, this present work will present in greater detail the complete system, the methods employed, results and performance, alongside further works that will be carried on.

Fast and accurate spectral estimation for online detection of partial broken bar in induction motors

NASA Astrophysics Data System (ADS)

Samanta, Anik Kumar; Naha, Arunava; Routray, Aurobinda; Deb, Alok Kanti

2018-01-01

In this paper, an online and real-time system is presented for detecting partial broken rotor bar (BRB) of inverter-fed squirrel cage induction motors under light load condition. This system with minor modifications can detect any fault that affects the stator current. A fast and accurate spectral estimator based on the theory of Rayleigh quotient is proposed for detecting the spectral signature of BRB. The proposed spectral estimator can precisely determine the relative amplitude of fault sidebands and has low complexity compared to available high-resolution subspace-based spectral estimators. Detection of low-amplitude fault components has been improved by removing the high-amplitude fundamental frequency using an extended-Kalman based signal conditioner. Slip is estimated from the stator current spectrum for accurate localization of the fault component. Complexity and cost of sensors are minimal as only a single-phase stator current is required. The hardware implementation has been carried out on an Intel i7 based embedded target ported through the Simulink Real-Time. Evaluation of threshold and detectability of faults with different conditions of load and fault severity are carried out with empirical cumulative distribution function.

Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals

PubMed Central

Tang, Gang; Hou, Wei; Wang, Huaqing; Luo, Ganggang; Ma, Jianwei

2015-01-01

The Shannon sampling principle requires substantial amounts of data to ensure the accuracy of on-line monitoring of roller bearing fault signals. Challenges are often encountered as a result of the cumbersome data monitoring, thus a novel method focused on compressed vibration signals for detecting roller bearing faults is developed in this study. Considering that harmonics often represent the fault characteristic frequencies in vibration signals, a compressive sensing frame of characteristic harmonics is proposed to detect bearing faults. A compressed vibration signal is first acquired from a sensing matrix with information preserved through a well-designed sampling strategy. A reconstruction process of the under-sampled vibration signal is then pursued as attempts are conducted to detect the characteristic harmonics from sparse measurements through a compressive matching pursuit strategy. In the proposed method bearing fault features depend on the existence of characteristic harmonics, as typically detected directly from compressed data far before reconstruction completion. The process of sampling and detection may then be performed simultaneously without complete recovery of the under-sampled signals. The effectiveness of the proposed method is validated by simulations and experiments. PMID:26473858

A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

NASA Astrophysics Data System (ADS)

Li, Yongbo; Li, Guoyan; Yang, Yuantao; Liang, Xihui; Xu, Minqiang

2018-05-01

The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.

Fault detection and diagnosis in asymmetric multilevel inverter using artificial neural network

NASA Astrophysics Data System (ADS)

Raj, Nithin; Jagadanand, G.; George, Saly

2018-04-01

The increased component requirement to realise multilevel inverter (MLI) fallout in a higher fault prospect due to power semiconductors. In this scenario, efficient fault detection and diagnosis (FDD) strategies to detect and locate the power semiconductor faults have to be incorporated in addition to the conventional protection systems. Even though a number of FDD methods have been introduced in the symmetrical cascaded H-bridge (CHB) MLIs, very few methods address the FDD in asymmetric CHB-MLIs. In this paper, the gate-open circuit FDD strategy in asymmetric CHB-MLI is presented. Here, a single artificial neural network (ANN) is used to detect and diagnose the fault in both binary and trinary configurations of the asymmetric CHB-MLIs. In this method, features of the output voltage of the MLIs are used as to train the ANN for FDD method. The results prove the validity of the proposed method in detecting and locating the fault in both asymmetric MLI configurations. Finally, the ANN response to the input parameter variation is also analysed to access the performance of the proposed ANN-based FDD strategy.

A novel end-to-end fault detection and localization protocol for wavelength-routed WDM networks

NASA Astrophysics Data System (ADS)

Zeng, Hongqing; Vukovic, Alex; Huang, Changcheng

2005-09-01

Recently the wavelength division multiplexing (WDM) networks are becoming prevalent for telecommunication networks. However, even a very short disruption of service caused by network faults may lead to high data loss in such networks due to the high date rates, increased wavelength numbers and density. Therefore, the network survivability is critical and has been intensively studied, where fault detection and localization is the vital part but has received disproportional attentions. In this paper we describe and analyze an end-to-end lightpath fault detection scheme in data plane with the fault notification in control plane. The endeavor is focused on reducing the fault detection time. In this protocol, the source node of each lightpath keeps sending hello packets to the destination node exactly following the path for data traffic. The destination node generates an alarm once a certain number of consecutive hello packets are missed within a given time period. Then the network management unit collects all alarms and locates the faulty source based on the network topology, as well as sends fault notification messages via control plane to either the source node or all upstream nodes along the lightpath. The performance evaluation shows such a protocol can achieve fast fault detection, and at the same time, the overhead brought to the user data by hello packets is negligible.

INNOVATIVE METHODOLOGY FOR DETECTION OF FRACTURE-CONTROLLED SWEET SPOTS IN THE NORTHERN APPALACHIAN BASIN

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

Robert Jacobi; John Fountain

2002-01-30

In the structure task, we completed reducing the data we had collected from a N-S transect on the east of Seneca Lake. We have calculated the fracture frequency for all the fracture sets at each site, and constructed modified rose diagrams that summarize the fracture attributes at each site. These data indicate a N-striking fault near the southeastern shore of Seneca Lake, and also indicate NE and ENE-trending FIDs and faults north of Valois. The orientation and existence of the ENE-striking FIDs and faults are thought to be guided by faults in the Precambrian basement; these basement faults apparently weremore » sufficiently reactivated to cause faulting in the Paleozoic section. Other faults are thrust ramps above the Silurian salt section that were controlled by a far-field Alleghanian stress field. Structure contour maps and isopach maps have been revised based on additional well log analyses. Except for the Glodes Corners Field, the well spacing generally is insufficient to definitively identify faults. However, relatively sharp elevational changes east of Keuka Lake support the contention that faults occur along the east side of Keuka Lake. Outcrop stratigraphy along the east side of Seneca Lake indicates that faults and gentle folds can be inferred from the some exposures along Seneca Lake, but the lensing nature of the individual sandstones can preclude long-distance definitive correlations and structure identification. Soil gas data collected during the 2000 field season was reduced and displayed in the previous semiannual report. The seismic data that Quest licensed has been reprocessed. Several grabens observed in the Trenton reflector are consistent with surface structure, soil gas, and aeromagnetic anomalies. In this report we display an interpreted seismic line that crosses the Glodes Corners and Muck Farm fields. The final report from the subcontractor concerning the completed aeromagnetic survey is included. Prominent magnetic anomalies suggest that faults in the Precambrian basement are located beneath regions where grabens in the Trenton are located. The trend and location of these faults based on aeromagnetics agrees with the location based on FIDs. These data indicate that integration of aeromagnetic and topographic lineaments, surface structure, soil gas with seismic and well logs allows us to extrapolate Trenton-Black River trends away from confirmatory seismic lines.« less

Methods and apparatus using commutative error detection values for fault isolation in multiple node computers

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

Almasi, Gheorghe; Blumrich, Matthias Augustin; Chen, Dong

Methods and apparatus perform fault isolation in multiple node computing systems using commutative error detection values for--example, checksums--to identify and to isolate faulty nodes. When information associated with a reproducible portion of a computer program is injected into a network by a node, a commutative error detection value is calculated. At intervals, node fault detection apparatus associated with the multiple node computer system retrieve commutative error detection values associated with the node and stores them in memory. When the computer program is executed again by the multiple node computer system, new commutative error detection values are created and stored inmore » memory. The node fault detection apparatus identifies faulty nodes by comparing commutative error detection values associated with reproducible portions of the application program generated by a particular node from different runs of the application program. Differences in values indicate a possible faulty node.« less

Tunable architecture for aircraft fault detection

NASA Technical Reports Server (NTRS)

Ganguli, Subhabrata (Inventor); Papageorgiou, George (Inventor); Glavaski-Radovanovic, Sonja (Inventor)

2012-01-01

A method for detecting faults in an aircraft is disclosed. The method involves predicting at least one state of the aircraft and tuning at least one threshold value to tightly upper bound the size of a mismatch between the at least one predicted state and a corresponding actual state of the non-faulted aircraft. If the mismatch between the at least one predicted state and the corresponding actual state is greater than or equal to the at least one threshold value, the method indicates that at least one fault has been detected.

Fracture Modes and Identification of Fault Zones in Wenchuan Earthquake Fault Scientific Drilling Boreholes

NASA Astrophysics Data System (ADS)

Deng, C.; Pan, H.; Zhao, P.; Qin, R.; Peng, L.

2017-12-01

After suffering from the disaster of Wenchuan earthquake on May 12th, 2008, scientists are eager to figure out the structure of formation, the geodynamic processes of faults and the mechanism of earthquake in Wenchuan by drilling five holes into the Yingxiu-Beichuan fault zone and Anxian-Guanxian fault zone. Fractures identification and in-situ stress determination can provide abundant information for formation evaluation and earthquake study. This study describe all the fracture modes in the five boreholes on the basis of cores and image logs, and summarize the response characteristics of fractures in conventional logs. The results indicate that the WFSD boreholes encounter enormous fractures, including natural fractures and induced fractures, and high dip-angle conductive fractures are the most common fractures. The maximum horizontal stress trends along the borehole are deduced as NWW-SEE according to orientations of borehole breakouts and drilling-induced fractures, which is nearly parallel to the strikes of the younger natural fracture sets. Minor positive deviations of AC (acoustic log) and negative deviation of DEN (density log) demonstrate their responses to fracture, followed by CNL (neutron log), resistivity logs and GR (gamma ray log) at different extent of intensity. Besides, considering the fact that the reliable methods for identifying fracture zone, like seismic, core recovery and image logs, can often be hampered by their high cost and limited application, this study propose a method by using conventional logs, which are low-cost and available in even old wells. We employ wavelet decomposition to extract the high frequency information of conventional logs and reconstruction a new log in special format of enhance fracture responses and eliminate nonfracture influence. Results reveal that the new log shows obvious deviations in fault zones, which confirm the potential of conventional logs in fracture zone identification.

The study of active tectonic based on hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Cui, J.; Zhang, S.; Zhang, J.; Shen, X.; Ding, R.; Xu, S.

2017-12-01

As of the latest technical methods, hyperspectral remote sensing technology has been widely used in each brach of the geosciences. However, it is still a blank for using the hyperspectral remote sensing to study the active structrure. Hyperspectral remote sensing, with high spectral resolution, continuous spectrum, continuous spatial data, low cost, etc, has great potentialities in the areas of stratum division and fault identification. Blind fault identification in plains and invisible fault discrimination in loess strata are the two hot problems in the current active fault research. Thus, the study of active fault based on the hyperspectral technology has great theoretical significance and practical value. Magnetic susceptibility (MS) records could reflect the rhythm alteration of the formation. Previous study shown that MS has correlation with spectral feature. In this study, the Emaokou section, located to the northwest of the town of Huairen, in Shanxi Province, has been chosen for invisible fault study. We collected data from the Emaokou section, including spectral data, hyperspectral image, MS data. MS models based on spectral features were established and applied to the UHD185 image for MS mapping. The results shown that MS map corresponded well to the loess sequences. It can recognize the stratum which can not identity by naked eyes. Invisible fault has been found in this section, which is useful for paleoearthquake analysis. The faults act as the conduit for migration of terrestrial gases, the fault zones, especially the structurally weak zones such as inrtersections or bends of fault, may has different material composition. We take Xiadian fault for study. Several samples cross-fault were collected and these samples were measured by ASD Field Spec 3 spectrometer. Spectral classification method has been used for spectral analysis, we found that the spectrum of the fault zone have four special spectral region(550-580nm, 600-700nm, 700-800nm and 800-900nm), which different with the spectrum of the none-fault zone. It could help us welly located the fault zone. The located result correspond well to the physical prospecting method result. The above study shown that Hypersepctral remote sensing technology provide a new method for active study.

Identification of the meta-instability stage via synergy of fault displacement: An experimental study based on the digital image correlation method

NASA Astrophysics Data System (ADS)

Zhuo, Yan-Qun; Ma, Jin; Guo, Yan-Shuang; Ji, Yun-Tao

In stick-slip experiments modeling the occurrence of earthquakes, the meta-instability stage (MIS) is the process that occurs between the peak differential stress and the onset of sudden stress drop. The MIS is the final stage before a fault becomes unstable. Thus, identification of the MIS can help to assess the proximity of the fault to the earthquake critical time. A series of stick-slip experiments on a simulated strike-slip fault were conducted using a biaxial servo-controlled press machine. Digital images of the sample surface were obtained via a high speed camera and processed using a digital image correlation method for analysis of the fault displacement field. Two parameters, A and S, are defined based on fault displacement. A, the normalized length of local pre-slip areas identified by the strike-slip component of fault displacement, is the ratio of the total length of the local pre-slip areas to the length of the fault within the observed areas and quantifies the growth of local unstable areas along the fault. S, the normalized entropy of fault displacement directions, is derived from Shannon entropy and quantifies the disorder of fault displacement directions along the fault. Based on the fault displacement field of three stick-slip events under different loading rates, the experimental results show the following: (1) Both A and S can be expressed as power functions of the normalized time during the non-linearity stage and the MIS. The peak curvatures of A and S represent the onsets of the distinct increase of A and the distinct reduction of S, respectively. (2) During each stick-slip event, the fault evolves into the MIS soon after the curvatures of both A and S reach their peak values, which indicates that the MIS is a synergetic process from independent to cooperative behavior among various parts of a fault and can be approximately identified via the peak curvatures of A and S. A possible application of these experimental results to field conditions is provided. However, further validation is required via additional experiments and exercises.

A Review of Transmission Diagnostics Research at NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Zakajsek, James J.

1994-01-01

This paper presents a summary of the transmission diagnostics research work conducted at NASA Lewis Research Center over the last four years. In 1990, the Transmission Health and Usage Monitoring Research Team at NASA Lewis conducted a survey to determine the critical needs of the diagnostics community. Survey results indicated that experimental verification of gear and bearing fault detection methods, improved fault detection in planetary systems, and damage magnitude assessment and prognostics research were all critical to a highly reliable health and usage monitoring system. In response to this, a variety of transmission fault detection methods were applied to experimentally obtained fatigue data. Failure modes of the fatigue data include a variety of gear pitting failures, tooth wear, tooth fracture, and bearing spalling failures. Overall results indicate that, of the gear fault detection techniques, no one method can successfully detect all possible failure modes. The more successful methods need to be integrated into a single more reliable detection technique. A recently developed method, NA4, in addition to being one of the more successful gear fault detection methods, was also found to exhibit damage magnitude estimation capabilities.

Improved Sensor Fault Detection, Isolation, and Mitigation Using Multiple Observers Approach

PubMed Central

Wang, Zheng; Anand, D. M.; Moyne, J.; Tilbury, D. M.

2017-01-01

Traditional Fault Detection and Isolation (FDI) methods analyze a residual signal to detect and isolate sensor faults. The residual signal is the difference between the sensor measurements and the estimated outputs of the system based on an observer. The traditional residual-based FDI methods, however, have some limitations. First, they require that the observer has reached its steady state. In addition, residual-based methods may not detect some sensor faults, such as faults on critical sensors that result in an unobservable system. Furthermore, the system may be in jeopardy if actions required for mitigating the impact of the faulty sensors are not taken before the faulty sensors are identified. The contribution of this paper is to propose three new methods to address these limitations. Faults that occur during the observers' transient state can be detected by analyzing the convergence rate of the estimation error. Open-loop observers, which do not rely on sensor information, are used to detect faults on critical sensors. By switching among different observers, we can potentially mitigate the impact of the faulty sensor during the FDI process. These three methods are systematically integrated with a previously developed residual-based method to provide an improved FDI and mitigation capability framework. The overall approach is validated mathematically, and the effectiveness of the overall approach is demonstrated through simulation on a 5-state suspension system. PMID:28924303

Algorithm-Based Fault Tolerance for Numerical Subroutines

NASA Technical Reports Server (NTRS)

Tumon, Michael; Granat, Robert; Lou, John

2007-01-01

A software library implements a new methodology of detecting faults in numerical subroutines, thus enabling application programs that contain the subroutines to recover transparently from single-event upsets. The software library in question is fault-detecting middleware that is wrapped around the numericalsubroutines. Conventional serial versions (based on LAPACK and FFTW) and a parallel version (based on ScaLAPACK) exist. The source code of the application program that contains the numerical subroutines is not modified, and the middleware is transparent to the user. The methodology used is a type of algorithm- based fault tolerance (ABFT). In ABFT, a checksum is computed before a computation and compared with the checksum of the computational result; an error is declared if the difference between the checksums exceeds some threshold. Novel normalization methods are used in the checksum comparison to ensure correct fault detections independent of algorithm inputs. In tests of this software reported in the peer-reviewed literature, this library was shown to enable detection of 99.9 percent of significant faults while generating no false alarms.

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.

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

A Three-Dimensional Receiver Operator Characteristic Surface Diagnostic Metric

NASA Technical Reports Server (NTRS)

Simon, Donald L.

2011-01-01

Receiver Operator Characteristic (ROC) curves are commonly applied as metrics for quantifying the performance of binary fault detection systems. An ROC curve provides a visual representation of a detection system s True Positive Rate versus False Positive Rate sensitivity as the detection threshold is varied. The area under the curve provides a measure of fault detection performance independent of the applied detection threshold. While the standard ROC curve is well suited for quantifying binary fault detection performance, it is not suitable for quantifying the classification performance of multi-fault classification problems. Furthermore, it does not provide a measure of diagnostic latency. To address these shortcomings, a novel three-dimensional receiver operator characteristic (3D ROC) surface metric has been developed. This is done by generating and applying two separate curves: the standard ROC curve reflecting fault detection performance, and a second curve reflecting fault classification performance. A third dimension, diagnostic latency, is added giving rise to 3D ROC surfaces. Applying numerical integration techniques, the volumes under and between the surfaces are calculated to produce metrics of the diagnostic system s detection and classification performance. This paper will describe the 3D ROC surface metric in detail, and present an example of its application for quantifying the performance of aircraft engine gas path diagnostic methods. Metric limitations and potential enhancements are also discussed

Generic, scalable and decentralized fault detection for robot swarms.

PubMed

Tarapore, Danesh; Christensen, Anders Lyhne; Timmis, Jon

2017-01-01

Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system's capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation.

Generic, scalable and decentralized fault detection for robot swarms

PubMed Central

Christensen, Anders Lyhne; Timmis, Jon

2017-01-01

Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system’s capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation. PMID:28806756

Fault-tolerant optimised tracking control for unknown discrete-time linear systems using a combined reinforcement learning and residual compensation methodology

NASA Astrophysics Data System (ADS)

Han, Ke-Zhen; Feng, Jian; Cui, Xiaohong

2017-10-01

This paper considers the fault-tolerant optimised tracking control (FTOTC) problem for unknown discrete-time linear system. A research scheme is proposed on the basis of data-based parity space identification, reinforcement learning and residual compensation techniques. The main characteristic of this research scheme lies in the parity-space-identification-based simultaneous tracking control and residual compensation. The specific technical line consists of four main contents: apply subspace aided method to design observer-based residual generator; use reinforcement Q-learning approach to solve optimised tracking control policy; rely on robust H∞ theory to achieve noise attenuation; adopt fault estimation triggered by residual generator to perform fault compensation. To clarify the design and implementation procedures, an integrated algorithm is further constructed to link up these four functional units. The detailed analysis and proof are subsequently given to explain the guaranteed FTOTC performance of the proposed conclusions. Finally, a case simulation is provided to verify its effectiveness.

Comparative Study of Fault Diagnostic Methods in Voltage Source Inverter Fed Three Phase Induction Motor Drive

NASA Astrophysics Data System (ADS)

Dhumale, R. B.; Lokhande, S. D.

2017-05-01

Three phase Pulse Width Modulation inverter plays vital role in industrial applications. The performance of inverter demeans as several types of faults take place in it. The widely used switching devices in power electronics are Insulated Gate Bipolar Transistors (IGBTs) and Metal Oxide Field Effect Transistors (MOSFET). The IGBTs faults are broadly classified as base or collector open circuit fault, misfiring fault and short circuit fault. To develop consistency and performance of inverter, knowledge of fault mode is extremely important. This paper presents the comparative study of IGBTs fault diagnosis. Experimental set up is implemented for data acquisition under various faulty and healthy conditions. Recent methods are executed using MATLAB-Simulink and compared using key parameters like average accuracy, fault detection time, implementation efforts, threshold dependency, and detection parameter, resistivity against noise and load dependency.

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.

Soft-Fault Detection Technologies Developed for Electrical Power Systems

NASA Technical Reports Server (NTRS)

Button, Robert M.

2004-01-01

The NASA Glenn Research Center, partner universities, and defense contractors are working to develop intelligent power management and distribution (PMAD) technologies for future spacecraft and launch vehicles. The goals are to provide higher performance (efficiency, transient response, and stability), higher fault tolerance, and higher reliability through the application of digital control and communication technologies. It is also expected that these technologies will eventually reduce the design, development, manufacturing, and integration costs for large, electrical power systems for space vehicles. The main focus of this research has been to incorporate digital control, communications, and intelligent algorithms into power electronic devices such as direct-current to direct-current (dc-dc) converters and protective switchgear. These technologies, in turn, will enable revolutionary changes in the way electrical power systems are designed, developed, configured, and integrated in aerospace vehicles and satellites. Initial successes in integrating modern, digital controllers have proven that transient response performance can be improved using advanced nonlinear control algorithms. One technology being developed includes the detection of "soft faults," those not typically covered by current systems in use today. Soft faults include arcing faults, corona discharge faults, and undetected leakage currents. Using digital control and advanced signal analysis algorithms, we have shown that it is possible to reliably detect arcing faults in high-voltage dc power distribution systems (see the preceding photograph). Another research effort has shown that low-level leakage faults and cable degradation can be detected by analyzing power system parameters over time. This additional fault detection capability will result in higher reliability for long-lived power systems such as reusable launch vehicles and space exploration missions.

Finding Faults: Tohoku and other Active Megathrusts/Megasplays

NASA Astrophysics Data System (ADS)

Moore, J. C.; Conin, M.; Cook, B. J.; Kirkpatrick, J. D.; Remitti, F.; Chester, F.; Nakamura, Y.; Lin, W.; Saito, S.; Scientific Team, E.

2012-12-01

Current subduction-fault drilling procedure is to drill a logging hole, identify target faults, then core and instrument them. Seismic data may constrain faults but the additional resolution of borehole logs is necessary for efficient coring and instrumentation under difficult conditions and tight schedules. Thus, refining the methodology of identifying faults in logging data has become important, and thus comparison of log signatures of faults in different locations is worthwhile. At the C0019 (JFAST) drill site, the Tohoku megathrust was principally identified as a decollement where steep cylindrically-folded bedding abruptly flattens below the basal detachment. A similar structural contrast occurs across a megasplay fault in the NanTroSEIZE transect (Site C0004). At the Tohoku decollement, a high gamma-ray value from a pelagic clay layer, predicted as a likely decollement sediment type, strengthens the megathrust interpretation. The original identification of the pelagic clay as a decollement candidate was based on results of previous coring of an oceanic reference site. Negative density anomalies, often seen as low resistivity zones, identified a subsidiary fault in the deformed prism overlying the Tohoku megathrust. Elsewhere, at Barbados, Nankai (Moroto), and Costa Rica, negative density anomalies are associated with the decollement and other faults in hanging walls. Log-based density anomalies in fault zones provide a basis for recognizing in-situ fault zone dilation. At the Tohoku Site C0019, breakouts are present above but not below the megathrust. Changes in breakout orientation and width (stress magnitude) occur across megasplay faults at Sites C0004 and C0010 in the NantroSEIZE transect. Annular pressure anomalies are not apparent at the Tohoku megathrust, but are variably associated with faults and fracture zones drilled along the NanTroSEIZE transect. Overall, images of changes in structural features, negative density anomalies, and changes in breakout occurrence and orientation provide the most common log criteria for recognizing major thrust zones in ocean drilling holes at convergent margins. In the case of JFAST, identification of faults by logging was confirmed during subsequent coring activities, and logging data was critical for successful placement of the observatory down hole.

A simplified dynamic model of the T700 turboshaft engine

NASA Technical Reports Server (NTRS)

Duyar, Ahmet; Gu, Zhen; Litt, Jonathan S.

1992-01-01

A simplified open-loop dynamic model of the T700 turboshaft engine, valid within the normal operating range of the engine, is developed. This model is obtained by linking linear state space models obtained at different engine operating points. Each linear model is developed from a detailed nonlinear engine simulation using a multivariable system identification and realization method. The simplified model may be used with a model-based real time diagnostic scheme for fault detection and diagnostics, as well as for open loop engine dynamics studies and closed loop control analysis utilizing a user generated control law.

Usage of Fault Detection Isolation & Recovery (FDIR) in Constellation (CxP) Launch Operations

NASA Technical Reports Server (NTRS)

Ferrell, Rob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Spirkovska, Lilly; Hall, David; Brown, Barbara

2010-01-01

This paper will explore the usage of Fault Detection Isolation & Recovery (FDIR) in the Constellation Exploration Program (CxP), in particular Launch Operations at Kennedy Space Center (KSC). NASA's Exploration Technology Development Program (ETDP) is currently funding a project that is developing a prototype FDIR to demonstrate the feasibility of incorporating FDIR into the CxP Ground Operations Launch Control System (LCS). An architecture that supports multiple FDIR tools has been formulated that will support integration into the CxP Ground Operation's Launch Control System (LCS). In addition, tools have been selected that provide fault detection, fault isolation, and anomaly detection along with integration between Flight and Ground elements.

Identification of an internal combustion engine model by nonlinear multi-input multi-output system identification. Ph.D. Thesis

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

Luh, G.C.

1994-01-01

This thesis presents the application of advanced modeling techniques to construct nonlinear forward and inverse models of internal combustion engines for the detection and isolation of incipient faults. The NARMAX (Nonlinear Auto-Regressive Moving Average modeling with eXogenous inputs) technique of system identification proposed by Leontaritis and Billings was used to derive the nonlinear model of a internal combustion engine, over operating conditions corresponding to the I/M240 cycle. The I/M240 cycle is a standard proposed by the United States Environmental Protection Agency to measure tailpipe emissions in inspection and maintenance programs and consists of a driving schedule developed for the purposemore » of testing compliance with federal vehicle emission standards for carbon monoxide, unburned hydrocarbons, and nitrogen oxides. The experimental work for model identification and validation was performed on a 3.0 liter V6 engine installed in an engine test cell at the Center for Automotive Research at The Ohio State University. In this thesis, different types of model structures were proposed to obtain multi-input multi-output (MIMO) nonlinear NARX models. A modification of the algorithm proposed by He and Asada was used to estimate the robust orders of the derived MIMO nonlinear models. A methodology for the analysis of inverse NARX model was developed. Two methods were proposed to derive the inverse NARX model: (1) inversion from the forward NARX model; and (2) direct identification of inverse model from the output-input data set. In this thesis, invertibility, minimum-phase characteristic of zero dynamics, and stability analysis of NARX forward model are also discussed. Stability in the sense of Lyapunov is also investigated to check the stability of the identified forward and inverse models. This application of inverse problem leads to the estimation of unknown inputs and to actuator fault diagnosis.« less

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.

All-to-all sequenced fault detection system

DOEpatents

Archer, Charles Jens; Pinnow, Kurt Walter; Ratterman, Joseph D.; Smith, Brian Edward

2010-11-02

An apparatus, program product and method enable nodal fault detection by sequencing communications between all system nodes. A master node may coordinate communications between two slave nodes before sequencing to and initiating communications between a new pair of slave nodes. The communications may be analyzed to determine the nodal fault.

Discrete Wavelet Transform for Fault Locations in Underground Distribution System

NASA Astrophysics Data System (ADS)

Apisit, C.; Ngaopitakkul, A.

2010-10-01

In this paper, a technique for detecting faults in underground distribution system is presented. Discrete Wavelet Transform (DWT) based on traveling wave is employed in order to detect the high frequency components and to identify fault locations in the underground distribution system. The first peak time obtained from the faulty bus is employed for calculating the distance of fault from sending end. The validity of the proposed technique is tested with various fault inception angles, fault locations and faulty phases. The result is found that the proposed technique provides satisfactory result and will be very useful in the development of power systems protection scheme.

Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization

PubMed Central

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called “relative ratio symptom parameters” are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks. PMID:22163833

Intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization.

PubMed

Li, Ke; Chen, Peng

2011-01-01

Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called "relative ratio symptom parameters" are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks.

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.

Various Indices for Diagnosis of Air-gap Eccentricity Fault in Induction Motor-A Review

NASA Astrophysics Data System (ADS)

Nikhil; Mathew, Lini, Dr.; Sharma, Amandeep

2018-03-01

From the past few years, research has gained an ardent pace in the field of fault detection and diagnosis in induction motors. In the current scenario, software is being introduced with diagnostic features to improve stability and reliability in fault diagnostic techniques. Human involvement in decision making for fault detection is slowly being replaced by Artificial Intelligence techniques. In this paper, a brief introduction of eccentricity fault is presented along with their causes and effects on the health of induction motors. Various indices used to detect eccentricity are being introduced along with their boundary conditions and their future scope of research. At last, merits and demerits of all indices are discussed and a comparison is made between them.

Soft Computing Application in Fault Detection of Induction Motor

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

Konar, P.; Puhan, P. S.; Chattopadhyay, P. Dr.

2010-10-26

The paper investigates the effectiveness of different patter classifier like Feed Forward Back Propagation (FFBPN), Radial Basis Function (RBF) and Support Vector Machine (SVM) for detection of bearing faults in Induction Motor. The steady state motor current with Park's Transformation has been used for discrimination of inner race and outer race bearing defects. The RBF neural network shows very encouraging results for multi-class classification problems and is hoped to set up a base for incipient fault detection of induction motor. SVM is also found to be a very good fault classifier which is highly competitive with RBF.

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.

Evaluation of an Enhanced Bank of Kalman Filters for In-Flight Aircraft Engine Sensor Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2004-01-01

In this paper, an approach for in-flight fault detection and isolation (FDI) of aircraft engine sensors based on a bank of Kalman filters is developed. This approach utilizes multiple Kalman filters, each of which is designed based on a specific fault hypothesis. When the propulsion system experiences a fault, only one Kalman filter with the correct hypothesis is able to maintain the nominal estimation performance. Based on this knowledge, the isolation of faults is achieved. Since the propulsion system may experience component and actuator faults as well, a sensor FDI system must be robust in terms of avoiding misclassifications of any anomalies. The proposed approach utilizes a bank of (m+1) Kalman filters where m is the number of sensors being monitored. One Kalman filter is used for the detection of component and actuator faults while each of the other m filters detects a fault in a specific sensor. With this setup, the overall robustness of the sensor FDI system to anomalies is enhanced. Moreover, numerous component fault events can be accounted for by the FDI system. The sensor FDI system is applied to a commercial aircraft engine simulation, and its performance is evaluated at multiple power settings at a cruise operating point using various fault scenarios.

Fault detection of gearbox using time-frequency method

NASA Astrophysics Data System (ADS)

Widodo, A.; Satrijo, Dj.; Prahasto, T.; Haryanto, I.

2017-04-01

This research deals with fault detection and diagnosis of gearbox by using vibration signature. In this work, fault detection and diagnosis are approached by employing time-frequency method, and then the results are compared with cepstrum analysis. Experimental work has been conducted for data acquisition of vibration signal thru self-designed gearbox test rig. This test-rig is able to demonstrate normal and faulty gearbox i.e., wears and tooth breakage. Three accelerometers were used for vibration signal acquisition from gearbox, and optical tachometer was used for shaft rotation speed measurement. The results show that frequency domain analysis using fast-fourier transform was less sensitive to wears and tooth breakage condition. However, the method of short-time fourier transform was able to monitor the faults in gearbox. Wavelet Transform (WT) method also showed good performance in gearbox fault detection using vibration signal after employing time synchronous averaging (TSA).

Remote Structural Health Monitoring and Advanced Prognostics of Wind Turbines

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

Douglas Brown; Bernard Laskowski

The prospect of substantial investment in wind energy generation represents a significant capital investment strategy. In order to maximize the life-cycle of wind turbines, associated rotors, gears, and structural towers, a capability to detect and predict (prognostics) the onset of mechanical faults at a sufficiently early stage for maintenance actions to be planned would significantly reduce both maintenance and operational costs. Advancement towards this effort has been made through the development of anomaly detection, fault detection and fault diagnosis routines to identify selected fault modes of a wind turbine based on available sensor data preceding an unscheduled emergency shutdown. Themore » anomaly detection approach employs spectral techniques to find an approximation of the data using a combination of attributes that capture the bulk of variability in the data. Fault detection and diagnosis (FDD) is performed using a neural network-based classifier trained from baseline and fault data recorded during known failure conditions. The approach has been evaluated for known baseline conditions and three selected failure modes: pitch rate failure, low oil pressure failure and a gearbox gear-tooth failure. Experimental results demonstrate the approach can distinguish between these failure modes and normal baseline behavior within a specified statistical accuracy.« less

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.

Fault diagnosis for wind turbine planetary ring gear via a meshing resonance based filtering algorithm.

PubMed

Wang, Tianyang; Chu, Fulei; Han, Qinkai

2017-03-01

Identifying the differences between the spectra or envelope spectra of a faulty signal and a healthy baseline signal is an efficient planetary gearbox local fault detection strategy. However, causes other than local faults can also generate the characteristic frequency of a ring gear fault; this may further affect the detection of a local fault. To address this issue, a new filtering algorithm based on the meshing resonance phenomenon is proposed. In detail, the raw signal is first decomposed into different frequency bands and levels. Then, a new meshing index and an MRgram are constructed to determine which bands belong to the meshing resonance frequency band. Furthermore, an optimal filter band is selected from this MRgram. Finally, the ring gear fault can be detected according to the envelope spectrum of the band-pass filtering result. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

System and method for motor fault detection using stator current noise cancellation

DOEpatents

Zhou, Wei; Lu, Bin; Nowak, Michael P.; Dimino, Steven A.

2010-12-07

A system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. The system includes a controller configured to detect indicia of incipient mechanical motor faults. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The processor is also programmed to acquire at least on additional set of real-time operating current data from the motor during operation, redefine the noise component present in each additional set of real-time operating current data, and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components.

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.

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.

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 diagnosis of sensor networked structures with multiple faults using a virtual beam based approach

NASA Astrophysics Data System (ADS)

Wang, H.; Jing, X. J.

2017-07-01

This paper presents a virtual beam based approach suitable for conducting diagnosis of multiple faults in complex structures with limited prior knowledge of the faults involved. The "virtual beam", a recently-proposed concept for fault detection in complex structures, is applied, which consists of a chain of sensors representing a vibration energy transmission path embedded in the complex structure. Statistical tests and adaptive threshold are particularly adopted for fault detection due to limited prior knowledge of normal operational conditions and fault conditions. To isolate the multiple faults within a specific structure or substructure of a more complex one, a 'biased running' strategy is developed and embedded within the bacterial-based optimization method to construct effective virtual beams and thus to improve the accuracy of localization. The proposed method is easy and efficient to implement for multiple fault localization with limited prior knowledge of normal conditions and faults. With extensive experimental results, it is validated that the proposed method can localize both single fault and multiple faults more effectively than the classical trust index subtract on negative add on positive (TI-SNAP) method.

Pseudo-fault signal assisted EMD for fault detection and isolation in rotating machines

NASA Astrophysics Data System (ADS)

Singh, Dheeraj Sharan; Zhao, Qing

2016-12-01

This paper presents a novel data driven technique for the detection and isolation of faults, which generate impacts in a rotating equipment. The technique is built upon the principles of empirical mode decomposition (EMD), envelope analysis and pseudo-fault signal for fault separation. Firstly, the most dominant intrinsic mode function (IMF) is identified using EMD of a raw signal, which contains all the necessary information about the faults. The envelope of this IMF is often modulated with multiple vibration sources and noise. A second level decomposition is performed by applying pseudo-fault signal (PFS) assisted EMD on the envelope. A pseudo-fault signal is constructed based on the known fault characteristic frequency of the particular machine. The objective of using external (pseudo-fault) signal is to isolate different fault frequencies, present in the envelope . The pseudo-fault signal serves dual purposes: (i) it solves the mode mixing problem inherent in EMD, (ii) it isolates and quantifies a particular fault frequency component. The proposed technique is suitable for real-time implementation, which has also been validated on simulated fault and experimental data corresponding to a bearing and a gear-box set-up, respectively.

Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.

PubMed

Yi, Qu; Zhan-ming, Li; Er-chao, Li

2012-11-01

A new fault detection and diagnosis (FDD) problem via the output probability density functions (PDFs) for non-gausian stochastic distribution systems (SDSs) is investigated. The PDFs can be approximated by radial basis functions (RBFs) neural networks. Different from conventional FDD problems, the measured information for FDD is the output stochastic distributions and the stochastic variables involved are not confined to Gaussian ones. A (RBFs) neural network technique is proposed so that the output PDFs can be formulated in terms of the dynamic weighings of the RBFs neural network. In this work, a nonlinear adaptive observer-based fault detection and diagnosis algorithm is presented by introducing the tuning parameter so that the residual is as sensitive as possible to the fault. Stability and Convergency analysis is performed in fault detection and fault diagnosis analysis for the error dynamic system. At last, an illustrated example is given to demonstrate the efficiency of the proposed algorithm, and satisfactory results have been obtained. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Autonomous Cryogenics Loading Operations Simulation Software: Knowledgebase Autonomous Test Engineer

NASA Technical Reports Server (NTRS)

Wehner, Walter S.

2012-01-01

The Simulation Software, KATE (Knowledgebase Autonomous Test Engineer), is used to demonstrate the automatic identification of faults in a system. The ACLO (Autonomous Cryogenics Loading Operation) project uses KATE to monitor and find faults in the loading of the cryogenics int o a vehicle fuel tank. The KATE software interfaces with the IHM (Integrated Health Management) systems bus to communicate with other systems that are part of ACLO. One system that KATE uses the IHM bus to communicate with is AIS (Advanced Inspection System). KATE will send messages to AIS when there is a detected anomaly. These messages include visual inspection of specific valves, pressure gauges and control messages to have AIS open or close manual valves. My goals include implementing the connection to the IHM bus within KATE and for the AIS project. I will also be working on implementing changes to KATE's Ul and implementing the physics objects in KATE that will model portions of the cryogenics loading operation.

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.

Fault Tree Analysis: A Bibliography

NASA Technical Reports Server (NTRS)

2000-01-01

Fault tree analysis is a top-down approach to the identification of process hazards. It is as one of the best methods for systematically identifying an graphically displaying the many ways some things can go wrong. This bibliography references 266 documents in the NASA STI Database that contain the major concepts. fault tree analysis, risk an probability theory, in the basic index or major subject terms. An abstract is included with most citations, followed by the applicable subject terms.

Error Mitigation of Point-to-Point Communication for Fault-Tolerant Computing

NASA Technical Reports Server (NTRS)

Akamine, Robert L.; Hodson, Robert F.; LaMeres, Brock J.; Ray, Robert E.

2011-01-01

Fault tolerant systems require the ability to detect and recover from physical damage caused by the hardware s environment, faulty connectors, and system degradation over time. This ability applies to military, space, and industrial computing applications. The integrity of Point-to-Point (P2P) communication, between two microcontrollers for example, is an essential part of fault tolerant computing systems. In this paper, different methods of fault detection and recovery are presented and analyzed.

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.

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.

Avionic Air Data Sensors Fault Detection and Isolation by means of Singular Perturbation and Geometric Approach

PubMed Central

2017-01-01

Singular Perturbations represent an advantageous theory to deal with systems characterized by a two-time scale separation, such as the longitudinal dynamics of aircraft which are called phugoid and short period. In this work, the combination of the NonLinear Geometric Approach and the Singular Perturbations leads to an innovative Fault Detection and Isolation system dedicated to the isolation of faults affecting the air data system of a general aviation aircraft. The isolation capabilities, obtained by means of the approach proposed in this work, allow for the solution of a fault isolation problem otherwise not solvable by means of standard geometric techniques. Extensive Monte-Carlo simulations, exploiting a high fidelity aircraft simulator, show the effectiveness of the proposed Fault Detection and Isolation system. PMID:28946673

A new debris sensor based on dual excitation sources for online debris monitoring

NASA Astrophysics Data System (ADS)

Hong, Wei; Wang, Shaoping; Tomovic, Mileta M.; Liu, Haokuo; Wang, Xingjian

2015-09-01

Mechanical systems could be severely damaged by loose debris generated through wear processes between contact surfaces. Hence, debris detection is necessary for effective fault diagnosis, life prediction, and prevention of catastrophic failures. This paper presents a new in-line debris sensor for hydraulic systems based on dual excitation sources. The proposed sensor makes magnetic lines more concentrated while at the same time improving magnetic field uniformity. As a result the sensor has higher sensitivity and improved precision. This paper develops the sensor model, discusses sensor structural features, and introduces a measurement method for debris size identification. Finally, experimental verification is presented indicating that that the sensor can effectively detect 81 μm (cube) or larger particles in 12 mm outside diameter (OD) organic glass pipe.

Intermittent/transient fault phenomena in digital systems

NASA Technical Reports Server (NTRS)

Masson, G. M.

1977-01-01

An overview of the intermittent/transient (IT) fault study is presented. An interval survivability evaluation of digital systems for IT faults is discussed along with a method for detecting and diagnosing IT faults in digital systems.

The detection error of thermal test low-frequency cable based on M sequence correlation algorithm

NASA Astrophysics Data System (ADS)

Wu, Dongliang; Ge, Zheyang; Tong, Xin; Du, Chunlin

2018-04-01

The problem of low accuracy and low efficiency of off-line detecting on thermal test low-frequency cable faults could be solved by designing a cable fault detection system, based on FPGA export M sequence code(Linear feedback shift register sequence) as pulse signal source. The design principle of SSTDR (Spread spectrum time-domain reflectometry) reflection method and hardware on-line monitoring setup figure is discussed in this paper. Testing data show that, this detection error increases with fault location of thermal test low-frequency cable.

An intelligent control system for failure detection and controller reconfiguration

NASA Technical Reports Server (NTRS)

Biswas, Saroj K.

1994-01-01

We present an architecture of an intelligent restructurable control system to automatically detect failure of system components, assess its impact on system performance and safety, and reconfigure the controller for performance recovery. Fault detection is based on neural network associative memories and pattern classifiers, and is implemented using a multilayer feedforward network. Details of the fault detection network along with simulation results on health monitoring of a dc motor have been presented. Conceptual developments for fault assessment using an expert system and controller reconfiguration using a neural network are outlined.

Rotorcraft Diagnostics

NASA Technical Reports Server (NTRS)

Haste, Deepak; Azam, Mohammad; Ghoshal, Sudipto; Monte, James

2012-01-01

Health management (HM) in any engineering systems requires adequate understanding about the system s functioning; a sufficient amount of monitored data; the capability to extract, analyze, and collate information; and the capability to combine understanding and information for HM-related estimation and decision-making. Rotorcraft systems are, in general, highly complex. Obtaining adequate understanding about functioning of such systems is quite difficult, because of the proprietary (restricted access) nature of their designs and dynamic models. Development of an EIM (exact inverse map) solution for rotorcraft requires a process that can overcome the abovementioned difficulties and maximally utilize monitored information for HM facilitation via employing advanced analytic techniques. The goal was to develop a versatile HM solution for rotorcraft for facilitation of the Condition Based Maintenance Plus (CBM+) capabilities. The effort was geared towards developing analytic and reasoning techniques, and proving the ability to embed the required capabilities on a rotorcraft platform, paving the way for implementing the solution on an aircraft-level system for consolidation and reporting. The solution for rotorcraft can he used offboard or embedded directly onto a rotorcraft system. The envisioned solution utilizes available monitored and archived data for real-time fault detection and identification, failure precursor identification, and offline fault detection and diagnostics, health condition forecasting, optimal guided troubleshooting, and maintenance decision support. A variant of the onboard version is a self-contained hardware and software (HW+SW) package that can be embedded on rotorcraft systems. The HM solution comprises components that gather/ingest data and information, perform information/feature extraction, analyze information in conjunction with the dependency/diagnostic model of the target system, facilitate optimal guided troubleshooting, and offer decision support for optimal maintenance.

Sensor fault detection and recovery in satellite attitude control

NASA Astrophysics Data System (ADS)

Nasrolahi, Seiied Saeed; Abdollahi, Farzaneh

2018-04-01

This paper proposes an integrated sensor fault detection and recovery for the satellite attitude control system. By introducing a nonlinear observer, the healthy sensor measurements are provided. Considering attitude dynamics and kinematic, a novel observer is developed to detect the fault in angular rate as well as attitude sensors individually or simultaneously. There is no limit on type and configuration of attitude sensors. By designing a state feedback based control signal and Lyapunov stability criterion, the uniformly ultimately boundedness of tracking errors in the presence of sensor faults is guaranteed. Finally, simulation results are presented to illustrate the performance of the integrated scheme.

Method and apparatus for in-situ detection and isolation of aircraft engine faults

NASA Technical Reports Server (NTRS)

Bonanni, Pierino Gianni (Inventor); Brunell, Brent Jerome (Inventor)

2007-01-01

A method for performing a fault estimation based on residuals of detected signals includes determining an operating regime based on a plurality of parameters, extracting predetermined noise standard deviations of the residuals corresponding to the operating regime and scaling the residuals, calculating a magnitude of a measurement vector of the scaled residuals and comparing the magnitude to a decision threshold value, extracting an average, or mean direction and a fault level mapping for each of a plurality of fault types, based on the operating regime, calculating a projection of the measurement vector onto the average direction of each of the plurality of fault types, determining a fault type based on which projection is maximum, and mapping the projection to a continuous-valued fault level using a lookup table.

In-flight Fault Detection and Isolation in Aircraft Flight Control Systems

NASA Technical Reports Server (NTRS)

Azam, Mohammad; Pattipati, Krishna; Allanach, Jeffrey; Poll, Scott; Patterson-Hine, Ann

2005-01-01

In this paper we consider the problem of test design for real-time fault detection and isolation (FDI) in the flight control system of fixed-wing aircraft. We focus on the faults that are manifested in the control surface elements (e.g., aileron, elevator, rudder and stabilizer) of an aircraft. For demonstration purposes, we restrict our focus on the faults belonging to nine basic fault classes. The diagnostic tests are performed on the features extracted from fifty monitored system parameters. The proposed tests are able to uniquely isolate each of the faults at almost all severity levels. A neural network-based flight control simulator, FLTZ(Registered TradeMark), is used for the simulation of various faults in fixed-wing aircraft flight control systems for the purpose of FDI.

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

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.

Unexpected earthquake hazard revealed by Holocene rupture on the Kenchreai Fault (central Greece): Implications for weak sub-fault shear zones

NASA Astrophysics Data System (ADS)

Copley, Alex; Grützner, Christoph; Howell, Andy; Jackson, James; Penney, Camilla; Wimpenny, Sam

2018-03-01

High-resolution elevation models, palaeoseismic trenching, and Quaternary dating demonstrate that the Kenchreai Fault in the eastern Gulf of Corinth (Greece) has ruptured in the Holocene. Along with the adjacent Pisia and Heraion Faults (which ruptured in 1981), our results indicate the presence of closely-spaced and parallel normal faults that are simultaneously active, but at different rates. Such a configuration allows us to address one of the major questions in understanding the earthquake cycle, specifically what controls the distribution of interseismic strain accumulation? Our results imply that the interseismic loading and subsequent earthquakes on these faults are governed by weak shear zones in the underlying ductile crust. In addition, the identification of significant earthquake slip on a fault that does not dominate the late Quaternary geomorphology or vertical coastal motions in the region provides an important lesson in earthquake hazard assessment.

Wavelet Based Protection Scheme for Multi Terminal Transmission System with PV and Wind Generation

NASA Astrophysics Data System (ADS)

Manju Sree, Y.; Goli, Ravi kumar; Ramaiah, V.

2017-08-01

A hybrid generation is a part of large power system in which number of sources usually attached to a power electronic converter and loads are clustered can operate independent of the main power system. The protection scheme is crucial against faults based on traditional over current protection since there are adequate problems due to fault currents in the mode of operation. This paper adopts a new approach for detection, discrimination of the faults for multi terminal transmission line protection in presence of hybrid generation. Transient current based protection scheme is developed with discrete wavelet transform. Fault indices of all phase currents at all terminals are obtained by analyzing the detail coefficients of current signals using bior 1.5 mother wavelet. This scheme is tested for different types of faults and is found effective for detection and discrimination of fault with various fault inception angle and fault impedance.

A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

NASA Astrophysics Data System (ADS)

Kim, Pyung Soo

2017-04-01

In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.

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.

Protection of Renewable-dominated Microgrids: Challenges and Potential Solutions.

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

Elkhatib, Mohamed; Ellis, Abraham; Milan Biswal

keywords : Microgrid Protection, Impedance Relay, Signal Processing-based Fault Detec- tion, Networked Microgrids, Communication-Assisted Protection In this report we address the challenge of designing efficient protection system for inverter- dominated microgrids. These microgrids are characterised with limited fault current capacity as a result of current-limiting protection functions of inverters. Typically, inverters limit their fault contribution in sub-cycle time frame to as low as 1.1 per unit. As a result, overcurrent protection could fail completely to detect faults in inverter-dominated microgrids. As part of this project a detailed literature survey of existing and proposed microgrid protection schemes were conducted. The surveymore » concluded that there is a gap in the available microgrid protection methods. The only credible protection solution available in literature for low- fault inverter-dominated microgrids is the differential protection scheme which represents a robust transmission-grade protection solution but at a very high cost. Two non-overcurrent protection schemes were investigated as part of this project; impedance-based protection and transient-based protection. Impedance-based protection depends on monitoring impedance trajectories at feeder relays to detect faults. Two communication-based impedance-based protection schemes were developed. the first scheme utilizes directional elements and pilot signals to locate the fault. The second scheme depends on a Central Protection Unit that communicates with all feeder relays to locate the fault based on directional flags received from feeder relays. The later approach could potentially be adapted to protect networked microgrids and dynamic topology microgrids. Transient-based protection relies on analyzing high frequency transients to detect and locate faults. This approach is very promising but its implementation in the filed faces several challenges. For example, high frequency transients due to faults can be confused with transients due to other events such as capacitor switching. Additionally, while detecting faults by analyzing transients could be doable, locating faults based on analyzing transients is still an open question.« less

Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems.

PubMed

Mekki, Hemza; Benzineb, Omar; Boukhetala, Djamel; Tadjine, Mohamed; Benbouzid, Mohamed

2015-07-01

The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required. This paper proposes an improved faults detection, reconstruction and fault-tolerant control (FTC) scheme for motor systems (MS) with typical faults. For this purpose, a sliding mode controller (SMC) with an integral sliding surface is adopted. This controller can make the output of system to track the desired position reference signal in finite-time and obtain a better dynamic response and anti-disturbance performance. But this controller cannot deal directly with total system failures. However an appropriate combination of the adopted SMC and sliding mode observer (SMO), later it is designed to on-line detect and reconstruct the faults and also to give a sensorless control strategy which can achieve tolerance to a wide class of total additive failures. The closed-loop stability is proved, using the Lyapunov stability theory. Simulation results in healthy and faulty conditions confirm the reliability of the suggested framework. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control for Lead-Wing close formation systems

NASA Astrophysics Data System (ADS)

Liu, Chun; Jiang, Bin; Zhang, Ke

2018-03-01

This paper investigates the attitude and position tracking control problem for Lead-Wing close formation systems in the presence of loss of effectiveness and lock-in-place or hardover failure. In close formation flight, Wing unmanned aerial vehicle movements are influenced by vortex effects of the neighbouring Lead unmanned aerial vehicle. This situation allows modelling of aerodynamic coupling vortex-effects and linearisation based on optimal close formation geometry. Linearised Lead-Wing close formation model is transformed into nominal robust H-infinity models with respect to Mach hold, Heading hold, and Altitude hold autopilots; static feedback H-infinity controller is designed to guarantee effective tracking of attitude and position while manoeuvring Lead unmanned aerial vehicle. Based on H-infinity control design, an integrated multiple-model adaptive fault identification and reconfigurable fault-tolerant control scheme is developed to guarantee asymptotic stability of close-loop systems, error signal boundedness, and attitude and position tracking properties. Simulation results for Lead-Wing close formation systems validate the efficiency of the proposed integrated multiple-model adaptive control algorithm.

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

Fault Management Architectures and the Challenges of Providing Software Assurance

NASA Technical Reports Server (NTRS)

Savarino, Shirley; Fitz, Rhonda; Fesq, Lorraine; Whitman, Gerek

2015-01-01

Fault Management (FM) is focused on safety, the preservation of assets, and maintaining the desired functionality of the system. How FM is implemented varies among missions. Common to most missions is system complexity due to a need to establish a multi-dimensional structure across hardware, software and spacecraft operations. FM is necessary to identify and respond to system faults, mitigate technical risks and ensure operational continuity. Generally, FM architecture, implementation, and software assurance efforts increase with mission complexity. Because FM is a systems engineering discipline with a distributed implementation, providing efficient and effective verification and validation (V&V) is challenging. A breakout session at the 2012 NASA Independent Verification & Validation (IV&V) Annual Workshop titled "V&V of Fault Management: Challenges and Successes" exposed this issue in terms of V&V for a representative set of architectures. NASA's Software Assurance Research Program (SARP) has provided funds to NASA IV&V to extend the work performed at the Workshop session in partnership with NASA's Jet Propulsion Laboratory (JPL). NASA IV&V will extract FM architectures across the IV&V portfolio and evaluate the data set, assess visibility for validation and test, and define software assurance methods that could be applied to the various architectures and designs. This SARP initiative focuses efforts on FM architectures from critical and complex projects within NASA. The identification of particular FM architectures and associated V&V/IV&V techniques provides a data set that can enable improved assurance that a system will adequately detect and respond to adverse conditions. Ultimately, results from this activity will be incorporated into the NASA Fault Management Handbook providing dissemination across NASA, other agencies and the space community. This paper discusses the approach taken to perform the evaluations and preliminary findings from the research.

Fault detection and classification in electrical power transmission system using artificial neural network.

PubMed

Jamil, Majid; Sharma, Sanjeev Kumar; Singh, Rajveer

2015-01-01

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment.

Identification of bearing faults using time domain zero-crossings

NASA Astrophysics Data System (ADS)

William, P. E.; Hoffman, M. W.

2011-11-01

In this paper, zero-crossing characteristic features are employed for early detection and identification of single point bearing defects in rotating machinery. As a result of bearing defects, characteristic defect frequencies appear in the machine vibration signal, normally requiring spectral analysis or envelope analysis to identify the defect type. Zero-crossing features are extracted directly from the time domain vibration signal using only the duration between successive zero-crossing intervals and do not require estimation of the rotational frequency. The features are a time domain representation of the composite vibration signature in the spectral domain. Features are normalized by the length of the observation window and classification is performed using a multilayer feedforward neural network. The model was evaluated on vibration data recorded using an accelerometer mounted on an induction motor housing subjected to a number of single point defects with different severity levels.

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.

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.

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

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.

Automatic bearing fault diagnosis of permanent magnet synchronous generators in wind turbines subjected to noise interference

NASA Astrophysics Data System (ADS)

Guo, Jun; Lu, Siliang; Zhai, Chao; He, Qingbo

2018-02-01

An automatic bearing fault diagnosis method is proposed for permanent magnet synchronous generators (PMSGs), which are widely installed in wind turbines subjected to low rotating speeds, speed fluctuations, and electrical device noise interferences. The mechanical rotating angle curve is first extracted from the phase current of a PMSG by sequentially applying a series of algorithms. The synchronous sampled vibration signal of the fault bearing is then resampled in the angular domain according to the obtained rotating phase information. Considering that the resampled vibration signal is still overwhelmed by heavy background noise, an adaptive stochastic resonance filter is applied to the resampled signal to enhance the fault indicator and facilitate bearing fault identification. Two types of fault bearings with different fault sizes in a PMSG test rig are subjected to experiments to test the effectiveness of the proposed method. The proposed method is fully automated and thus shows potential for convenient, highly efficient and in situ bearing fault diagnosis for wind turbines subjected to harsh environments.

Pattern Recognition Application of Support Vector Machine for Fault Classification of Thyristor Controlled Series Compensated Transmission Lines

NASA Astrophysics Data System (ADS)

Yashvantrai Vyas, Bhargav; Maheshwari, Rudra Prakash; Das, Biswarup

2016-06-01

Application of series compensation in extra high voltage (EHV) transmission line makes the protection job difficult for engineers, due to alteration in system parameters and measurements. The problem amplifies with inclusion of electronically controlled compensation like thyristor controlled series compensation (TCSC) as it produce harmonics and rapid change in system parameters during fault associated with TCSC control. This paper presents a pattern recognition based fault type identification approach with support vector machine. The scheme uses only half cycle post fault data of three phase currents to accomplish the task. The change in current signal features during fault has been considered as discriminatory measure. The developed scheme in this paper is tested over a large set of fault data with variation in system and fault parameters. These fault cases have been generated with PSCAD/EMTDC on a 400 kV, 300 km transmission line model. The developed algorithm has proved better for implementation on TCSC compensated line with its improved accuracy and speed.

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.

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

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.

Experience of automation failures in training: effects on trust, automation bias, complacency and performance.

PubMed

Sauer, Juergen; Chavaillaz, Alain; Wastell, David

2016-06-01

This work examined the effects of operators' exposure to various types of automation failures in training. Forty-five participants were trained for 3.5 h on a simulated process control environment. During training, participants either experienced a fully reliable, automatic fault repair facility (i.e. faults detected and correctly diagnosed), a misdiagnosis-prone one (i.e. faults detected but not correctly diagnosed) or a miss-prone one (i.e. faults not detected). One week after training, participants were tested for 3 h, experiencing two types of automation failures (misdiagnosis, miss). The results showed that automation bias was very high when operators trained on miss-prone automation encountered a failure of the diagnostic system. Operator errors resulting from automation bias were much higher when automation misdiagnosed a fault than when it missed one. Differences in trust levels that were instilled by the different training experiences disappeared during the testing session. Practitioner Summary: The experience of automation failures during training has some consequences. A greater potential for operator errors may be expected when an automatic system failed to diagnose a fault than when it failed to detect one.

Fault detection and isolation of high temperature proton exchange membrane fuel cell stack under the influence of degradation

NASA Astrophysics Data System (ADS)

Jeppesen, Christian; Araya, Samuel Simon; Sahlin, Simon Lennart; Thomas, Sobi; Andreasen, Søren Juhl; Kær, Søren Knudsen

2017-08-01

This study proposes a data-drive impedance-based methodology for fault detection and isolation of low and high cathode stoichiometry, high CO concentration in the anode gas, high methanol vapour concentrations in the anode gas and low anode stoichiometry, for high temperature PEM fuel cells. The fault detection and isolation algorithm is based on an artificial neural network classifier, which uses three extracted features as input. Two of the proposed features are based on angles in the impedance spectrum, and are therefore relative to specific points, and shown to be independent of degradation, contrary to other available feature extraction methods in the literature. The experimental data is based on a 35 day experiment, where 2010 unique electrochemical impedance spectroscopy measurements were recorded. The test of the algorithm resulted in a good detectability of the faults, except for high methanol vapour concentration in the anode gas fault, which was found to be difficult to distinguish from a normal operational data. The achieved accuracy for faults related to CO pollution, anode- and cathode stoichiometry is 100% success rate. Overall global accuracy on the test data is 94.6%.

An Extension to the Kalman Filter for an Improved Detection of Unknown Behavior

NASA Technical Reports Server (NTRS)

Benazera, Emmanuel; Narasimhan, Sriram

2005-01-01

The use of Kalman filter (KF) interferes with fault detection algorithms based on the residual between estimated and measured variables, since the measured values are used to update the estimates. This feedback results in the estimates being pulled closer to the measured values, influencing the residuals in the process. Here we present a fault detection scheme for systems that are being tracked by a KF. Our approach combines an open-loop prediction over an adaptive window and an information-based measure of the deviation of the Kalman estimate from the prediction to improve fault detection.

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.

Radio System for Locating Emergency Workers

NASA Technical Reports Server (NTRS)

Larson, William; Medelius, Pedro; Starr, Stan; Bedette, Guy; Taylor, John; Moerk, Steve

2003-01-01

A system based on low-power radio transponders and associated analog and digital electronic circuitry has been developed for locating firefighters and other emergency workers deployed in a building or other structure. The system has obvious potential for saving lives and reducing the risk of injuries. The system includes (1) a central station equipped with a computer and a transceiver; (2) active radio-frequency (RF) identification tags, each placed in a different room or region of the structure; and (3) transponder units worn by the emergency workers. The RF identification tags can be installed in a new building as built-in components of standard fire-detection devices or ground-fault electrical outlets or can be attached to such devices in a previously constructed building, without need for rewiring the building. Each RF identification tag contains information that uniquely identifies it. When each tag is installed, information on its location and identity are reported to, and stored at, the central station. In an emergency, if a building has not been prewired with RF identification tags, leading emergency workers could drop sequentially numbered portable tags in the rooms of the building, reporting the tag numbers and locations by radio to the central station as they proceed.

LEADER - An integrated engine behavior and design analyses based real-time fault diagnostic expert system for Space Shuttle Main Engine (SSME)

NASA Technical Reports Server (NTRS)

Gupta, U. K.; Ali, M.

1989-01-01

The LEADER expert system has been developed for automatic learning tasks encompassing real-time detection, identification, verification, and correction of anomalous propulsion system operations, using a set of sensors to monitor engine component performance to ascertain anomalies in engine dynamics and behavior. Two diagnostic approaches are embodied in LEADER's architecture: (1) learning and identifying engine behavior patterns to generate novel hypotheses about possible abnormalities, and (2) the direction of engine sensor data processing to perform resoning based on engine design and functional knowledge, as well as the principles of the relevant mechanics and physics.

Protection of Renewable-dominated Microgrids: Challenges and Potential Solutions

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

Elkhatib, Mohamed; Ellis, Abraham; Biswal, Milan

In this report we address the challenge of designing efficient protection system for inverter- dominated microgrids. These microgrids are characterised with limited fault current capacity as a result of current-limiting protection functions of inverters. Typically, inverters limit their fault contribution in sub-cycle time frame to as low as 1.1 per unit. As a result, overcurrent protection could fail completely to detect faults in inverter-dominated microgrids. As part of this project a detailed literature survey of existing and proposed microgrid protection schemes were conducted. The survey concluded that there is a gap in the available microgrid protection methods. The only crediblemore » protection solution available in literature for low- fault inverter-dominated microgrids is the differential protection scheme which represents a robust transmission-grade protection solution but at a very high cost. Two non-overcurrent protection schemes were investigated as part of this project; impedance-based protection and transient-based protection. Impedance-based protection depends on monitoring impedance trajectories at feeder relays to detect faults. Two communication-based impedance-based protection schemes were developed. the first scheme utilizes directional elements and pilot signals to locate the fault. The second scheme depends on a Central Protection Unit that communicates with all feeder relays to locate the fault based on directional flags received from feeder relays. The later approach could potentially be adapted to protect networked microgrids and dynamic topology microgrids. Transient-based protection relies on analyzing high frequency transients to detect and locate faults. This approach is very promising but its implementation in the filed faces several challenges. For example, high frequency transients due to faults can be confused with transients due to other events such as capacitor switching. Additionally, while detecting faults by analyzing transients could be doable, locating faults based on analyzing transients is still an open question.« less

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.

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.

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

NASA Technical Reports Server (NTRS)

Keesler, E. L.

1974-01-01

The operational flight instrumentation required for performance monitoring and fault detection are presented. Measurements by the burn through monitors are presented along with manifold and helium source pressures.

Fault detection and isolation in the challenging Tennessee Eastman process by using image processing techniques.

PubMed

Hajihosseini, Payman; Anzehaee, Mohammad Mousavi; Behnam, Behzad

2018-05-22

The early fault detection and isolation in industrial systems is a critical factor in preventing equipment damage. In the proposed method, instead of using the time signals of sensors, the 2D image obtained by placing these signals next to each other in a matrix has been used; and then a novel fault detection and isolation procedure has been carried out based on image processing techniques. Different features including texture, wavelet transform, mean and standard deviation of the image accompanied with MLP and RBF neural networks based classifiers have been used for this purpose. Obtained results indicate the notable efficacy and success of the proposed method in detecting and isolating faults of the Tennessee Eastman benchmark process and its superiority over previous techniques. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

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.

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.

Characterization of emission microscopy and liquid crystal thermography in IC fault localization

NASA Astrophysics Data System (ADS)

Lau, C. K.; Sim, K. S.

2013-05-01

This paper characterizes two fault localization techniques - Emission Microscopy (EMMI) and Liquid Crystal Thermography (LCT) by using integrated circuit (IC) leakage failures. The majority of today's semiconductor failures do not reveal a clear visual defect on the die surface and therefore require fault localization tools to identify the fault location. Among the various fault localization tools, liquid crystal thermography and frontside emission microscopy are commonly used in most semiconductor failure analysis laboratories. Many people misunderstand that both techniques are the same and both are detecting hot spot in chip failing with short or leakage. As a result, analysts tend to use only LCT since this technique involves very simple test setup compared to EMMI. The omission of EMMI as the alternative technique in fault localization always leads to incomplete analysis when LCT fails to localize any hot spot on a failing chip. Therefore, this research was established to characterize and compare both the techniques in terms of their sensitivity in detecting the fault location in common semiconductor failures. A new method was also proposed as an alternative technique i.e. the backside LCT technique. The research observed that both techniques have successfully detected the defect locations resulted from the leakage failures. LCT wass observed more sensitive than EMMI in the frontside analysis approach. On the other hand, EMMI performed better in the backside analysis approach. LCT was more sensitive in localizing ESD defect location and EMMI was more sensitive in detecting non ESD defect location. Backside LCT was proven to work as effectively as the frontside LCT and was ready to serve as an alternative technique to the backside EMMI. The research confirmed that LCT detects heat generation and EMMI detects photon emission (recombination radiation). The analysis results also suggested that both techniques complementing each other in the IC fault localization. It is necessary for a failure analyst to use both techniques when one of the techniques produces no result.

Seismic reflection images of shallow faulting, northernmost Mississippi embayment, north of the New Madrid seismic zone

USGS Publications Warehouse

McBride, J.H.; Nelson, W.J.

2001-01-01

High-resolution seismic reflection surveys document tectonic faults that displace Pleistocene and older strata just beyond the northeast termination of the New Madrid seismic zone, at the northernmost extent of the Mississippi embayment. These faults, which are part of the Fluorspar Area fault complex in southeastern Illinois, are directly in line with the northeast-trending seismic zone. The reflection data were acquired using an elastic weight-drop source recorded to 500 msec by a 48-geophone array (24-fold) with a 10-ft (??3.0m) station interval. Recognizable reflections were recorded to about 200 msec (100-150 m). The effects of multiple reflections, numerous diffractions, low apparent velocity (i.e., steeply dipping) noise, and the relatively low-frequency content of the recorded signal provided challenges for data processing and interpreting subtle fault offsets. Data processing steps that were critical to the detection of faults included residual statics, post-stack migration, deconvolution, and noise-reduction filtering. Seismic migration was crucial for detecting and mitigating complex fault-related diffraction patterns, which produced an apparent 'folding' of reflectors on unmigrated sections. Detected individual offsets of shallow reflectors range from 5 to 10 m for the top of Paleozoic bedrock and younger strata. The migrated sections generally indicate vertical to steeply dipping normal and reverse faults, which in places outline small horsts and/or grabens. Tilting or folding of stratal reflectors associated with faulting is also locally observed. At one site, the observed faulting is superimposed over a prominent antiformal structure, which may itself be a product of the Quaternary deformation that produced the steep normal and reverse faults. Our results suggest that faulting of the Paleozoic bedrock and younger sediments of the northern Mississippi embayment is more pervasive and less localized than previously thought.

CPSGrader: Auto-Grading and Feedback Generation for Cyber-Physical Systems Education

DTIC Science & Technology

2014-12-21

to refer to ⋃ C∈C+ Ω(C,Γ(p)) (and⋃ C∈C− Ω(C,Γ(p))). The rationale behind this choice of ρ is two-fold: 1. To increase coverage of fault detection ... fault mentioned in Section 2.3. The purpose of the test is to detect that at some time instant t0, the robot bumps into the obstacle, then turns about...sampling. 5.6 Investigating Unknown Faults Using Clustering CPSGrader works with a fixed pre-defined library of faults and associated test benches

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

Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission.

PubMed

Gao, Zheyu; Lin, Jing; Wang, Xiufeng; Xu, Xiaoqiang

2017-05-24

Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

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.

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.

Probabilistic evaluation of on-line checks in fault-tolerant multiprocessor systems

NASA Technical Reports Server (NTRS)

Nair, V. S. S.; Hoskote, Yatin V.; Abraham, Jacob A.

1992-01-01

The analysis of fault-tolerant multiprocessor systems that use concurrent error detection (CED) schemes is much more difficult than the analysis of conventional fault-tolerant architectures. Various analytical techniques have been proposed to evaluate CED schemes deterministically. However, these approaches are based on worst-case assumptions related to the failure of system components. Often, the evaluation results do not reflect the actual fault tolerance capabilities of the system. A probabilistic approach to evaluate the fault detecting and locating capabilities of on-line checks in a system is developed. The various probabilities associated with the checking schemes are identified and used in the framework of the matrix-based model. Based on these probabilistic matrices, estimates for the fault tolerance capabilities of various systems are derived analytically.

Characterization of System Status Signals for Multivariate Time Series Discretization Based on Frequency and Amplitude Variation

PubMed Central

2018-01-01

Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user’s subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study investigates the performance sensitivity of two detection methods, with respect to status signal characteristics of given systems: abrupt variance, characteristic indicator, discernable frequency, and discernable index. Relation between key characteristics indicators from four different real-world systems and the performance of two fault detection methods using pattern recognition are evaluated. PMID:29316731

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

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.

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.

Multiple sensor fault diagnosis for dynamic processes.

PubMed

Li, Cheng-Chih; Jeng, Jyh-Cheng

2010-10-01

Modern industrial plants are usually large scaled and contain a great amount of sensors. Sensor fault diagnosis is crucial and necessary to process safety and optimal operation. This paper proposes a systematic approach to detect, isolate and identify multiple sensor faults for multivariate dynamic systems. The current work first defines deviation vectors for sensor observations, and further defines and derives the basic sensor fault matrix (BSFM), consisting of the normalized basic fault vectors, by several different methods. By projecting a process deviation vector to the space spanned by BSFM, this research uses a vector with the resulted weights on each direction for multiple sensor fault diagnosis. This study also proposes a novel monitoring index and derives corresponding sensor fault detectability. The study also utilizes that vector to isolate and identify multiple sensor faults, and discusses the isolatability and identifiability. Simulation examples and comparison with two conventional PCA-based contribution plots are presented to demonstrate the effectiveness of the proposed methodology. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Aircraft Engine Sensor/Actuator/Component Fault Diagnosis Using a Bank of Kalman Filters

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L. (Technical Monitor)

2003-01-01

In this report, a fault detection and isolation (FDI) system which utilizes a bank of Kalman filters is developed for aircraft engine sensor and actuator FDI in conjunction with the detection of component faults. This FDI approach uses multiple Kalman filters, each of which is designed based on a specific hypothesis 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, from which a specific fault is isolated. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The performance of the FDI system is evaluated against a nonlinear engine simulation for various engine faults at cruise operating conditions. In order to mimic the real engine environment, the nonlinear simulation is executed not only at the nominal, or healthy, condition but also at aged conditions. When the FDI system designed at the healthy condition is applied to an aged engine, the effectiveness of the FDI system is impacted by the mismatch in the engine health condition. Depending on its severity, this mismatch can cause the FDI system to generate incorrect diagnostic results, such as false alarms and missed detections. To partially recover the nominal performance, two approaches, which incorporate information regarding the engine s aging condition in the FDI system, will be discussed and evaluated. The results indicate that the proposed FDI system is promising for reliable diagnostics of aircraft engines.

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.

A New Real - Time Fault Detection Methodology for Systems Under Test. Phase 1

NASA Technical Reports Server (NTRS)

Johnson, Roger W.; Jayaram, Sanjay; Hull, Richard A.

1998-01-01

The purpose of this research is focussed on the identification/demonstration of critical technology innovations that will be applied to various applications viz. Detection of automated machine Health Monitoring (BM, real-time data analysis and control of Systems Under Test (SUT). This new innovation using a High Fidelity Dynamic Model-based Simulation (BFDMS) approach will be used to implement a real-time monitoring, Test and Evaluation (T&E) methodology including the transient behavior of the system under test. The unique element of this process control technique is the use of high fidelity, computer generated dynamic models to replicate the behavior of actual Systems Under Test (SUT). It will provide a dynamic simulation capability that becomes the reference truth model, from which comparisons are made with the actual raw/conditioned data from the test elements.

Use Of Infrared Thermography For The Identification Of Design And Construction Faults In Buildings

NASA Astrophysics Data System (ADS)

Seeber, Stephen A.

1984-03-01

Many design and construction details can affect building energy consumption in unex-pected ways. Further, design and construction errors can increase building energy consumption, result in discomfort to building occupants and cause structural damage to the building. Infrared inspections can easily evaluate the energy efficiency of various aspects of a building's design and identify flaws that might otherwise be detected as a result of occupants' complaints or damage to the building's mechanical or structural systems. Infrared thermography can be used by the architect to evaluate his designs and by the contractor to control the quality of construction. This paper discusses a number of issues that can help determine the effectiveness of infrared building surveys. Following this, three case stud-ies will be presented to illustrate design flaws that were detected through infrared build-ing surveys.

The design and implementation of on-line monitoring system for UHV compact shunt capacitors

NASA Astrophysics Data System (ADS)

Tao, Weiliang; Ni, Xuefeng; Lin, Hao; Jiang, Shengbao

2017-08-01

Because of the large capacity and compact structure of the UHV compact shunt capacitor, it is difficult to take effective measures to detect and prevent the faults. If the fault capacitor fails to take timely maintenance, it will pose a threat to the safe operation of the system and the life safety of the maintenance personnel. The development of UHV compact shunt capacitor on-line monitoring system can detect and record the on-line operation information of UHV compact shunt capacitors, analyze and evaluate the early fault warning signs, find out the fault capacitor or the capacitor with fault symptom, to ensure safe and reliable operation of the system.

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

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.

Runtime Verification in Context : Can Optimizing Error Detection Improve Fault Diagnosis

NASA Technical Reports Server (NTRS)

Dwyer, Matthew B.; Purandare, Rahul; Person, Suzette

2010-01-01

Runtime verification has primarily been developed and evaluated as a means of enriching the software testing process. While many researchers have pointed to its potential applicability in online approaches to software fault tolerance, there has been a dearth of work exploring the details of how that might be accomplished. In this paper, we describe how a component-oriented approach to software health management exposes the connections between program execution, error detection, fault diagnosis, and recovery. We identify both research challenges and opportunities in exploiting those connections. Specifically, we describe how recent approaches to reducing the overhead of runtime monitoring aimed at error detection might be adapted to reduce the overhead and improve the effectiveness of fault diagnosis.

A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

NASA Astrophysics Data System (ADS)

Shao, Haidong; Jiang, Hongkai; Lin, Ying; Li, Xingqiu

2018-03-01

Automatic and accurate identification of rolling bearings fault categories, especially for the fault severities and fault orientations, is still a major challenge in rotating machinery fault diagnosis. In this paper, a novel method called ensemble deep auto-encoders (EDAEs) is proposed for intelligent fault diagnosis of rolling bearings. Firstly, different activation functions are employed as the hidden functions to design a series of auto-encoders (AEs) with different characteristics. Secondly, EDAEs are constructed with various auto-encoders for unsupervised feature learning from the measured vibration signals. Finally, a combination strategy is designed to ensure accurate and stable diagnosis results. The proposed method is applied to analyze the experimental bearing vibration signals. The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.

Health Monitoring Survey of Bell 412EP Transmissions

NASA Technical Reports Server (NTRS)

Tucker, Brian E.; Dempsey, Paula J.

2016-01-01

Health and usage monitoring systems (HUMS) use vibration-based Condition Indicators (CI) to assess the health of helicopter powertrain components. A fault is detected when a CI exceeds its threshold value. The effectiveness of fault detection can be judged on the basis of assessing the condition of actual components from fleet aircraft. The Bell 412 HUMS-equipped helicopter is chosen for such an evaluation. A sample of 20 aircraft included 12 aircraft with confirmed transmission and gearbox faults (detected by CIs) and eight aircraft with no known faults. The associated CI data is classified into "healthy" and "faulted" populations based on actual condition and these populations are compared against their CI thresholds to quantify the probability of false alarm and the probability of missed detection. Receiver Operator Characteristic analysis is used to optimize thresholds. Based on the results of the analysis, shortcomings in the classification method are identified for slow-moving CI trends. Recommendations for improving classification using time-dependent receiver-operator characteristic methods are put forth. Finally, lessons learned regarding OEM-operator communication are presented.

An adaptive confidence limit for periodic non-steady conditions fault detection

NASA Astrophysics Data System (ADS)

Wang, Tianzhen; Wu, Hao; Ni, Mengqi; Zhang, Milu; Dong, Jingjing; Benbouzid, Mohamed El Hachemi; Hu, Xiong

2016-05-01

System monitoring has become a major concern in batch process due to the fact that failure rate in non-steady conditions is much higher than in steady ones. A series of approaches based on PCA have already solved problems such as data dimensionality reduction, multivariable decorrelation, and processing non-changing signal. However, if the data follows non-Gaussian distribution or the variables contain some signal changes, the above approaches are not applicable. To deal with these concerns and to enhance performance in multiperiod data processing, this paper proposes a fault detection method using adaptive confidence limit (ACL) in periodic non-steady conditions. The proposed ACL method achieves four main enhancements: Longitudinal-Standardization could convert non-Gaussian sampling data to Gaussian ones; the multiperiod PCA algorithm could reduce dimensionality, remove correlation, and improve the monitoring accuracy; the adaptive confidence limit could detect faults under non-steady conditions; the fault sections determination procedure could select the appropriate parameter of the adaptive confidence limit. The achieved result analysis clearly shows that the proposed ACL method is superior to other fault detection approaches under periodic non-steady conditions.

Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions

NASA Astrophysics Data System (ADS)

Ruiz-Cárcel, C.; Jaramillo, V. H.; Mba, D.; Ottewill, J. R.; Cao, Y.

2016-01-01

The detection and diagnosis of faults in industrial processes is a very active field of research due to the reduction in maintenance costs achieved by the implementation of process monitoring algorithms such as Principal Component Analysis, Partial Least Squares or more recently Canonical Variate Analysis (CVA). Typically the condition of rotating machinery is monitored separately using vibration analysis or other specific techniques. Conventional vibration-based condition monitoring techniques are based on the tracking of key features observed in the measured signal. Typically steady-state loading conditions are required to ensure consistency between measurements. In this paper, a technique based on merging process and vibration data is proposed with the objective of improving the detection of mechanical faults in industrial systems working under variable operating conditions. The capabilities of CVA for detection and diagnosis of faults were tested using experimental data acquired from a compressor test rig where different process faults were introduced. Results suggest that the combination of process and vibration data can effectively improve the detectability of mechanical faults in systems working under variable operating conditions.

A method based on multi-sensor data fusion for fault detection of planetary gearboxes.

PubMed

Lei, Yaguo; Lin, Jing; He, Zhengjia; Kong, Detong

2012-01-01

Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults.

Optical fiber-fault surveillance for passive optical networks in S-band operation window

NASA Astrophysics Data System (ADS)

Yeh, Chien-Hung; Chi, Sien

2005-07-01

An S-band (1470 to 1520 nm) fiber laser scheme, which uses multiple fiber Bragg grating (FBG) elements as feedback elements on each passive branch, is proposed and described for in-service fault identification in passive optical networks (PONs). By tuning a wavelength selective filter located within the laser cavity over a gain bandwidth, the fiber-fault of each branch can be monitored without affecting the in-service channels. In our experiment, an S-band four-branch monitoring tree-structured PON system is demonstrated and investigated experimentally.

Optical fiber-fault surveillance for passive optical networks in S-band operation window.

PubMed

Yeh, Chien-Hung; Chi, Sien

2005-07-11

An S-band (1470 to 1520 nm) fiber laser scheme, which uses multiple fiber Bragg grating (FBG) elements as feedback elements on each passive branch, is proposed and described for in-service fault identification in passive optical networks (PONs). By tuning a wavelength selective filter located within the laser cavity over a gain bandwidth, the fiber-fault of each branch can be monitored without affecting the in-service channels. In our experiment, an S-band four-branch monitoring tree-structured PON system is demonstrated and investigated experimentally.

Automated fault-management in a simulated spaceflight micro-world

NASA Technical Reports Server (NTRS)

Lorenz, Bernd; Di Nocera, Francesco; Rottger, Stefan; Parasuraman, Raja

2002-01-01

BACKGROUND: As human spaceflight missions extend in duration and distance from Earth, a self-sufficient crew will bear far greater onboard responsibility and authority for mission success. This will increase the need for automated fault management (FM). Human factors issues in the use of such systems include maintenance of cognitive skill, situational awareness (SA), trust in automation, and workload. This study examine the human performance consequences of operator use of intelligent FM support in interaction with an autonomous, space-related, atmospheric control system. METHODS: An expert system representing a model-base reasoning agent supported operators at a low level of automation (LOA) by a computerized fault finding guide, at a medium LOA by an automated diagnosis and recovery advisory, and at a high LOA by automate diagnosis and recovery implementation, subject to operator approval or veto. Ten percent of the experimental trials involved complete failure of FM support. RESULTS: Benefits of automation were reflected in more accurate diagnoses, shorter fault identification time, and reduced subjective operator workload. Unexpectedly, fault identification times deteriorated more at the medium than at the high LOA during automation failure. Analyses of information sampling behavior showed that offloading operators from recovery implementation during reliable automation enabled operators at high LOA to engage in fault assessment activities CONCLUSIONS: The potential threat to SA imposed by high-level automation, in which decision advisories are automatically generated, need not inevitably be counteracted by choosing a lower LOA. Instead, freeing operator cognitive resources by automatic implementation of recover plans at a higher LOA can promote better fault comprehension, so long as the automation interface is designed to support efficient information sampling.

Development and evaluation of a fault-tolerant multiprocessor (FTMP) computer. Volume 1: FTMP principles of operation

NASA Technical Reports Server (NTRS)

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

1983-01-01

The basic organization of the fault tolerant multiprocessor, (FTMP) is that of a general purpose homogeneous multiprocessor. Three processors operate on a shared system (memory and I/O) bus. Replication and tight synchronization of all elements and hardware voting is employed to detect and correct any single fault. Reconfiguration is then employed to repair a fault. Multiple faults may be tolerated as a sequence of single faults with repair between fault occurrences.

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.

Induction motor inter turn fault detection using infrared thermographic analysis

NASA Astrophysics Data System (ADS)

Singh, Gurmeet; Anil Kumar, T. Ch.; Naikan, V. N. A.

2016-07-01

Induction motors are the most commonly used prime movers in industries. These are subjected to various environmental, thermal and load stresses that ultimately reduces the motor efficiency and later leads to failure. Inter turn fault is the second most commonly observed faults in the motors and is considered the most severe. It can lead to the failure of complete phase and can even cause accidents, if left undetected or untreated. This paper proposes an online and non invasive technique that uses infrared thermography, in order to detect the presence of inter turn fault in induction motor drive. Two methods have been proposed that detect the fault and estimate its severity. One method uses transient thermal monitoring during the start of motor and other applies pseudo coloring technique on infrared image of the motor, after it reaches a thermal steady state. The designed template for pseudo-coloring is in acquiescence with the InterNational Electrical Testing Association (NETA) thermographic standard. An index is proposed to assess the severity of the fault present in the motor.

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.

Appropriate IMFs associated with cepstrum and envelope analysis for ball-bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Tsao, Wen-Chang; Pan, Min-Chun

2014-03-01

The traditional envelope analysis is an effective method for the fault detection of rolling bearings. However, all the resonant frequency bands must be examined during the bearing-fault detection process. To handle the above deficiency, this paper proposes using the empirical mode decomposition (EMD) to select a proper intrinsic mode function (IMF) for the subsequent detection tools; here both envelope analysis and cepstrum analysis are employed and compared. By virtue of the band-pass filtering nature of EMD, the resonant frequency bands of structure to be measured are captured in the IMFs. As impulses arising from rolling elements striking bearing faults modulate with structure resonance, proper IMFs potentially enable to characterize fault signatures. In the study, faulty ball bearings are used to justify the proposed method, and comparisons with the traditional envelope analysis are made. Post the use of IMFs highlighting faultybearing features, the performance of using envelope analysis and cepstrum analysis to single out bearing faults is objectively compared and addressed; it is noted that generally envelope analysis offers better performance.

Discrete wavelet transform and energy eigen value for rotor bars fault detection in variable speed field-oriented control of induction motor drive.

PubMed

Ameid, Tarek; Menacer, Arezki; Talhaoui, Hicham; Azzoug, Youness

2018-05-03

This paper presents a methodology for the broken rotor bars fault detection is considered when the rotor speed varies continuously and the induction machine is controlled by Field-Oriented Control (FOC). The rotor fault detection is obtained by analyzing a several mechanical and electrical quantities (i.e., rotor speed, stator phase current and output signal of the speed regulator) by the Discrete Wavelet Transform (DWT) in variable speed drives. The severity of the fault is obtained by stored energy calculation for active power signal. Hence, it can be a useful solution as fault indicator. The FOC is implemented in order to preserve a good performance speed control; to compensate the broken rotor bars effect in the mechanical speed and to ensure the operation continuity and to investigate the fault effect in the variable speed. The effectiveness of the technique is evaluated in simulation and in a real-time implementation by using Matlab/Simulink with the real-time interface (RTI) based on dSpace 1104 board. Copyright © 2018. Published by Elsevier Ltd.

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.

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.

A leakage-free resonance sparse decomposition technique for bearing fault detection in gearboxes

NASA Astrophysics Data System (ADS)

Osman, Shazali; Wang, Wilson

2018-03-01

Most of rotating machinery deficiencies are related to defects in rolling element bearings. Reliable bearing fault detection still remains a challenging task, especially for bearings in gearboxes as bearing-defect-related features are nonstationary and modulated by gear mesh vibration. A new leakage-free resonance sparse decomposition (LRSD) technique is proposed in this paper for early bearing fault detection of gearboxes. In the proposed LRSD technique, a leakage-free filter is suggested to remove strong gear mesh and shaft running signatures. A kurtosis and cosine distance measure is suggested to select appropriate redundancy r and quality factor Q. The signal residual is processed by signal sparse decomposition for highpass and lowpass resonance analysis to extract representative features for bearing fault detection. The effectiveness of the proposed technique is verified by a succession of experimental tests corresponding to different gearbox and bearing conditions.

BEAT: A Web-Based Boolean Expression Fault-Based Test Case Generation Tool

ERIC Educational Resources Information Center

Chen, T. Y.; Grant, D. D.; Lau, M. F.; Ng, S. P.; Vasa, V. R.

2006-01-01

BEAT is a Web-based system that generates fault-based test cases from Boolean expressions. It is based on the integration of our several fault-based test case selection strategies. The generated test cases are considered to be fault-based, because they are aiming at the detection of particular faults. For example, when the Boolean expression is in…

Advanced Information Processing System - Fault detection and error handling

NASA Technical Reports Server (NTRS)

Lala, J. H.

1985-01-01

The Advanced Information Processing System (AIPS) is designed to provide a fault tolerant and damage tolerant data processing architecture for a broad range of aerospace vehicles, including tactical and transport aircraft, and manned and autonomous spacecraft. A proof-of-concept (POC) system is now in the detailed design and fabrication phase. This paper gives an overview of a preliminary fault detection and error handling philosophy in AIPS.

The northwest trending north Boquerón Bay-Punta Montalva Fault Zone; A through going active fault system in southwestern Puerto Rico

USGS Publications Warehouse

Roig‐Silva, Coral Marie; Asencio, Eugenio; Joyce, James

2013-01-01

The North Boquerón Bay–Punta Montalva fault zone has been mapped crossing the Lajas Valley in southwest Puerto Rico. Identification of the fault was based upon detailed analysis of geophysical data, satellite images, and field mapping. The fault zone consists of a series of Cretaceous bedrock faults that reactivated and deformed Miocene limestone and Quaternary alluvial fan sediments. The fault zone is seismically active (local magnitude greater than 5.0) with numerous locally felt earthquakes. Focal mechanism solutions suggest strain partitioning with predominantly east–west left-lateral displacements with small normal faults striking mostly toward the northeast. Northeast-trending fractures and normal faults can be found in intermittent streams that cut through the Quaternary alluvial fan deposits along the southern margin of the Lajas Valley, an east–west-trending 30-km-long fault-controlled depression. Areas of preferred erosion within the alluvial fan trend toward the west-northwest parallel to the onland projection of the North Boquerón Bay fault. The North Boquerón Bay fault aligns with the Punta Montalva fault southeast of the Lajas Valley. Both faults show strong southward tilting of Miocene strata. On the western end, the Northern Boquerón Bay fault is covered with flat-lying Holocene sediments, whereas at the southern end the Punta Montalva fault shows left-lateral displacement of stream drainage on the order of a few hundred meters.

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%.

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.

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.

Infrared thermography based diagnosis of inter-turn fault and cooling system failure in three phase induction motor

NASA Astrophysics Data System (ADS)

Singh, Gurmeet; Naikan, V. N. A.

2017-12-01

Thermography has been widely used as a technique for anomaly detection in induction motors. International Electrical Testing Association (NETA) proposed guidelines for thermographic inspection of electrical systems and rotating equipment. These guidelines help in anomaly detection and estimating its severity. However, it focus only on location of hotspot rather than diagnosing the fault. This paper addresses two such faults i.e. inter-turn fault and failure of cooling system, where both results in increase of stator temperature. Present paper proposes two thermal profile indicators using thermal analysis of IRT images. These indicators are in compliance with NETA standard. These indicators help in correctly diagnosing inter-turn fault and failure of cooling system. The work has been experimentally validated for healthy and with seeded faults scenarios of induction motors.

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.

Preliminary atlas of active shallow tectonic deformation in the Puget Lowland, Washington

USGS Publications Warehouse

Barnett, Elizabeth A.; Haugerud, Ralph A.; Sherrod, Brian L.; Weaver, Craig S.; Pratt, Thomas L.; Blakely, Richard J.

2010-01-01

This atlas presents an up-to-date map compilation of the geological and geophysical observations that underpin interpretations of active, surface-deforming faults in the Puget Lowland, Washington. Shallow lowland faults are mapped where observations of deformation from paleoseismic, seismic-reflection, and potential-field investigations converge. Together, results from these studies strengthen the identification and characterization of regional faults and show that as many as a dozen shallow faults have been active during the Holocene. The suite of maps presented in our atlas identifies sites that have evidence of deformation attributed to these shallow faults. For example, the paleoseismic-investigations map shows where coseismic surface rupture and deformation produced geomorphic scarps and deformed shorelines. Other maps compile results of seismic-reflection and potential-field studies that demonstrate evidence of deformation along suspected fault structures in the subsurface. Summary maps show the fault traces derived from, and draped over, the datasets presented in the preceding maps. Overall, the atlas provides map users with a visual overview of the observations and interpretations that support the existence of active, shallow faults beneath the densely populated Puget Lowland.

49 CFR Appendix D to Part 236 - Independent Review of Verification and Validation

Code of Federal Regulations, 2010 CFR

2010-10-01

... standards. (f) The reviewer shall analyze all Fault Tree Analyses (FTA), Failure Mode and Effects... for each product vulnerability cited by the reviewer; (4) Identification of any documentation or... not properly followed; (6) Identification of the software verification and validation procedures, as...

The buried active faults in southeastern China as revealed by the relocated background seismicity and fault plane solutions

NASA Astrophysics Data System (ADS)

Zhu, A.; Wang, P.; Liu, F.

2017-12-01

The southeastern China in the mainland corresponds to the south China block, which is characterized by moderate historical seismicity and low stain rate. Most faults are buried under thick Quaternary deposits, so it is difficult to detect and locate them using the routine geological methods. Only a few have been identified to be active in late Quaternary, which leads to relatively high potentially seismic risk to this region due to the unexpected locations of the earthquakes. We performed both hypoDD and tomoDD for the background seismicity from 2000 to 2016 to investigate the buried faults. Some buried active faults are revealed by the relocated seismicity and the velocity structure, no geologically known faults corresponding to them and no surface active evidence ever observed. The geometries of the faults are obtained by analyzing the hypocentral distribution pattern and focal mechanism. The focal mechanism solutions indicate that all the revealed faults are dominated in strike-slip mechanisms, or with some thrust components. While the previous fault investigation and detection results show that most of the Quaternary faults in southeastern China are dominated by normal movement. It suggests that there may exist two fault systems in deep and shallow tectonic regimes. The revealed faults may construct the deep one that act as the seismogenic faults, and the normal faults at shallow cannot generate the destructive earthquakes. The variation in the Curie-point depths agrees well with the structure plane of the revealed active faults, suggesting that the faults may have changed the deep structure.

Simultaneous fault detection and control design for switched systems with two quantized signals.

PubMed

Li, Jian; Park, Ju H; Ye, Dan

2017-01-01

The problem of simultaneous fault detection and control design for switched systems with two quantized signals is presented in this paper. Dynamic quantizers are employed, respectively, before the output is passed to fault detector, and before the control input is transmitted to the switched system. Taking the quantized errors into account, the robust performance for this kind of system is given. Furthermore, sufficient conditions for the existence of fault detector/controller are presented in the framework of linear matrix inequalities, and fault detector/controller gains and the supremum of quantizer range are derived by a convex optimized method. Finally, two illustrative examples demonstrate the effectiveness of the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Fuzzy logic based on-line fault detection and classification in transmission line.

PubMed

Adhikari, Shuma; Sinha, Nidul; Dorendrajit, Thingam

2016-01-01

This study presents fuzzy logic based online fault detection and classification of transmission line using Programmable Automation and Control technology based National Instrument Compact Reconfigurable i/o (CRIO) devices. The LabVIEW software combined with CRIO can perform real time data acquisition of transmission line. When fault occurs in the system current waveforms are distorted due to transients and their pattern changes according to the type of fault in the system. The three phase alternating current, zero sequence and positive sequence current data generated by LabVIEW through CRIO-9067 are processed directly for relaying. The result shows that proposed technique is capable of right tripping action and classification of type of fault at high speed therefore can be employed in practical application.

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.

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

Experiments in fault tolerant software reliability

NASA Technical Reports Server (NTRS)

Mcallister, David F.; Vouk, Mladen A.

1989-01-01

Twenty functionally equivalent programs were built and tested in a multiversion software experiment. Following unit testing, all programs were subjected to an extensive system test. In the process sixty-one distinct faults were identified among the versions. Less than 12 percent of the faults exhibited varying degrees of positive correlation. The common-cause (or similar) faults spanned as many as 14 components. However, a majority of these faults were trivial, and easily detected by proper unit and/or system testing. Only two of the seven similar faults were difficult faults, and both were caused by specification ambiguities. One of these faults exhibited variable identical-and-wrong response span, i.e. response span which varied with the testing conditions and input data. Techniques that could have been used to avoid the faults are discussed. For example, it was determined that back-to-back testing of 2-tuples could have been used to eliminate about 90 percent of the faults. In addition, four of the seven similar faults could have been detected by using back-to-back testing of 5-tuples. It is believed that most, if not all, similar faults could have been avoided had the specifications been written using more formal notation, the unit testing phase was subject to more stringent standards and controls, and better tools for measuring the quality and adequacy of the test data (e.g. coverage) were used.

A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the Kurtogram

NASA Astrophysics Data System (ADS)

Tian, Xiange; Xi Gu, James; Rehab, Ibrahim; Abdalla, Gaballa M.; Gu, Fengshou; Ball, A. D.

2018-02-01

Envelope analysis is a widely used method for rolling element bearing fault detection. To obtain high detection accuracy, it is critical to determine an optimal frequency narrowband for the envelope demodulation. However, many of the schemes which are used for the narrowband selection, such as the Kurtogram, can produce poor detection results because they are sensitive to random noise and aperiodic impulses which normally occur in practical applications. To achieve the purposes of denoising and frequency band optimisation, this paper proposes a novel modulation signal bispectrum (MSB) based robust detector for bearing fault detection. Because of its inherent noise suppression capability, the MSB allows effective suppression of both stationary random noise and discrete aperiodic noise. The high magnitude features that result from the use of the MSB also enhance the modulation effects of a bearing fault and can be used to provide optimal frequency bands for fault detection. The Kurtogram is generally accepted as a powerful means of selecting the most appropriate frequency band for envelope analysis, and as such it has been used as the benchmark comparator for performance evaluation in this paper. Both simulated and experimental data analysis results show that the proposed method produces more accurate and robust detection results than Kurtogram based approaches for common bearing faults under a range of representative scenarios.

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.

A single dynamic observer-based module for design of simultaneous fault detection, isolation and tracking control scheme

NASA Astrophysics Data System (ADS)

Davoodi, M.; Meskin, N.; Khorasani, K.

2018-03-01

The problem of simultaneous fault detection, isolation and tracking (SFDIT) control design for linear systems subject to both bounded energy and bounded peak disturbances is considered in this work. A dynamic observer is proposed and implemented by using the H∞/H-/L1 formulation of the SFDIT problem. A single dynamic observer module is designed that generates the residuals as well as the control signals. The objective of the SFDIT module is to ensure that simultaneously the effects of disturbances and control signals on the residual signals are minimised (in order to accomplish the fault detection goal) subject to the constraint that the transfer matrix from the faults to the residuals is equal to a pre-assigned diagonal transfer matrix (in order to accomplish the fault isolation goal), while the effects of disturbances, reference inputs and faults on the specified control outputs are minimised (in order to accomplish the fault-tolerant and tracking control goals). A set of linear matrix inequality (LMI) feasibility conditions are derived to ensure solvability of the problem. In order to illustrate and demonstrate the effectiveness of our proposed design methodology, the developed and proposed schemes are applied to an autonomous unmanned underwater vehicle (AUV).

Iterative generalized time-frequency reassignment for planetary gearbox fault diagnosis under nonstationary conditions

NASA Astrophysics Data System (ADS)

Chen, Xiaowang; Feng, Zhipeng

2016-12-01

Planetary gearboxes are widely used in many sorts of machinery, for its large transmission ratio and high load bearing capacity in a compact structure. Their fault diagnosis relies on effective identification of fault characteristic frequencies. However, in addition to the vibration complexity caused by intricate mechanical kinematics, volatile external conditions result in time-varying running speed and/or load, and therefore nonstationary vibration signals. This usually leads to time-varying complex fault characteristics, and adds difficulty to planetary gearbox fault diagnosis. Time-frequency analysis is an effective approach to extracting the frequency components and their time variation of nonstationary signals. Nevertheless, the commonly used time-frequency analysis methods suffer from poor time-frequency resolution as well as outer and inner interferences, which hinder accurate identification of time-varying fault characteristic frequencies. Although time-frequency reassignment improves the time-frequency readability, it is essentially subject to the constraints of mono-component and symmetric time-frequency distribution about true instantaneous frequency. Hence, it is still susceptible to erroneous energy reallocation or even generates pseudo interferences, particularly for multi-component signals of highly nonlinear instantaneous frequency. In this paper, to overcome the limitations of time-frequency reassignment, we propose an improvement with fine time-frequency resolution and free from interferences for highly nonstationary multi-component signals, by exploiting the merits of iterative generalized demodulation. The signal is firstly decomposed into mono-components of constant frequency by iterative generalized demodulation. Time-frequency reassignment is then applied to each generalized demodulated mono-component, obtaining a fine time-frequency distribution. Finally, the time-frequency distribution of each signal component is restored and superposed to get the time-frequency distribution of original signal. The proposed method is validated using both numerical simulated and lab experimental planetary gearbox vibration signals. The time-varying gear fault symptoms are successfully extracted, showing effectiveness of the proposed iterative generalized time-frequency reassignment method in planetary gearbox fault diagnosis under nonstationary conditions.

Identification of new NE-trending deep-seated faults and tectonic pattern updating in northern Tunisia (Mogodos-Bizerte region), insights from field and seismic reflection data

NASA Astrophysics Data System (ADS)

Essid, El Mabrouk; Kadri, Ali; Inoubli, Mohamed Hedi; Zargouni, Fouad

2016-07-01

The northern Tunisia is occupied by the Tellian domain constituent the eastern end of the Maghrebides, Alpine fold-thrust belt. Study area includes partially the Tellian domain (Mogodos belt) and its foreland (Bizerte region). Most of this region outcrops consist of Numidian thrust sheet flysch attributed to the lower Oligocene-Burdigalian. In the study area, the major fault systems are still subject of discussion. The Numidian nappe structure, the distribution of basalt and Triassic outcrops within and at the front of this Tellian domain deserve more explanation. In this work we intend to update the structural scheme and the tectonic evolution of the northern Tunisia, taking into account salt tectonics and magmatism. The updated tectonic evolution will be integrated in the geodynamic framework of the Central Mediterranean. For this purpose, we have analyzed morphologic, seismic and structural data. The compilation of the results has allowed the identification of new regional NE-trending faults dipping towards the NW: the Bled el Aouana-Bizerte, the Sejnane-Ras Enjla and the Oued el Harka faults. They correspond to the reactivation of deep-seated normal faults splaying on the Triassic evaporites. This fault system constitutes the main component of the northern Tunisia structural scheme and has influenced its tectonic evolution marked by the main following stages. The Tellian thrust-sheets were immobilized at the uppermost Langhian. During the major Tortonian NW-trending compressive phase, these faults were reactivated with reverse kinematics and controlled the distribution of the post-nappes Neogene continental deposits. At the early Pleistocene, a compressive NNW-trending event has reactivated again these faults with sinistral-reverse movements and deformed the post-nappes Neogene series. Late Quaternary to Actual, the tectonic regime continues to be compressive with a NNW-trending maximum horizontal stress.

Localization of thermal anomalies in electrical equipment using Infrared Thermography and support vector machine

NASA Astrophysics Data System (ADS)

Laib dit Leksir, Y.; Mansour, M.; Moussaoui, A.

2018-03-01

Analysis and processing of databases obtained from infrared thermal inspections made on electrical installations require the development of new tools to obtain more information to visual inspections. Consequently, methods based on the capture of thermal images show a great potential and are increasingly employed in this field. However, there is a need for the development of effective techniques to analyse these databases in order to extract significant information relating to the state of the infrastructures. This paper presents a technique explaining how this approach can be implemented and proposes a system that can help to detect faults in thermal images of electrical installations. The proposed method classifies and identifies the region of interest (ROI). The identification is conducted using support vector machine (SVM) algorithm. The aim here is to capture the faults that exist in electrical equipments during an inspection of some machines using A40 FLIR camera. After that, binarization techniques are employed to select the region of interest. Later the comparative analysis of the obtained misclassification errors using the proposed method with Fuzzy c means and Ostu, has also be addressed.

Sparse Reconstruction for Temperature Distribution Using DTS Fiber Optic Sensors with Applications in Electrical Generator Stator Monitoring.

PubMed

Bazzo, João Paulo; Pipa, Daniel Rodrigues; da Silva, Erlon Vagner; Martelli, Cicero; Cardozo da Silva, Jean Carlos

2016-09-07

This paper presents an image reconstruction method to monitor the temperature distribution of electric generator stators. The main objective is to identify insulation failures that may arise as hotspots in the structure. The method is based on temperature readings of fiber optic distributed sensors (DTS) and a sparse reconstruction algorithm. Thermal images of the structure are formed by appropriately combining atoms of a dictionary of hotspots, which was constructed by finite element simulation with a multi-physical model. Due to difficulties for reproducing insulation faults in real stator structure, experimental tests were performed using a prototype similar to the real structure. The results demonstrate the ability of the proposed method to reconstruct images of hotspots with dimensions down to 15 cm, representing a resolution gain of up to six times when compared to the DTS spatial resolution. In addition, satisfactory results were also obtained to detect hotspots with only 5 cm. The application of the proposed algorithm for thermal imaging of generator stators can contribute to the identification of insulation faults in early stages, thereby avoiding catastrophic damage to the structure.

MIRAP, microcomputer reliability analysis program

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

Jehee, J.N.T.

1989-01-01

A program for a microcomputer is outlined that can determine minimal cut sets from a specified fault tree logic. The speed and memory limitations of the microcomputers on which the program is implemented (Atari ST and IBM) are addressed by reducing the fault tree's size and by storing the cut set data on disk. Extensive well proven fault tree restructuring techniques, such as the identification of sibling events and of independent gate events, reduces the fault tree's size but does not alter its logic. New methods are used for the Boolean reduction of the fault tree logic. Special criteria formore » combining events in the 'AND' and 'OR' logic avoid the creation of many subsuming cut sets which all would cancel out due to existing cut sets. Figures and tables illustrates these methods. 4 refs., 5 tabs.« less

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.

Critical fault patterns determination in fault-tolerant computer systems

NASA Technical Reports Server (NTRS)

Mccluskey, E. J.; Losq, J.

1978-01-01

The method proposed tries to enumerate all the critical fault-patterns (successive occurrences of failures) without analyzing every single possible fault. The conditions for the system to be operating in a given mode can be expressed in terms of the static states. Thus, one can find all the system states that correspond to a given critical mode of operation. The next step consists in analyzing the fault-detection mechanisms, the diagnosis algorithm and the process of switch control. From them, one can find all the possible system configurations that can result from a failure occurrence. Thus, one can list all the characteristics, with respect to detection, diagnosis, and switch control, that failures must have to constitute critical fault-patterns. Such an enumeration of the critical fault-patterns can be directly used to evaluate the overall system tolerance to failures. Present research is focused on how to efficiently make use of these system-level characteristics to enumerate all the failures that verify these characteristics.

Fault Diagnosis for Centre Wear Fault of Roll Grinder Based on a Resonance Demodulation Scheme

NASA Astrophysics Data System (ADS)

Wang, Liming; Shao, Yimin; Yin, Lei; Yuan, Yilin; Liu, Jing

2017-05-01

Roll grinder is one of the important parts in the rolling machinery, and the grinding precision of roll surface has direct influence on the surface quality of steel strip. However, during the grinding process, the centre bears the gravity of the roll and alternating stress. Therefore, wear or spalling faults are easily observed on the centre, which will lead to an anomalous vibration of the roll grinder. In this study, a resonance demodulation scheme is proposed to detect the centre wear fault of roll grinder. Firstly, fast kurtogram method is employed to help select the sub-band filter parameters for optimal resonance demodulation. Further, the envelope spectrum are derived based on the filtered signal. Finally, two health indicators are designed to conduct the fault diagnosis for centre wear fault. The proposed scheme is assessed by analysing experimental data from a roll grinder of twenty-high rolling mill. The results show that the proposed scheme can effectively detect the centre wear fault of the roll grinder.

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.

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

Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter

NASA Astrophysics Data System (ADS)

Li, Yifan; Liang, Xihui; Lin, Jianhui; Chen, Yuejian; Liu, Jianxin

2018-02-01

This paper presents a novel signal processing scheme, feature selection based multi-scale morphological filter (MMF), for train axle bearing fault detection. In this scheme, more than 30 feature indicators of vibration signals are calculated for axle bearings with different conditions and the features which can reflect fault characteristics more effectively and representatively are selected using the max-relevance and min-redundancy principle. Then, a filtering scale selection approach for MMF based on feature selection and grey relational analysis is proposed. The feature selection based MMF method is tested on diagnosis of artificially created damages of rolling bearings of railway trains. Experimental results show that the proposed method has a superior performance in extracting fault features of defective train axle bearings. In addition, comparisons are performed with the kurtosis criterion based MMF and the spectral kurtosis criterion based MMF. The proposed feature selection based MMF method outperforms these two methods in detection of train axle bearing faults.

Detection of Frictional Heating on Faults Using Raman Spectra of Carbonaceous Material

NASA Astrophysics Data System (ADS)

Ito, K.; Ujiie, K.; Kagi, H.

2017-12-01

Raman spectra of carbonaceous material (RSCM) have been used as geothermometer in sedimentary and metamorphic rocks. However, it remains poorly understood whether RSCM are useful for detecting past frictional heating on faults. To detect increased heating during seismic slip, we examine the thrust fault in the Jurassic accretionary complex, central Japan. The thrust fault zone includes 10 cm-thick cataclasite and a few mm-thick dark layer. The cataclasite is characterized by fragments of black and gray chert in the black carbonaceous mudstone matrix. The dark layer is marked by intensely cracked gray chert fragments in the dark matrix of carbonaceous mudstone composition, which bounds the fractured gray chert above from the cataclasite below. The RSCM are analyzed for carbonaceous material in the cataclasite, dark layer, and host rock <10 mm from cataclasite and dark layer boundaries. The result indicates that there is no increased carbonization in the cataclasite. In contrast, the dark layer and part of host rocks <2 mm from the dark layer boundaries show prominent increase in carbonization. The absent of increased carbonization in the cataclasite could be attributed to insufficient frictional heating associated with distributed shear and/or faulting at low slip rates. The dark layer exhibits the appearance of fault and injection veins, and the dark layer boundaries are irregularly embayed or intensely cracked; these features have been characteristically observed in pseudotachylytes. Therefore, the increased carbonization in the dark layer is likely resulted from increased heating during earthquake faulting. The intensely cracked fragments in the dark layer and cracked wall rocks may reflect thermal fracturing in chert, which is caused by heat conduction from the molten zone. We suggest that RSCM are useful for the detection of increased heating on faults, particularly when the temperature is high enough for frictional melting and thermal fracturing.

Sensor fault-tolerant control for gear-shifting engaging process of automated manual transmission

NASA Astrophysics Data System (ADS)

Li, Liang; He, Kai; Wang, Xiangyu; Liu, Yahui

2018-01-01

Angular displacement sensor on the actuator of automated manual transmission (AMT) is sensitive to fault, and the sensor fault will disturb its normal control, which affects the entire gear-shifting process of AMT and results in awful riding comfort. In order to solve this problem, this paper proposes a method of fault-tolerant control for AMT gear-shifting engaging process. By using the measured current of actuator motor and angular displacement of actuator, the gear-shifting engaging load torque table is built and updated before the occurrence of the sensor fault. Meanwhile, residual between estimated and measured angular displacements is used to detect the sensor fault. Once the residual exceeds a determined fault threshold, the sensor fault is detected. Then, switch control is triggered, and the current observer and load torque table estimates an actual gear-shifting position to replace the measured one to continue controlling the gear-shifting process. Numerical and experiment tests are carried out to evaluate the reliability and feasibility of proposed methods, and the results show that the performance of estimation and control is satisfactory.

Analytical minimization of synchronicity errors in stochastic identification

NASA Astrophysics Data System (ADS)

Bernal, D.

2018-01-01

An approach to minimize error due to synchronicity faults in stochastic system identification is presented. The scheme is based on shifting the time domain signals so the phases of the fundamental eigenvector estimated from the spectral density are zero. A threshold on the mean of the amplitude-weighted absolute value of these phases, above which signal shifting is deemed justified, is derived and found to be proportional to the first mode damping ratio. It is shown that synchronicity faults do not map precisely to phasor multiplications in subspace identification and that the accuracy of spectral density estimated eigenvectors, for inputs with arbitrary spectral density, decrease with increasing mode number. Selection of a corrective strategy based on signal alignment, instead of eigenvector adjustment using phasors, is shown to be the product of the foregoing observations. Simulations that include noise and non-classical damping suggest that the scheme can provide sufficient accuracy to be of practical value.

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.

Optimal Sensor Allocation for Fault Detection and Isolation

NASA Technical Reports Server (NTRS)

Azam, Mohammad; Pattipati, Krishna; Patterson-Hine, Ann

2004-01-01

Automatic fault diagnostic schemes rely on various types of sensors (e.g., temperature, pressure, vibration, etc) to measure the system parameters. Efficacy of a diagnostic scheme is largely dependent on the amount and quality of information available from these sensors. The reliability of sensors, as well as the weight, volume, power, and cost constraints, often makes it impractical to monitor a large number of system parameters. An optimized sensor allocation that maximizes the fault diagnosibility, subject to specified weight, volume, power, and cost constraints is required. Use of optimal sensor allocation strategies during the design phase can ensure better diagnostics at a reduced cost for a system incorporating a high degree of built-in testing. In this paper, we propose an approach that employs multiple fault diagnosis (MFD) and optimization techniques for optimal sensor placement for fault detection and isolation (FDI) in complex systems. Keywords: sensor allocation, multiple fault diagnosis, Lagrangian relaxation, approximate belief revision, multidimensional knapsack problem.

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

Multi-Agent Diagnosis and Control of an Air Revitalization System for Life Support in Space

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Kowing, Jeffrey; Nieten, Joseph; Graham, Jeffrey s.; Schreckenghost, Debra; Bonasso, Pete; Fleming, Land D.; MacMahon, Matt; Thronesbery, Carroll

2000-01-01

An architecture of interoperating agents has been developed to provide control and fault management for advanced life support systems in space. In this adjustable autonomy architecture, software agents coordinate with human agents and provide support in novel fault management situations. This architecture combines the Livingstone model-based mode identification and reconfiguration (MIR) system with the 3T architecture for autonomous flexible command and control. The MIR software agent performs model-based state identification and diagnosis. MIR identifies novel recovery configurations and the set of commands required for the recovery. The AZT procedural executive and the human operator use the diagnoses and recovery recommendations, and provide command sequencing. User interface extensions have been developed to support human monitoring of both AZT and MIR data and activities. This architecture has been demonstrated performing control and fault management for an oxygen production system for air revitalization in space. The software operates in a dynamic simulation testbed.

Identification of Baribis fault - West Java using second vertical derivative method of gravity

NASA Astrophysics Data System (ADS)

Sari, Endah Puspita; Subakti, Hendri

2015-04-01

Baribis fault is one of West Java fault zones which is an active fault. In modern era, the existence of fault zone can be observed by gravity anomaly. Baribis fault zone has not yet been measured by gravity directly. Based on this reason, satellite data supported this research. Data used on this research are GPS satellite data downloaded from TOPEX. The purpose of this research is to determine the type and strike of Baribis fault. The scope of this research is Baribis fault zone which lies on 6.50o - 7.50o S and 107.50o - 108.80o E. It consists of 5146 points which one point to another is separated by 1 minute meridian. The method used in this research is the Second Vertical Derivative (SVD) of gravity anomaly. The Second Vertical Derivative of gravity anomaly show as the amplitude of gravity anomaly caused by fault structure which appears as residual anomaly. The zero value of residual gravity anomaly indicates that the contact boundary of fault plane. Second Vertical Derivative method of gravity was applied for identifying Baribis fault. The result of this research shows that Baribis fault has a thrust mechanism. It has a lineament strike varies from 107o to 127o. This result agrees with focal mechanism data of earthquakes occurring on this region based on Global CMT catalogue.

On-Line, Gyro-Based, Mass-Property Identification for Thruster-Controlled Spacecraft Using Recursive Least Squares

NASA Technical Reports Server (NTRS)

Wilson, Edward; Lages, Chris; Mah, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

Spacecraft control, state estimation, and fault-detection-and-isolation systems are affected by unknown v aerations in the vehicle mass properties. It is often difficult to accurately measure inertia terms on the ground, and mass properties can change on-orbit as fuel is expended, the configuration changes, or payloads are added or removed. Recursive least squares -based algorithms that use gyro signals to identify the center of mass and inverse inertia matrix are presented. They are applied in simulation to 3 thruster-controlled vehicles: the X-38 and Mini-AERCam under development at NASA-JSC, and the SAM, an air-bearing spacecraft simulator at the NASA-Ames Smart Systems Research Lab (SSRL).

Structural Analysis and Evolution of the Kashan (Qom-Zefreh) Fault, Central Iran

NASA Astrophysics Data System (ADS)

Safaei, H.; Taheri, A.; Vaziri-Moghaddam, H.

The main objectives of this research were to identify the geometry and structure of the Qom-Zefreh fault and to determine the extent of its effects on stratigraphy and facies changes. The identification of movement mechanism of major faults in basement, extent and time of their activities are important effects for evaluation of paleogeography of the Iran plateau. In the Orumieh-Dokhtar volcanic band, there are nearly parallel faults to the Zagros Zone. These faults were formed during closure of the Neothetys and collision of the Arabic plate with crust of Iran. The Qom-Zefreh fault is one of these faults, which is known as having four different trend faults. The result indicates that, this fault is not divided in four segments with different trends but the major trend is of Central section, which is the Kashan segment with AZ140 trend and other segments are just related faults. Thus the name of the Kashan fault is recommended for this fault. The mechanism of the Kashan fault is dextral transpression and other related faults in the region are in good correlation with fractures in a dextral transpression system. The stratigraphic studies conducted on the present formations show the effect of fault movements in Upper Cretaceous sedimentary basin. Lack of noticeable changes in Lower Cretaceous sediments and before that indicates that, the fault system activity has been started from the Upper Cretaceous. Thus, based upon these results, the effect of the Neothetys sea closure in this region could be considered at least from the Upper Cretaceous.

Lessons Learned on Implementing Fault Detection, Isolation, and Recovery (FDIR) in a Ground Launch Environment

NASA Technical Reports Server (NTRS)

Ferell, Bob; Lewis, Mark; Perotti, Jose; Oostdyk, Rebecca; Goerz, Jesse; Brown, Barbara

2010-01-01

This paper's main purpose is to detail issues and lessons learned regarding designing, integrating, and implementing Fault Detection Isolation and Recovery (FDIR) for Constellation Exploration Program (CxP) Ground Operations at Kennedy Space Center (KSC).

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.

Fault finder

DOEpatents

Bunch, Richard H.

1986-01-01

A fault finder for locating faults along a high voltage electrical transmission line. Real time monitoring of background noise and improved filtering of input signals is used to identify the occurrence of a fault. A fault is detected at both a master and remote unit spaced along the line. A master clock synchronizes operation of a similar clock at the remote unit. Both units include modulator and demodulator circuits for transmission of clock signals and data. All data is received at the master unit for processing to determine an accurate fault distance calculation.

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.

Simple Random Sampling-Based Probe Station Selection for Fault Detection in Wireless Sensor Networks

PubMed Central

Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

2011-01-01

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate. PMID:22163789

Simple random sampling-based probe station selection for fault detection in wireless sensor networks.

PubMed

Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

2011-01-01

Fault detection for wireless sensor networks (WSNs) has been studied intensively in recent years. Most existing works statically choose the manager nodes as probe stations and probe the network at a fixed frequency. This straightforward solution leads however to several deficiencies. Firstly, by only assigning the fault detection task to the manager node the whole network is out of balance, and this quickly overloads the already heavily burdened manager node, which in turn ultimately shortens the lifetime of the whole network. Secondly, probing with a fixed frequency often generates too much useless network traffic, which results in a waste of the limited network energy. Thirdly, the traditional algorithm for choosing a probing node is too complicated to be used in energy-critical wireless sensor networks. In this paper, we study the distribution characters of the fault nodes in wireless sensor networks, validate the Pareto principle that a small number of clusters contain most of the faults. We then present a Simple Random Sampling-based algorithm to dynamic choose sensor nodes as probe stations. A dynamic adjusting rule for probing frequency is also proposed to reduce the number of useless probing packets. The simulation experiments demonstrate that the algorithm and adjusting rule we present can effectively prolong the lifetime of a wireless sensor network without decreasing the fault detected rate.

Lembang fault plane identification using electrical resistivity method for disaster mitigation

NASA Astrophysics Data System (ADS)

Maulinadya, S.; Ramadhan, M. Lutfi; N. Wening, F.; Pinehas, D.; Widodo

2017-07-01

Lembang Fault is an active fault lies from West to East located 10 kilometers in north of Bandung. It is a normal fault that its foot wall raises 40-450 meters above the ground. Its location that is not so far from Bandung, which is densely populated and frequently visited by tourists, makes Lembang Fault a threat if it becomes suddenly active. Its movement can cause earthquakes that can result in fatalities. Therefore, act of mitigation is necessary, such as educating people about Lembang Fault and its potential to cause disaster. The objective of this study is to find Lembang Fault plane below the surface with geo electrical mapping method and vertical elect rical sounding method around Ciwarega and The Peak, Lembang (west side of Lembang Fault). Both of these methods are using electricity current to measure rock resistivity. Currents are injected to the ground and potential differences are measured. According to Ohm's Law, resistivity can be calculated so that resistivity distribution can be obtained. In this study, high resistivity contrast is obtained; it is about 1400-5000 Ohm.m. This resistivity contrast can be caused by lateral lithology difference resulted by fault existence. This proves that there is actually a fault in Lembang that potentially cause disasters like earthquakes.

Flight experience with a fail-operational digital fly-by-wire control system

NASA Technical Reports Server (NTRS)

Brown, S. R.; Szalai, K. J.

1977-01-01

The NASA Dryden Flight Research Center is flight testing a triply redundant digital fly-by-wire (DFBW) control system installed in an F-8 aircraft. The full-time, full-authority system performs three-axis flight control computations, including stability and command augmentation, autopilot functions, failure detection and isolation, and self-test functions. Advanced control law experiments include an active flap mode for ride smoothing and maneuver drag reduction. This paper discusses research being conducted on computer synchronization, fault detection, fault isolation, and recovery from transient faults. The F-8 DFBW system has demonstrated immunity from nuisance fault declarations while quickly identifying truly faulty components.

Regional investigations of tectonic and igneous geology, Iran, Pakistan, and Turkey

NASA Technical Reports Server (NTRS)

1978-01-01

The author has identified the following significant results. An extension of the trace of the Chaman-Nushki fault was detected and delineated for 42 km, as was the Ornach-Nal fault for 170 km. Two structural intersections responsible for restricted movements in particular segments of the Chaman-Nushki fault were detected and interpreted. The newest and youngest fault named the Quetta-Mustung-Surab system was delineated for 580 km. The igneous complex of the Lasbela area was interpreted and differentiation was made between ultramafic complex, mafic complex, and basaltic lava flows. One oblong feature was also found which was interpreted as a porphyritic basalt plug.

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.

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.

System and method of detecting cavitation in pumps

DOEpatents

Lu, Bin; Sharma, Santosh Kumar; Yan, Ting; Dimino, Steven A.

2017-10-03

A system and method for detecting cavitation in pumps for fixed and variable supply frequency applications is disclosed. The system includes a controller having a processor programmed to repeatedly receive real-time operating current data from a motor driving a pump, generate a current frequency spectrum from the current data, and analyze current data within a pair of signature frequency bands of the current frequency spectrum. The processor is further programmed to repeatedly determine fault signatures as a function of the current data within the pair of signature frequency bands, repeatedly determine fault indices based on the fault signatures and a dynamic reference signature, compare the fault indices to a reference index, and identify a cavitation condition in a pump based on a comparison between the reference index and a current fault index.

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.

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.

Identification and location of catenary insulator in complex background based on machine vision

NASA Astrophysics Data System (ADS)

Yao, Xiaotong; Pan, Yingli; Liu, Li; Cheng, Xiao

2018-04-01

It is an important premise to locate insulator precisely for fault detection. Current location algorithms for insulator under catenary checking images are not accurate, a target recognition and localization method based on binocular vision combined with SURF features is proposed. First of all, because of the location of the insulator in complex environment, using SURF features to achieve the coarse positioning of target recognition; then Using binocular vision principle to calculate the 3D coordinates of the object which has been coarsely located, realization of target object recognition and fine location; Finally, Finally, the key is to preserve the 3D coordinate of the object's center of mass, transfer to the inspection robot to control the detection position of the robot. Experimental results demonstrate that the proposed method has better recognition efficiency and accuracy, can successfully identify the target and has a define application value.

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.

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.

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.

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.

Multi-phase inversion tectonics related to the Hendijan-Nowrooz-Khafji Fault activity, Zagros Mountains, SW Iran

NASA Astrophysics Data System (ADS)

Kazem Shiroodi, Sadjad; Ghafoori, Mohammad; Faghih, Ali; Ghanadian, Mostafa; Lashkaripour, Gholamreza; Hafezi Moghadas, Naser

2015-11-01

Distinctive characteristics of inverted structures make them important criteria for the identification of certain structural styles of folded belts. The interpretation of 3D seismic reflection and well data sheds new light on the structural evolution and age of inverted structures associated to the Hendijan-Nowrooz-Khafji Fault within the Persian Gulf Basin and northeastern margin of Afro-Arabian plate. Analysis of thickness variations of growth strata using "T-Z plot" (thickness versus throw plot) method revealed the kinematics of the fault. Obtained results show that the fault has experienced a multi-phase evolutionary history over six different extension and compression deformation events (i.e. positive and negative inversion) between 252.2 and 11.62 Ma. This cyclic activity of the growth fault was resulted from alteration of sedimentary processes during continuous fault slip. The structural development of the study area both during positive and negative inversion geometry styles was ultimately controlled by the relative motion between the Afro-Arabian and Central-Iranian plates.

A review of fault tolerant control strategies applied to proton exchange membrane fuel cell systems

NASA Astrophysics Data System (ADS)

Dijoux, Etienne; Steiner, Nadia Yousfi; Benne, Michel; Péra, Marie-Cécile; Pérez, Brigitte Grondin

2017-08-01

Fuel cells are powerful systems for power generation. They have a good efficiency and do not generate greenhouse gases. This technology involves a lot of scientific fields, which leads to the appearance of strongly inter-dependent parameters. This makes the system particularly hard to control and increases fault's occurrence frequency. These two issues call for the necessity to maintain the system performance at the expected level, even in faulty operating conditions. It is called "fault tolerant control" (FTC). The present paper aims to give the state of the art of FTC applied to the proton exchange membrane fuel cell (PEMFC). The FTC approach is composed of two parts. First, a diagnosis part allows the identification and the isolation of a fault; it requires a good a priori knowledge of all the possible faults. Then, a control part allows an optimal control strategy to find the best operating point to recover/mitigate the fault; it requires the knowledge of the degradation phenomena and their mitigation strategies.

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.

Real time automatic detection of bearing fault in induction machine using kurtogram analysis.

PubMed

Tafinine, Farid; Mokrani, Karim

2012-11-01

A proposed signal processing technique for incipient real time bearing fault detection based on kurtogram analysis is presented in this paper. The kurtogram is a fourth-order spectral analysis tool introduced for detecting and characterizing non-stationarities in a signal. This technique starts from investigating the resonance signatures over selected frequency bands to extract the representative features. The traditional spectral analysis is not appropriate for non-stationary vibration signal and for real time diagnosis. The performance of the proposed technique is examined by a series of experimental tests corresponding to different bearing conditions. Test results show that this signal processing technique is an effective bearing fault automatic detection method and gives a good basis for an integrated induction machine condition monitor.

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.

Identification of active fault using analysis of derivatives with vertical second based on gravity anomaly data (Case study: Seulimeum fault in Sumatera fault system)

NASA Astrophysics Data System (ADS)

Hududillah, Teuku Hafid; Simanjuntak, Andrean V. H.; Husni, Muhammad

2017-07-01

Gravity is a non-destructive geophysical technique that has numerous application in engineering and environmental field like locating a fault zone. The purpose of this study is to spot the Seulimeum fault system in Iejue, Aceh Besar (Indonesia) by using a gravity technique and correlate the result with geologic map and conjointly to grasp a trend pattern of fault system. An estimation of subsurface geological structure of Seulimeum fault has been done by using gravity field anomaly data. Gravity anomaly data which used in this study is from Topex that is processed up to Free Air Correction. The step in the Next data processing is applying Bouger correction and Terrin Correction to obtain complete Bouger anomaly that is topographically dependent. Subsurface modeling is done using the Gav2DC for windows software. The result showed a low residual gravity value at a north half compared to south a part of study space that indicated a pattern of fault zone. Gravity residual was successfully correlate with the geologic map that show the existence of the Seulimeum fault in this study space. The study of earthquake records can be used for differentiating the active and non active fault elements, this gives an indication that the delineated fault elements are active.

Optimal Sensor Location Design for Reliable Fault Detection in Presence of False Alarms

PubMed Central

Yang, Fan; Xiao, Deyun; Shah, Sirish L.

2009-01-01

To improve fault detection reliability, sensor location should be designed according to an optimization criterion with constraints imposed by issues of detectability and identifiability. Reliability requires the minimization of undetectability and false alarm probability due to random factors on sensor readings, which is not only related with sensor readings but also affected by fault propagation. This paper introduces the reliability criteria expression based on the missed/false alarm probability of each sensor and system topology or connectivity derived from the directed graph. The algorithm for the optimization problem is presented as a heuristic procedure. Finally, a boiler system is illustrated using the proposed method. PMID:22291524

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.

Fault detection in reciprocating compressor valves under varying load conditions

NASA Astrophysics Data System (ADS)

Pichler, Kurt; Lughofer, Edwin; Pichler, Markus; Buchegger, Thomas; Klement, Erich Peter; Huschenbett, Matthias

2016-03-01

This paper presents a novel approach for detecting cracked or broken reciprocating compressor valves under varying load conditions. The main idea is that the time frequency representation of vibration measurement data will show typical patterns depending on the fault state. The problem is to detect these patterns reliably. For the detection task, we make a detour via the two dimensional autocorrelation. The autocorrelation emphasizes the patterns and reduces noise effects. This makes it easier to define appropriate features. After feature extraction, classification is done using logistic regression and support vector machines. The method's performance is validated by analyzing real world measurement data. The results will show a very high detection accuracy while keeping the false alarm rates at a very low level for different compressor loads, thus achieving a load-independent method. The proposed approach is, to our best knowledge, the first automated method for reciprocating compressor valve fault detection that can handle varying load conditions.

Gear Fault Detection Effectiveness as Applied to Tooth Surface Pitting Fatigue Damage

NASA Technical Reports Server (NTRS)

Lewicki, David G.; Dempsey, Paula J.; Heath, Gregory F.; Shanthakumaran, Perumal

2009-01-01

A study was performed to evaluate fault detection effectiveness as applied to gear tooth pitting fatigue damage. Vibration and oil-debris monitoring (ODM) data were gathered from 24 sets of spur pinion and face gears run during a previous endurance evaluation study. Three common condition indicators (RMS, FM4, and NA4) were deduced from the time-averaged vibration data and used with the ODM to evaluate their performance for gear fault detection. The NA4 parameter showed to be a very good condition indicator for the detection of gear tooth surface pitting failures. The FM4 and RMS parameters performed average to below average in detection of gear tooth surface pitting failures. The ODM sensor was successful in detecting a significant amount of debris from all the gear tooth pitting fatigue failures. Excluding outliers, the average cumulative mass at the end of a test was 40 mg.

DIFFERENTIAL FAULT SENSING CIRCUIT

DOEpatents

Roberts, J.H.

1961-09-01

A differential fault sensing circuit is designed for detecting arcing in high-voltage vacuum tubes arranged in parallel. A circuit is provided which senses differences in voltages appearing between corresponding elements likely to fault. Sensitivity of the circuit is adjusted to some level above which arcing will cause detectable differences in voltage. For particular corresponding elements, a group of pulse transformers are connected in parallel with diodes connected across the secondaries thereof so that only voltage excursions are transmitted to a thyratron which is biased to the sensitivity level mentioned.

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.

Failure Mode Identification Through Clustering Analysis

NASA Technical Reports Server (NTRS)

Arunajadai, Srikesh G.; Stone, Robert B.; Tumer, Irem Y.; Clancy, Daniel (Technical Monitor)

2002-01-01

Research has shown that nearly 80% of the costs and problems are created in product development and that cost and quality are essentially designed into products in the conceptual stage. Currently, failure identification procedures (such as FMEA (Failure Modes and Effects Analysis), FMECA (Failure Modes, Effects and Criticality Analysis) and FTA (Fault Tree Analysis)) and design of experiments are being used for quality control and for the detection of potential failure modes during the detail design stage or post-product launch. Though all of these methods have their own advantages, they do not give information as to what are the predominant failures that a designer should focus on while designing a product. This work uses a functional approach to identify failure modes, which hypothesizes that similarities exist between different failure modes based on the functionality of the product/component. In this paper, a statistical clustering procedure is proposed to retrieve information on the set of predominant failures that a function experiences. The various stages of the methodology are illustrated using a hypothetical design example.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line.

PubMed

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-09-16

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF₂) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach.

Short-Circuit Fault Detection and Classification Using Empirical Wavelet Transform and Local Energy for Electric Transmission Line

PubMed Central

Huang, Nantian; Qi, Jiajin; Li, Fuqing; Yang, Dongfeng; Cai, Guowei; Huang, Guilin; Zheng, Jian; Li, Zhenxin

2017-01-01

In order to improve the classification accuracy of recognizing short-circuit faults in electric transmission lines, a novel detection and diagnosis method based on empirical wavelet transform (EWT) and local energy (LE) is proposed. First, EWT is used to deal with the original short-circuit fault signals from photoelectric voltage transformers, before the amplitude modulated-frequency modulated (AM-FM) mode with a compactly supported Fourier spectrum is extracted. Subsequently, the fault occurrence time is detected according to the modulus maxima of intrinsic mode function (IMF2) from three-phase voltage signals processed by EWT. After this process, the feature vectors are constructed by calculating the LE of the fundamental frequency based on the three-phase voltage signals of one period after the fault occurred. Finally, the classifier based on support vector machine (SVM) which was constructed with the LE feature vectors is used to classify 10 types of short-circuit fault signals. Compared with complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved CEEMDAN methods, the new method using EWT has a better ability to present the frequency in time. The difference in the characteristics of the energy distribution in the time domain between different types of short-circuit faults can be presented by the feature vectors of LE. Together, simulation and real signals experiment demonstrate the validity and effectiveness of the new approach. PMID:28926953

Enhanced Bank of Kalman Filters Developed and Demonstrated for In-Flight Aircraft Engine Sensor Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2005-01-01

In-flight sensor fault detection and isolation (FDI) is critical to maintaining reliable engine operation during flight. The aircraft engine control system, which computes control commands on the basis of sensor measurements, operates the propulsion systems at the demanded conditions. Any undetected sensor faults, therefore, may cause the control system to drive the engine into an undesirable operating condition. It is critical to detect and isolate failed sensors as soon as possible so that such scenarios can be avoided. A challenging issue in developing reliable sensor FDI systems is to make them robust to changes in engine operating characteristics due to degradation with usage and other faults that can occur during flight. A sensor FDI system that cannot appropriately account for such scenarios may result in false alarms, missed detections, or misclassifications when such faults do occur. To address this issue, an enhanced bank of Kalman filters was developed, and its performance and robustness were demonstrated in a simulation environment. The bank of filters is composed of m + 1 Kalman filters, where m is the number of sensors being used by the control system and, thus, in need of monitoring. Each Kalman filter is designed on the basis of a unique fault hypothesis so that it will be able to maintain its performance if a particular fault scenario, hypothesized by that particular filter, takes place.

Implanted component faults and their effects on gas turbine engine performance

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

MacLeod, J.D.; Taylor, V.; Laflamme, J.C.G.

Under the sponsorship of the Canadian Department of National Defence, the Engine Laboratory of the National Research Council of Canada (NRCC) has established a program for the evaluation of component deterioration on gas turbine engine performance. The effect is aimed at investigating the effects of typical in-service faults on the performance characteristics of each individual engine component. The objective of the program is the development of a generalized fault library, which will be used with fault identification techniques in the field, to reduce unscheduled maintenance. To evaluate the effects of implanted faults on the performance of a single spool engine,more » such as an Allison T56 turboprop engine, a series of faulted parts were installed. For this paper the following faults were analyzed: (a) first-stage turbine nozzle erosion damage; (b) first-stage turbine rotor blade untwist; (c) compressor seal wear; (d) first and second-stage compressor blade tip clearance increase. This paper describes the project objectives, the experimental installation, and the results of the fault implantation on engine performance. Discussed are performance variations on both engine and component characteristics. As the performance changes were significant, a rigorous measurement uncertainty analysis is included.« less

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.

Identification of Lembang fault, West-Java Indonesia by using controlled source audio-magnetotelluric (CSAMT)

NASA Astrophysics Data System (ADS)

Sanny, Teuku A.

2017-07-01

The objective of this study is to determine boundary and how to know surrounding area between Lembang Fault and Cimandiri fault. For the detailed study we used three methodologies: (1). Surface deformation modeling by using Boundary Element method and (2) Controlled Source Audiomagneto Telluric (CSAMT). Based on the study by using surface deformation by using Boundary Element Methods (BEM), the direction Lembang fault has a dominant displacement in east direction. The eastward displacement at the nothern fault block is smaller than the eastward displacement at the southern fault block which indicates that each fault block move in left direction relative to each other. From this study we know that Lembang fault in this area has left lateral strike slip component. The western part of the Lembang fault move in west direction different from the eastern part that moves in east direction. Stress distribution map of Lembang fault shows difference between the eastern and western segments of Lembang fault. Displacement distribution map along x-direction and y-direction of Lembang fault shows a linement oriented in northeast-southwest direction right on Tangkuban Perahu Mountain. Displacement pattern of Cimandiri fault indicates that the Cimandiri fault is devided into two segment. Eastern segment has left lateral strike slip component while the western segment has right lateral strike slip component. Based on the displacement distribution map along y-direction, a linement oriented in northwest-southeast direction is observed at the western segment of the Cimandiri fault. The displacement along x-direction and y-direction between the Lembang and Cimandiri fault is nearly equal to zero indicating that the Lembang fault and Cimandiri Fault are not connected to each others. Based on refraction seismic tomography that we know the characteristic of Cimandiri fault as normal fault. Based on CSAMT method th e lembang fault is normal fault that different of dip which formed as graben structure.

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.

Automated visual inspection of brake shoe wear

NASA Astrophysics Data System (ADS)

Lu, Shengfang; Liu, Zhen; Nan, Guo; Zhang, Guangjun

2015-10-01

With the rapid development of high-speed railway, the automated fault inspection is necessary to ensure train's operation safety. Visual technology is paid more attention in trouble detection and maintenance. For a linear CCD camera, Image alignment is the first step in fault detection. To increase the speed of image processing, an improved scale invariant feature transform (SIFT) method is presented. The image is divided into multiple levels of different resolution. Then, we do not stop to extract the feature from the lowest resolution to the highest level until we get sufficient SIFT key points. At that level, the image is registered and aligned quickly. In the stage of inspection, we devote our efforts to finding the trouble of brake shoe, which is one of the key components in brake system on electrical multiple units train (EMU). Its pre-warning on wear limitation is very important in fault detection. In this paper, we propose an automatic inspection approach to detect the fault of brake shoe. Firstly, we use multi-resolution pyramid template matching technology to fast locate the brake shoe. Then, we employ Hough transform to detect the circles of bolts in brake region. Due to the rigid characteristic of structure, we can identify whether the brake shoe has a fault. The experiments demonstrate that the way we propose has a good performance, and can meet the need of practical applications.

Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

PubMed Central

Lee, Jonguk; Choi, Heesu; Park, Daihee; Chung, Yongwha; Kim, Hee-Young; Yoon, Sukhan

2016-01-01

Railway point devices act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Point failure can significantly affect railway operations, with potentially disastrous consequences. Therefore, early detection of anomalies is critical for monitoring and managing the condition of rail infrastructure. We present a data mining solution that utilizes audio data to efficiently detect and diagnose faults in railway condition monitoring systems. The system enables extracting mel-frequency cepstrum coefficients (MFCCs) from audio data with reduced feature dimensions using attribute subset selection, and employs support vector machines (SVMs) for early detection and classification of anomalies. Experimental results show that the system enables cost-effective detection and diagnosis of faults using a cheap microphone, with accuracy exceeding 94.1% whether used alone or in combination with other known methods. PMID:27092509

Earthquakes: Risk, Detection, Warning, and Research

DTIC Science & Technology

2010-01-14

which affect taller , multi-story buildings. Ground motion that affects shorter buildings of a few stories, called short-period seismic waves, is...places in a single fault, or jump between connected faults. Earthquakes that occur along the Sierra Madre fault in southern California, for example

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

A Method to Simultaneously Detect the Current Sensor Fault and Estimate the State of Energy for Batteries in Electric Vehicles

PubMed Central

Xu, Jun; Wang, Jing; Li, Shiying; Cao, Binggang

2016-01-01

Recently, State of energy (SOE) has become one of the most fundamental parameters for battery management systems in electric vehicles. However, current information is critical in SOE estimation and current sensor is usually utilized to obtain the latest current information. However, if the current sensor fails, the SOE estimation may be confronted with large error. Therefore, this paper attempts to make the following contributions: Current sensor fault detection and SOE estimation method is realized simultaneously. Through using the proportional integral observer (PIO) based method, the current sensor fault could be accurately estimated. By taking advantage of the accurate estimated current sensor fault, the influence caused by the current sensor fault can be eliminated and compensated. As a result, the results of the SOE estimation will be influenced little by the fault. In addition, the simulation and experimental workbench is established to verify the proposed method. The results indicate that the current sensor fault can be estimated accurately. Simultaneously, the SOE can also be estimated accurately and the estimation error is influenced little by the fault. The maximum SOE estimation error is less than 2%, even though the large current error caused by the current sensor fault still exists. PMID:27548183