Applications of Fault Detection in Vibrating Structures

NASA Technical Reports Server (NTRS)

Eure, Kenneth W.; Hogge, Edward; Quach, Cuong C.; Vazquez, Sixto L.; Russell, Andrew; Hill, Boyd L.

2012-01-01

Structural fault detection and identification remains an area of active research. Solutions to fault detection and identification may be based on subtle changes in the time series history of vibration signals originating from various sensor locations throughout the structure. The purpose of this paper is to document the application of vibration based fault detection methods applied to several structures. Overall, this paper demonstrates the utility of vibration based methods for fault detection in a controlled laboratory setting and limitations of applying the same methods to a similar structure during flight on an experimental subscale aircraft.

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.

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.

Robust Fault Detection and Isolation for Stochastic Systems

NASA Technical Reports Server (NTRS)

George, Jemin; Gregory, Irene M.

2010-01-01

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

Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution

PubMed Central

Jia, Feng; Lei, Yaguo; Shan, Hongkai; Lin, Jing

2015-01-01

The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings. PMID:26610501

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.

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.

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.

Dynamic Fault Detection Chassis

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

Mize, Jeffery J

2007-01-01

Abstract The high frequency switching megawatt-class High Voltage Converter Modulator (HVCM) developed by Los Alamos National Laboratory for the Oak Ridge National Laboratory's Spallation Neutron Source (SNS) is now in operation. One of the major problems with the modulator systems is shoot-thru conditions that can occur in a IGBTs H-bridge topology resulting in large fault currents and device failure in a few microseconds. The Dynamic Fault Detection Chassis (DFDC) is a fault monitoring system; it monitors transformer flux saturation using a window comparator and dV/dt events on the cathode voltage caused by any abnormality such as capacitor breakdown, transformer primarymore » turns shorts, or dielectric breakdown between the transformer primary and secondary. If faults are detected, the DFDC will inhibit the IGBT gate drives and shut the system down, significantly reducing the possibility of a shoot-thru condition or other equipment damaging events. In this paper, we will present system integration considerations, performance characteristics of the DFDC, and discuss its ability to significantly reduce costly down time for the entire facility.« less

Fault detection and isolation for complex system

NASA Astrophysics Data System (ADS)

Jing, Chan Shi; Bayuaji, Luhur; Samad, R.; Mustafa, M.; Abdullah, N. R. H.; Zain, Z. M.; Pebrianti, Dwi

2017-07-01

Fault Detection and Isolation (FDI) is a method to monitor, identify, and pinpoint the type and location of system fault in a complex multiple input multiple output (MIMO) non-linear system. A two wheel robot is used as a complex system in this study. The aim of the research is to construct and design a Fault Detection and Isolation algorithm. The proposed method for the fault identification is using hybrid technique that combines Kalman filter and Artificial Neural Network (ANN). The Kalman filter is able to recognize the data from the sensors of the system and indicate the fault of the system in the sensor reading. Error prediction is based on the fault magnitude and the time occurrence of fault. Additionally, Artificial Neural Network (ANN) is another algorithm used to determine the type of fault and isolate the fault in the system.

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.

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.

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.

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.

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.

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

NASA Technical Reports Server (NTRS)

Joshi, Suresh M.

2012-01-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2015-09-01

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

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.

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.

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.

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.

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.

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.

Gyro-based Maximum-Likelihood Thruster Fault Detection and Identification

NASA Technical Reports Server (NTRS)

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

2002-01-01

When building smaller, less expensive spacecraft, there is a need for intelligent fault tolerance vs. increased hardware redundancy. If fault tolerance can be achieved using existing navigation sensors, cost and vehicle complexity can be reduced. A maximum likelihood-based approach to thruster fault detection and identification (FDI) for spacecraft is developed here and applied in simulation to the X-38 space vehicle. The system uses only gyro signals to detect and identify hard, abrupt, single and multiple jet on- and off-failures. Faults are detected within one second and identified within one to five accords,

Reset Tree-Based Optical Fault Detection

PubMed Central

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

2013-01-01

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

Fault 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

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).

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.

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

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.

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

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.

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.

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.

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

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.

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.

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.

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.

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.

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.

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.

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

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

Weak characteristic information extraction from early fault of wind turbine generator gearbox

NASA Astrophysics Data System (ADS)

Xu, Xiaoli; Liu, Xiuli

2017-09-01

Given the weak early degradation characteristic information during early fault evolution in gearbox of wind turbine generator, traditional singular value decomposition (SVD)-based denoising may result in loss of useful information. A weak characteristic information extraction based on μ-SVD and local mean decomposition (LMD) is developed to address this problem. The basic principle of the method is as follows: Determine the denoising order based on cumulative contribution rate, perform signal reconstruction, extract and subject the noisy part of signal to LMD and μ-SVD denoising, and obtain denoised signal through superposition. Experimental results show that this method can significantly weaken signal noise, effectively extract the weak characteristic information of early fault, and facilitate the early fault warning and dynamic predictive maintenance.

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.

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.

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.

Early Tertiary transtension-related deformation and magmatism along the Tintina fault system, Alaska

USGS Publications Warehouse

Till, A.B.; Roeske, S.M.; Bradley, D.C.; Friedman, R.; Layer, P.W.

2007-01-01

Transtensional deformation was concentrated in a zone adjacent to the Tintina strike-slip fault system in Alaska during the early Tertiary. The deformation occurred along the Victoria Creek fault, the trace of the Tintina system that connects it with the Kaltag fault; together the Tintina and Kaltag fault systems girdle Alaska from east to west. Over an area of ???25 by 70 km between the Victoria Creek and Tozitna faults, bimodal volcanics erupted; lacustrine and fluvial rocks were deposited; plutons were emplaced and deformed; and metamorphic rocks cooled, all at about the same time. Plutonic and volcanic rocks in this zone yield U-Pb zircon ages of ca. 60 Ma; 40Ar/ 39Ar cooling ages from those plutons and adjacent metamorphic rocks are also ca. 60 Ma. Although early Tertiary magmatism occurred over a broad area in central Alaska, meta- morphism and ductile deformation accompanied that magmatism in this one zone only. Within the zone of deformation, pluton aureoles and metamorphic rocks display consistent NE-SW-stretching lineations parallel to the Victoria Creek fault, suggesting that deformation processes involved subhorizontal elongation of the package. The most deeply buried metamorphic rocks, kyanite-bearing metapelites, occur as lenses adjacent to the fault, which cuts the crust to the Moho (Beaudoin et al., 1997). Geochronologic data and field relationships suggest that the amount of early Tertiary exhumation was greatest adjacent to the Victoria Creek fault. The early Tertiary crustal-scale events that may have operated to produce transtension in this area are (1) increased heat flux and related bimodal within-plate magmatism, (2) movement on a releasing stepover within the Tintina fault system or on a regional scale involving both the Tintina and the Kobuk fault systems, and (3) oroclinal bending of the Tintina-Kaltag fault system with counterclockwise rotation of western Alaska. ?? 2007 The Geological Society of America. All rights reserved.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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.

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.

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

Fault Detection and Correction for the Solar Dynamics Observatory Attitude Control System

NASA Technical Reports Server (NTRS)

Starin, Scott R.; Vess, Melissa F.; Kenney, Thomas M.; Maldonado, Manuel D.; Morgenstern, Wendy M.

2007-01-01

The Solar Dynamics Observatory is an Explorer-class mission that will launch in early 2009. The spacecraft will operate in a geosynchronous orbit, sending data 24 hours a day to a devoted ground station in White Sands, New Mexico. It will carry a suite of instruments designed to observe the Sun in multiple wavelengths at unprecedented resolution. The Atmospheric Imaging Assembly includes four telescopes with focal plane CCDs that can image the full solar disk in four different visible wavelengths. The Extreme-ultraviolet Variability Experiment will collect time-correlated data on the activity of the Sun's corona. The Helioseismic and Magnetic Imager will enable study of pressure waves moving through the body of the Sun. The attitude control system on Solar Dynamics Observatory is responsible for four main phases of activity. The physical safety of the spacecraft after separation must be guaranteed. Fine attitude determination and control must be sufficient for instrument calibration maneuvers. The mission science mode requires 2-arcsecond control according to error signals provided by guide telescopes on the Atmospheric Imaging Assembly, one of the three instruments to be carried. Lastly, accurate execution of linear and angular momentum changes to the spacecraft must be provided for momentum management and orbit maintenance. In thsp aper, single-fault tolerant fault detection and correction of the Solar Dynamics Observatory attitude control system is described. The attitude control hardware suite for the mission is catalogued, with special attention to redundancy at the hardware level. Four reaction wheels are used where any three are satisfactory. Four pairs of redundant thrusters are employed for orbit change maneuvers and momentum management. Three two-axis gyroscopes provide full redundancy for rate sensing. A digital Sun sensor and two autonomous star trackers provide two-out-of-three redundancy for fine attitude determination. The use of software to maximize

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.

Randomness fault detection system

NASA Technical Reports Server (NTRS)

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

1996-01-01

A method and apparatus are provided for detecting a fault on a power line carrying a line parameter such as a load current. The apparatus monitors and analyzes the load current to obtain an energy value. The energy value is compared to a threshold value stored in a buffer. If the energy value is greater than the threshold value a counter is incremented. If the energy value is greater than a high value threshold or less than a low value threshold then a second counter is incremented. If the difference between two subsequent energy values is greater than a constant then a third counter is incremented. A fault signal is issued if the counter is greater than a counter limit value and either the second counter is greater than a second limit value or the third counter is greater than a third limit value.

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

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.

Weak fault detection and health degradation monitoring using customized standard multiwavelets

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Automatic Channel Fault Detection on a Small Animal APD-Based Digital PET Scanner

NASA Astrophysics Data System (ADS)

Charest, Jonathan; Beaudoin, Jean-François; Cadorette, Jules; Lecomte, Roger; Brunet, Charles-Antoine; Fontaine, Réjean

2014-10-01

Avalanche photodiode (APD) based positron emission tomography (PET) scanners show enhanced imaging capabilities in terms of spatial resolution and contrast due to the one to one coupling and size of individual crystal-APD detectors. However, to ensure the maximal performance, these PET scanners require proper calibration by qualified scanner operators, which can become a cumbersome task because of the huge number of channels they are made of. An intelligent system (IS) intends to alleviate this workload by enabling a diagnosis of the observational errors of the scanner. The IS can be broken down into four hierarchical blocks: parameter extraction, channel fault detection, prioritization and diagnosis. One of the main activities of the IS consists in analyzing available channel data such as: normalization coincidence counts and single count rates, crystal identification classification data, energy histograms, APD bias and noise thresholds to establish the channel health status that will be used to detect channel faults. This paper focuses on the first two blocks of the IS: parameter extraction and channel fault detection. The purpose of the parameter extraction block is to process available data on individual channels into parameters that are subsequently used by the fault detection block to generate the channel health status. To ensure extensibility, the channel fault detection block is divided into indicators representing different aspects of PET scanner performance: sensitivity, timing, crystal identification and energy. Some experiments on a 8 cm axial length LabPET scanner located at the Sherbrooke Molecular Imaging Center demonstrated an erroneous channel fault detection rate of 10% (with a 95% confidence interval (CI) of [9, 11]) which is considered tolerable. Globally, the IS achieves a channel fault detection efficiency of 96% (CI: [95, 97]), which proves that many faults can be detected automatically. Increased fault detection efficiency would be

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.

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.

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.

A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays.

PubMed

Medina-García, Jonathan; Sánchez-Rodríguez, Trinidad; Galán, Juan Antonio Gómez; Delgado, Aránzazu; Gómez-Bravo, Fernando; Jiménez, Raúl

2017-02-25

This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system.

A Wireless Sensor System for Real-Time Monitoring and Fault Detection of Motor Arrays

PubMed Central

Medina-García, Jonathan; Sánchez-Rodríguez, Trinidad; Galán, Juan Antonio Gómez; Delgado, Aránzazu; Gómez-Bravo, Fernando; Jiménez, Raúl

2017-01-01

This paper presents a wireless fault detection system for industrial motors that combines vibration, motor current and temperature analysis, thus improving the detection of mechanical faults. The design also considers the time of detection and further possible actions, which are also important for the early detection of possible malfunctions, and thus for avoiding irreversible damage to the motor. The remote motor condition monitoring is implemented through a wireless sensor network (WSN) based on the IEEE 802.15.4 standard. The deployed network uses the beacon-enabled mode to synchronize several sensor nodes with the coordinator node, and the guaranteed time slot mechanism provides data monitoring with a predetermined latency. A graphic user interface offers remote access to motor conditions and real-time monitoring of several parameters. The developed wireless sensor node exhibits very low power consumption since it has been optimized both in terms of hardware and software. The result is a low cost, highly reliable and compact design, achieving a high degree of autonomy of more than two years with just one 3.3 V/2600 mAh battery. Laboratory and field tests confirm the feasibility of the wireless system. PMID:28245623

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

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.

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.

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 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.

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.

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.

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.

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.

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

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

Cheung, Howard; Braun, James E.

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

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

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

Cheung, Howard; Braun, James E.

2015-12-31

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

Detection 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.

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.

Incipient fault detection study for advanced spacecraft systems

NASA Technical Reports Server (NTRS)

Milner, G. Martin; Black, Michael C.; Hovenga, J. Mike; Mcclure, Paul F.

1986-01-01

A feasibility study to investigate the application of vibration monitoring to the rotating machinery of planned NASA advanced spacecraft components is described. Factors investigated include: (1) special problems associated with small, high RPM machines; (2) application across multiple component types; (3) microgravity; (4) multiple fault types; (5) eight different analysis techniques including signature analysis, high frequency demodulation, cepstrum, clustering, amplitude analysis, and pattern recognition are compared; and (6) small sample statistical analysis is used to compare performance by computation of probability of detection and false alarm for an ensemble of repeated baseline and faulted tests. Both detection and classification performance are quantified. Vibration monitoring is shown to be an effective means of detecting the most important problem types for small, high RPM fans and pumps typical of those planned for the advanced spacecraft. A preliminary monitoring system design and implementation plan is presented.

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.

Slowing extrusion tectonics: Lowered estimate of post-Early Miocene slip rate for the Altyn Tagh fault

USGS Publications Warehouse

Yue, Y.; Ritts, B.D.; Graham, S.A.; Wooden, J.L.; Gehrels, G.E.; Zhang, Z.

2004-01-01

Determination of long-term slip rate for the Altyn Tagh fault is essential for testing whether Asian tectonics is dominated by lateral extrusion or distributed crustal shortening. Previous slip-history studies focused on either Quaternary slip-rate measurements or pre-Early Miocene total-offset estimates and do not allow a clear distinction between rates based on the two. The magmatic and metamorphic history revealed by SHRIMP zircon dating of clasts from Miocene conglomerate in the Xorkol basin north of the Altyn Tagh fault strikingly matches that of basement in the southern Qilian Shan and northern Qaidam regions south of the fault. This match requires that the post-Early Miocene long-term slip rate along the Altyn Tagh fault cannot exceed 10 mm/year, supporting the hypothesis of distributed crustal thickening for post-Early Miocene times. This low long-term slip rate and recently documented large pre-Early Miocene cumulative offset across the fault support a two-stage evolution, wherein Asian tectonics was dominated by lateral extrusion before the end of Early Miocene, and since then has been dominated by distributed crustal thickening and rapid plateau uplift. ?? 2003 Elsevier B.V. All rights reserved.

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

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.

EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis

NASA Astrophysics Data System (ADS)

Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

2016-05-01

A novel multivariate and multiscale statistical process monitoring method is proposed with the aim of detecting incipient failures in large slewing bearings, where subjective influence plays a minor role. The proposed method integrates the strengths of the Independent Component Analysis (ICA) multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD), which adaptively decomposes signals into different time scales and can thus cope with multiscale system dynamics. The method, which was named EEMD-based multiscale ICA (EEMD-MSICA), not only enables bearing fault detection but also offers a mechanism of multivariate signal denoising and, in combination with the Envelope Analysis (EA), a diagnostic tool. The multiscale nature of the proposed approach makes the method convenient to cope with data which emanate from bearings in complex real-world rotating machinery and frequently represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane. The efficiency of the proposed method was tested on simulated as well as real vibration and Acoustic Emission (AE) signals obtained through conducting an accelerated run-to-failure lifetime experiment on a purpose-built laboratory slewing bearing test stand. The ability to detect and locate the early-stage rolling-sliding contact fatigue failure of the bearing indicates that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSICA method is able to effectively extract it, thereby representing a reliable bearing fault detection and diagnosis strategy.

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.

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

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.

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

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

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

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

Signal injection as a fault detection technique.

PubMed

Cusidó, Jordi; Romeral, Luis; Ortega, Juan Antonio; Garcia, Antoni; Riba, Jordi

2011-01-01

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.

Signal Injection as a Fault Detection Technique

PubMed Central

Cusidó, Jordi; Romeral, Luis; Ortega, Juan Antonio; Garcia, Antoni; Riba, Jordi

2011-01-01

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies. PMID:22163801

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.

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.

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.

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.

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.

All row, planar fault detection system

DOEpatents

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

2013-07-23

An apparatus, program product and method for detecting nodal faults may simultaneously cause designated nodes of a cell to communicate with all nodes adjacent to each of the designated nodes. Furthermore, all nodes along the axes of the designated nodes are made to communicate with their adjacent nodes, and the communications are analyzed to determine if a node or connection is faulty.

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.

Gear-box fault detection using time-frequency based methods

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

Odgaard, Peter Fogh; Stoustrup, Jakob

2015-01-01

Gear-box fault monitoring and detection is important for optimization of power generation and availability of wind turbines. The current industrial approach is to use condition monitoring systems, which runs in parallel with the wind turbine control system, using expensive additional sensors. An alternative would be to use the existing measurements which are normally available for the wind turbine control system. The usage of these sensors instead would cut down the cost of the wind turbine by not using additional sensors. One of these available measurements is the generator speed, in which changes in the gear-box resonance frequency can be detected.more » Two different time-frequency based approaches are presented in this paper. One is a filter based approach and the other is based on a Karhunen-Loeve basis. Both of them detects the gear-box fault with an acceptable detection delay.« less

Fault detection and diagnosis of photovoltaic systems

NASA Astrophysics Data System (ADS)

Wu, Xing

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

Composite Bending Box Section Modal Vibration Fault Detection

NASA Technical Reports Server (NTRS)

Werlink, Rudy

2002-01-01

One of the primary concerns with Composite construction in critical structures such as wings and stabilizers is that hidden faults and cracks can develop operationally. In the real world, catastrophic sudden failure can result from these undetected faults in composite structures. Vibration data incorporating a broad frequency modal approach, could detect significant changes prior to failure. The purpose of this report is to investigate the usefulness of frequency mode testing before and after bending and torsion loading on a composite bending Box Test section. This test article is representative of construction techniques being developed for the recent NASA Blended Wing Body Low Speed Vehicle Project. The Box section represents the construction technique on the proposed blended wing aircraft. Modal testing using an impact hammer provides an frequency fingerprint before and after bending and torsional loading. If a significant structural discontinuity develops, the vibration response is expected to change. The limitations of the data will be evaluated for future use as a non-destructive in-situ method of assessing hidden damage in similarly constructed composite wing assemblies. Modal vibration fault detection sensitivity to band-width, location and axis will be investigated. Do the sensor accelerometers need to be near the fault and or in the same axis? The response data used in this report was recorded at 17 locations using tri-axial accelerometers. The modal tests were conducted following 5 independent loading conditions before load to failure and 2 following load to failure over a period of 6 weeks. Redundant data was used to minimize effects from uncontrolled variables which could lead to incorrect interpretations. It will be shown that vibrational modes detected failure at many locations when skin de-bonding failures occurred near the center section. Important considerations are the axis selected and frequency range.

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.

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

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

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.

Shallow Faulting in Morelia, Mexico, Based on Seismic Tomography and Geodetically Detected Land Subsidence

NASA Astrophysics Data System (ADS)

Cabral-Cano, E.; Arciniega-Ceballos, A.; Vergara-Huerta, F.; Chaussard, E.; Wdowinski, S.; DeMets, C.; Salazar-Tlaczani, L.

2013-12-01

Subsidence has been a common occurrence in several cities in central Mexico for the past three decades. This process causes substantial damage to the urban infrastructure and housing in several cities and it is a major factor to be considered when planning urban development, land-use zoning and hazard mitigation strategies. Since the early 1980's the city of Morelia in Central Mexico has experienced subsidence associated with groundwater extraction in excess of natural recharge from rainfall. Previous works have focused on the detection and temporal evolution of the subsidence spatial distribution. The most recent InSAR analysis confirms the permanence of previously detected rapidly subsiding areas such as the Rio Grande Meander area and also defines 2 subsidence patches previously undetected in the newly developed suburban sectors west of Morelia at the Fraccionamiento Del Bosque along, south of Hwy. 15 and another patch located north of Morelia along Gabino Castañeda del Rio Ave. Because subsidence-induced, shallow faulting develops at high horizontal strain localization, newly developed a subsidence areas are particularly prone to faulting and fissuring. Shallow faulting increases groundwater vulnerability because it disrupts discharge hydraulic infrastructure and creates a direct path for transport of surface pollutants into the underlying aquifer. Other sectors in Morelia that have been experiencing subsidence for longer time have already developed well defined faults such as La Colina, Central Camionera, Torremolinos and La Paloma faults. Local construction codes in the vicinity of these faults define a very narrow swath along which housing construction is not allowed. In order to better characterize these fault systems and provide better criteria for future municipal construction codes we have surveyed the La Colina and Torremolinos fault systems in the western sector of Morelia using seismic tomographic techniques. Our results indicate that La Colina Fault

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.

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

NASA Astrophysics Data System (ADS)

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

2011-04-01

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

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

Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

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.

Fault-tolerant quantum error detection.

PubMed

Linke, Norbert M; Gutierrez, Mauricio; Landsman, Kevin A; Figgatt, Caroline; Debnath, Shantanu; Brown, Kenneth R; Monroe, Christopher

2017-10-01

Quantum computers will eventually reach a size at which quantum error correction becomes imperative. Quantum information can be protected from qubit imperfections and flawed control operations by encoding a single logical qubit in multiple physical qubits. This redundancy allows the extraction of error syndromes and the subsequent detection or correction of errors without destroying the logical state itself through direct measurement. We show the encoding and syndrome measurement of a fault-tolerantly prepared logical qubit via an error detection protocol on four physical qubits, represented by trapped atomic ions. This demonstrates the robustness of a logical qubit to imperfections in the very operations used to encode it. The advantage persists in the face of large added error rates and experimental calibration errors.

Fault-tolerant quantum error detection

PubMed Central

Linke, Norbert M.; Gutierrez, Mauricio; Landsman, Kevin A.; Figgatt, Caroline; Debnath, Shantanu; Brown, Kenneth R.; Monroe, Christopher

2017-01-01

Quantum computers will eventually reach a size at which quantum error correction becomes imperative. Quantum information can be protected from qubit imperfections and flawed control operations by encoding a single logical qubit in multiple physical qubits. This redundancy allows the extraction of error syndromes and the subsequent detection or correction of errors without destroying the logical state itself through direct measurement. We show the encoding and syndrome measurement of a fault-tolerantly prepared logical qubit via an error detection protocol on four physical qubits, represented by trapped atomic ions. This demonstrates the robustness of a logical qubit to imperfections in the very operations used to encode it. The advantage persists in the face of large added error rates and experimental calibration errors. PMID:29062889

Diagnostics Tools Identify Faults Prior to Failure

NASA Technical Reports Server (NTRS)

2013-01-01

Through the SBIR program, Rochester, New York-based Impact Technologies LLC collaborated with Ames Research Center to commercialize the Center s Hybrid Diagnostic Engine, or HyDE, software. The fault detecting program is now incorporated into a software suite that identifies potential faults early in the design phase of systems ranging from printers to vehicles and robots, saving time and money.

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.

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

Methodology for fault detection in induction motors via sound and vibration signals

NASA Astrophysics Data System (ADS)

Delgado-Arredondo, Paulo Antonio; Morinigo-Sotelo, Daniel; Osornio-Rios, Roque Alfredo; Avina-Cervantes, Juan Gabriel; Rostro-Gonzalez, Horacio; Romero-Troncoso, Rene de Jesus

2017-01-01

Nowadays, timely maintenance of electric motors is vital to keep up the complex processes of industrial production. There are currently a variety of methodologies for fault diagnosis. Usually, the diagnosis is performed by analyzing current signals at a steady-state motor operation or during a start-up transient. This method is known as motor current signature analysis, which identifies frequencies associated with faults in the frequency domain or by the time-frequency decomposition of the current signals. Fault identification may also be possible by analyzing acoustic sound and vibration signals, which is useful because sometimes this information is the only available. The contribution of this work is a methodology for detecting faults in induction motors in steady-state operation based on the analysis of acoustic sound and vibration signals. This proposed approach uses the Complete Ensemble Empirical Mode Decomposition for decomposing the signal into several intrinsic mode functions. Subsequently, the frequency marginal of the Gabor representation is calculated to obtain the spectral content of the IMF in the frequency domain. This proposal provides good fault detectability results compared to other published works in addition to the identification of more frequencies associated with the faults. The faults diagnosed in this work are two broken rotor bars, mechanical unbalance and bearing defects.

Temperature and composition of carbonate cements record early structural control on cementation in a nascent deformation band fault zone: Moab Fault, Utah, USA

NASA Astrophysics Data System (ADS)

Hodson, Keith R.; Crider, Juliet G.; Huntington, Katharine W.

2016-10-01

Fluid-driven cementation and diagenesis within fault zones can influence host rock permeability and rheology, affecting subsequent fluid migration and rock strength. However, there are few constraints on the feedbacks between diagenetic conditions and structural deformation. We investigate the cementation history of a fault-intersection zone on the Moab Fault, a well-studied fault system within the exhumed reservoir rocks of the Paradox Basin, Utah, USA. The fault zone hosts brittle structures recording different stages of deformation, including joints and two types of deformation bands. Using stable isotopes of carbon and oxygen, clumped isotope thermometry, and cathodoluminescence, we identify distinct source fluid compositions for the carbonate cements within the fault damage zone. Each source fluid is associated with different carbonate precipitation temperatures, luminescence characteristics, and styles of structural deformation. Luminescent carbonates appear to be derived from meteoric waters mixing with an organic-rich or magmatic carbon source. These cements have warm precipitation temperatures and are closely associated with jointing, capitalizing on increases in permeability associated with fracturing during faulting and subsequent exhumation. Earlier-formed non-luminescent carbonates have source fluid compositions similar to marine waters, low precipitation temperatures, and are closely associated with deformation bands. The deformation bands formed at shallow depths very early in the burial history, preconditioning the rock for fracturing and associated increases in permeability. Carbonate clumped isotope temperatures allow us to associate structural and diagenetic features with burial history, revealing that structural controls on fluid distribution are established early in the evolution of the host rock and fault zone, before the onset of major displacement.

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.

Detection of High-impedance Arcing Faults in Radial Distribution DC Systems

NASA Technical Reports Server (NTRS)

Gonzalez, Marcelo C.; Button, Robert M.

2003-01-01

High voltage, low current arcing faults in DC power systems have been researched at the NASA Glenn Research Center in order to develop a method for detecting these 'hidden faults', in-situ, before damage to cables and components from localized heating can occur. A simple arc generator was built and high-speed and low-speed monitoring of the voltage and current waveforms, respectively, has shown that these high impedance faults produce a significant increase in high frequency content in the DC bus voltage and low frequency content in the DC system current. Based on these observations, an algorithm was developed using a high-speed data acquisition system that was able to accurately detect high impedance arcing events induced in a single-line system based on the frequency content of the DC bus voltage or the system current. Next, a multi-line, radial distribution system was researched to see if the arc location could be determined through the voltage information when multiple 'detectors' are present in the system. It was shown that a small, passive LC filter was sufficient to reliably isolate the fault to a single line in a multi-line distribution system. Of course, no modification is necessary if only the current information is used to locate the arc. However, data shows that it might be necessary to monitor both the system current and bus voltage to improve the chances of detecting and locating high impedance arcing faults

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.

Detecting Taiwan's Shanchiao Active Fault Using AMT and Gravity Methods

NASA Astrophysics Data System (ADS)

Liu, H.-C.; Yang, C.-H.

2009-04-01

Taiwan's Shanchiao normal fault runs in a northeast-southwest direction and is located on the western edge of the Taipei Basin in northern Taiwan. The overburden of the fault is late Quaternary sediment with a thickness of approximately a few tenth of a meter to several hundred meters. No detailed studies of the western side of the Shanchiao fault are available. As Taiwan is located on the Neotectonic Belt in the western Pacific, detecting active faults near the Taipei metropolitan area will provide necessary information for further disaster prevention. It is the responsibility of geologists and geophysicists in Taiwan to perform this task. Examination of the resistivity and density contrasts of subsurface layers permits a mapping of the Shanchiao fault and the deformed Tertiary strata of the Taipei Basin. The audio-frequency magnetotelluric (AMT) method and gravity method were chosen for this study. Significant resistivity and gravity anomalies were observed in the suspected fault zone. The interpretation reveals a good correlation between the features of the Shanchiao fault and resistivity and density distribution at depth. In this observation, AMT and gravity methods provides a viable means for mapping the Shanchiao fault position and studying its features associated with the subsidence of the western side of the Taipei Basin. This study indicates the AMT and gravity methods' considerable potential for accurately mapping an active fault.

Automated Monitoring with a BSP Fault-Detection Test

NASA Technical Reports Server (NTRS)

Bickford, Randall L.; Herzog, James P.

2003-01-01

The figure schematically illustrates a method and procedure for automated monitoring of an asset, as well as a hardware- and-software system that implements the method and procedure. As used here, asset could signify an industrial process, power plant, medical instrument, aircraft, or any of a variety of other systems that generate electronic signals (e.g., sensor outputs). In automated monitoring, the signals are digitized and then processed in order to detect faults and otherwise monitor operational status and integrity of the monitored asset. The major distinguishing feature of the present method is that the fault-detection function is implemented by use of a Bayesian sequential probability (BSP) technique. This technique is superior to other techniques for automated monitoring because it affords sensitivity, not only to disturbances in the mean values, but also to very subtle changes in the statistical characteristics (variance, skewness, and bias) of the monitored signals.

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.

Controls on Early-Rift Geometry: New Perspectives From the Bilila-Mtakataka Fault, Malawi

NASA Astrophysics Data System (ADS)

Hodge, M.; Fagereng, Å.; Biggs, J.; Mdala, H.

2018-05-01

We use the ˜110-km long Bilila-Mtakataka fault in the amagmatic southern East African Rift, Malawi, to investigate the controls on early-rift geometry at the scale of a major border fault. Morphological variations along the 14 ± 8-m high scarp define six 10- to 40-km long segments, which are either foliation parallel or oblique to both foliation and the current regional extension direction. As the scarp is neither consistently parallel to foliation nor well oriented for the current regional extension direction, we suggest that the segmented surface expression is related to the local reactivation of well-oriented weak shallow fabrics above a broadly continuous structure at depth. Using a geometrical model, the geometry of the best fitting subsurface structure is consistent with the local strain field from recent seismicity. In conclusion, within this early-rift, preexisting weaknesses only locally control border fault geometry at subsurface.

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.

Fault detection and isolation in motion monitoring system.

PubMed

Kim, Duk-Jin; Suk, Myoung Hoon; Prabhakaran, B

2012-01-01

Pervasive computing becomes very active research field these days. A watch that can trace human movement to record motion boundary as well as to study of finding social life pattern by one's localized visiting area. Pervasive computing also helps patient monitoring. A daily monitoring system helps longitudinal study of patient monitoring such as Alzheimer's and Parkinson's or obesity monitoring. Due to the nature of monitoring sensor (on-body wireless sensor), however, signal noise or faulty sensors errors can be present at any time. Many research works have addressed these problems any with a large amount of sensor deployment. In this paper, we present the faulty sensor detection and isolation using only two on-body sensors. We have been investigating three different types of sensor errors: the SHORT error, the CONSTANT error, and the NOISY SENSOR error (see more details on section V). Our experimental results show that the success rate of isolating faulty signals are an average of over 91.5% on fault type 1, over 92% on fault type 2, and over 99% on fault type 3 with the fault prior of 30% sensor errors.

Application of fault detection techniques to spiral bevel gear fatigue data

NASA Technical Reports Server (NTRS)

Zakrajsek, James J.; Handschuh, Robert F.; Decker, Harry J.

1994-01-01

Results of applying a variety of gear fault detection techniques to experimental data is presented. A spiral bevel gear fatigue rig was used to initiate a naturally occurring fault and propagate the fault to a near catastrophic condition of the test gear pair. The spiral bevel gear fatigue test lasted a total of eighteen hours. At approximately five and a half hours into the test, the rig was stopped to inspect the gears for damage, at which time a small pit was identified on a tooth of the pinion. The test was then stopped an additional seven times throughout the rest of the test in order to observe and document the growth and propagation of the fault. The test was ended when a major portion of a pinion tooth broke off. A personal computer based diagnostic system was developed to obtain vibration data from the test rig, and to perform the on-line gear condition monitoring. A number of gear fault detection techniques, which use the signal average in both the time and frequency domain, were applied to the experimental data. Among the techniques investigated, two of the recently developed methods appeared to be the first to react to the start of tooth damage. These methods continued to react to the damage as the pitted area grew in size to cover approximately 75% of the face width of the pinion tooth. In addition, information gathered from one of the newer methods was found to be a good accumulative damage indicator. An unexpected result of the test showed that although the speed of the rig was held to within a band of six percent of the nominal speed, and the load within eighteen percent of nominal, the resulting speed and load variations substantially affected the performance of all of the gear fault detection techniques investigated.

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.

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.

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.

Fault detection and diagnosis using neural network approaches

NASA Technical Reports Server (NTRS)

Kramer, Mark A.

1992-01-01

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

Arc fault detection system

DOEpatents

Jha, K.N.

1999-05-18

An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard. 1 fig.

Arc fault detection system

DOEpatents

Jha, Kamal N.

1999-01-01

An arc fault detection system for use on ungrounded or high-resistance-grounded power distribution systems is provided which can be retrofitted outside electrical switchboard circuits having limited space constraints. The system includes a differential current relay that senses a current differential between current flowing from secondary windings located in a current transformer coupled to a power supply side of a switchboard, and a total current induced in secondary windings coupled to a load side of the switchboard. When such a current differential is experienced, a current travels through a operating coil of the differential current relay, which in turn opens an upstream circuit breaker located between the switchboard and a power supply to remove the supply of power to the switchboard.

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.

Failure Detecting Method of Fault Current Limiter System with Rectifier

NASA Astrophysics Data System (ADS)

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

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

Implementing a C++ Version of the Joint Seismic-Geodetic Algorithm for Finite-Fault Detection and Slip Inversion for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Smith, D. E.; Felizardo, C.; Minson, S. E.; Boese, M.; Langbein, J. O.; Guillemot, C.; Murray, J. R.

2015-12-01

The earthquake early warning (EEW) systems in California and elsewhere can greatly benefit from algorithms that generate estimates of finite-fault parameters. These estimates could significantly improve real-time shaking calculations and yield important information for immediate disaster response. Minson et al. (2015) determined that combining FinDer's seismic-based algorithm (Böse et al., 2012) with BEFORES' geodetic-based algorithm (Minson et al., 2014) yields a more robust and informative joint solution than using either algorithm alone. FinDer examines the distribution of peak ground accelerations from seismic stations and determines the best finite-fault extent and strike from template matching. BEFORES employs a Bayesian framework to search for the best slip inversion over all possible fault geometries in terms of strike and dip. Using FinDer and BEFORES together generates estimates of finite-fault extent, strike, dip, preferred slip, and magnitude. To yield the quickest, most flexible, and open-source version of the joint algorithm, we translated BEFORES and FinDer from Matlab into C++. We are now developing a C++ Application Protocol Interface for these two algorithms to be connected to the seismic and geodetic data flowing from the EEW system. The interface that is being developed will also enable communication between the two algorithms to generate the joint solution of finite-fault parameters. Once this interface is developed and implemented, the next step will be to run test seismic and geodetic data through the system via the Earthworm module, Tank Player. This will allow us to examine algorithm performance on simulated data and past real events.

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.

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

Fault detection and multiclassifier fusion for unmanned aerial vehicles (UAVs)

NASA Astrophysics Data System (ADS)

Yan, Weizhong

2001-03-01

UAVs demand more accurate fault accommodation for their mission manager and vehicle control system in order to achieve a reliability level that is comparable to that of a pilot aircraft. This paper attempts to apply multi-classifier fusion techniques to achieve the necessary performance of the fault detection function for the Lockheed Martin Skunk Works (LMSW) UAV Mission Manager. Three different classifiers that meet the design requirements of the fault detection of the UAAV are employed. The binary decision outputs from the classifiers are then aggregated using three different classifier fusion schemes, namely, majority vote, weighted majority vote, and Naieve Bayes combination. All of the three schemes are simple and need no retraining. The three fusion schemes (except the majority vote that gives an average performance of the three classifiers) show the classification performance that is better than or equal to that of the best individual. The unavoidable correlation between the classifiers with binary outputs is observed in this study. We conclude that it is the correlation between the classifiers that limits the fusion schemes to achieve an even better performance.

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.

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.

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

Fault detection and bypass in a sequence information signal processor

NASA Technical Reports Server (NTRS)

Peterson, John C. (Inventor); Chow, Edward T. (Inventor)

1992-01-01

The invention comprises a plurality of scan registers, each such register respectively associated with a processor element; an on-chip comparator, encoder and fault bypass register. Each scan register generates a unitary signal the logic state of which depends on the correctness of the input from the previous processor in the systolic array. These unitary signals are input to a common comparator which generates an output indicating whether or not an error has occurred. These unitary signals are also input to an encoder which identifies the location of any fault detected so that an appropriate multiplexer can be switched to bypass the faulty processor element. Input scan data can be readily programmed to fully exercise all of the processor elements so that no fault can remain undetected.

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

Fault detection for hydraulic pump based on chaotic parallel RBF network

NASA Astrophysics Data System (ADS)

Lu, Chen; Ma, Ning; Wang, Zhipeng

2011-12-01

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

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.

Pressure Monitoring to Detect Fault Rupture Due to CO 2 Injection

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

Keating, Elizabeth; Dempsey, David; Pawar, Rajesh

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

Pressure Monitoring to Detect Fault Rupture Due to CO 2 Injection

DOE PAGES

Keating, Elizabeth; Dempsey, David; Pawar, Rajesh

2017-08-18

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

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.

Multiple tests for wind turbine fault detection and score fusion using two- level multidimensional scaling (MDS)

NASA Astrophysics Data System (ADS)

Ye, Xiang; Gao, Weihua; Yan, Yanjun; Osadciw, Lisa A.

2010-04-01

Wind is an important renewable energy source. The energy and economic return from building wind farms justify the expensive investments in doing so. However, without an effective monitoring system, underperforming or faulty turbines will cause a huge loss in revenue. Early detection of such failures help prevent these undesired working conditions. We develop three tests on power curve, rotor speed curve, pitch angle curve of individual turbine. In each test, multiple states are defined to distinguish different working conditions, including complete shut-downs, under-performing states, abnormally frequent default states, as well as normal working states. These three tests are combined to reach a final conclusion, which is more effective than any single test. Through extensive data mining of historical data and verification from farm operators, some state combinations are discovered to be strong indicators of spindle failures, lightning strikes, anemometer faults, etc, for fault detection. In each individual test, and in the score fusion of these tests, we apply multidimensional scaling (MDS) to reduce the high dimensional feature space into a 3-dimensional visualization, from which it is easier to discover turbine working information. This approach gains a qualitative understanding of turbine performance status to detect faults, and also provides explanations on what has happened for detailed diagnostics. The state-of-the-art SCADA (Supervisory Control And Data Acquisition) system in industry can only answer the question whether there are abnormal working states, and our evaluation of multiple states in multiple tests is also promising for diagnostics. In the future, these tests can be readily incorporated in a Bayesian network for intelligent analysis and decision support.

Sensor fault detection and isolation system for a condensation process.

PubMed

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

2016-11-01

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

Early Proterozoic activity on Archean faults in the western Superior province - evidence from pseudotachylite

USGS Publications Warehouse

Peterman, Z.E.; Day, W.

1989-01-01

Major transcurrent faults in the Superior province developed in the Late Archean at the close of the Kenoran orogeny. Reactivation of some of these faults late in the Early Proterozoic is indicated by Rb-Sr analyses of pseudotachylite from the Rainy Lake-Seine River and Quetico faults in the Rainy Lake region of Minnesota and Ontario. Fault veins of pseudotachylite and immediately adjacent country rock at two localities yielded subparallel isochrons that are pooled for an age of 1947??23 Ma. K-Ar and Rb-Sr biotite ages register earlier regional cooling of the terrane at about 2500 Ma with no evidence of younger thermal overprinting at temperatures exceeding 300??C. Accordingly, the 1947??23 Ma age is interpreted as dating the formation of the pseudotachylite. Reactivation of existing faults at this time was caused by stresses transmitted from margins of the Superior province where compressional tectonic events were occurring. -Authors

Fault Detection and Diagnosis System for the Air-conditioning

NASA Astrophysics Data System (ADS)

Nakahara, Nobuo

The fault detection and diagnosis system, the FDD system, for the HVAC was initiated around the middle of 1970s in Japan but it still remains at the elementary stage. The HVAC is really one of the most complicated and large scaled system for the FDD system. Besides, the maintenance engineering was never focussed as the target of the academic study since after the war, but the FDD system for some kinds of the components and subsystems has been developed for the sake of the practical industrial needs. Recently, international cooperative study in the IEA Annex 25 on the energy conservation for the building and community targetted on the BOFD, the building optimization, fault detection and diagnosis. Not a few academic peaple from various engineering field got interested and, moreover, some national projects seem to start in the European countries. The author has reviewed the state of the art of the FDD and BO as well based on the references and the experience at the IEA study.

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.

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.

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.

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.

Tectono-stratigraphic evolution of normal fault zones: Thal Fault Zone, Suez Rift, Egypt

NASA Astrophysics Data System (ADS)

Leppard, Christopher William

The evolution of linkage of normal fault populations to form continuous, basin bounding normal fault zones is recognised as an important control on the stratigraphic evolution of rift-basins. This project aims to investigate the temporal and spatial evolution of normal fault populations and associated syn-rift deposits from the initiation of early-formed, isolated normal faults (rift-initiation) to the development of a through-going fault zone (rift-climax) by documenting the tectono-stratigraphic evolution of the Sarbut EI Gamal segment of the exceptionally well-exposed Thai fault zone, Suez Rift, Egypt. A number of dated stratal surfaces mapped around the syn-rift depocentre of the Sarbut El Gamal segment allow constraints to be placed on the timing and style of deformation, and the spatial variability of facies along this segment of the fault zone. Data collected indicates that during the first 3.5 My of rifting the structural style was characterised by numerous, closely spaced, short (< 3 km), low displacement (< 200 m) synthetic and antithetic normal faults within 1 - 2 km of the present-day fault segment trace, accommodating surface deformation associated with the development of a fault propagation monocline above the buried, pre-cursor strands of the Sarbut El Gamal fault segment. The progressive localisation of displacement onto the fault segment during rift-climax resulted in the development of a major, surface-breaking fault 3.5 - 5 My after the onset of rifting and is recorded by the death of early-formed synthetic and antithetic faults up-section, and thickening of syn-rift strata towards the fault segment. The influence of intrabasinal highs at the tips of the Sarbut EI Gamal fault segment on the pre-rift sub-crop level, combined with observations from the early-formed structures and coeval deposits suggest that the overall length of the fault segment was fixed from an early stage. The fault segment is interpreted to have grown through rapid lateral

Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis.

PubMed

Zhang, Hanyuan; Tian, Xuemin; Deng, Xiaogang; Cao, Yuping

2018-05-16

As an attractive nonlinear dynamic data analysis tool, global preserving kernel slow feature analysis (GKSFA) has achieved great success in extracting the high nonlinearity and inherently time-varying dynamics of batch process. However, GKSFA is an unsupervised feature extraction method and lacks the ability to utilize batch process class label information, which may not offer the most effective means for dealing with batch process monitoring. To overcome this problem, we propose a novel batch process monitoring method based on the modified GKSFA, referred to as discriminant global preserving kernel slow feature analysis (DGKSFA), by closely integrating discriminant analysis and GKSFA. The proposed DGKSFA method can extract discriminant feature of batch process as well as preserve global and local geometrical structure information of observed data. For the purpose of fault detection, a monitoring statistic is constructed based on the distance between the optimal kernel feature vectors of test data and normal data. To tackle the challenging issue of nonlinear fault variable identification, a new nonlinear contribution plot method is also developed to help identifying the fault variable after a fault is detected, which is derived from the idea of variable pseudo-sample trajectory projection in DGKSFA nonlinear biplot. Simulation results conducted on a numerical nonlinear dynamic system and the benchmark fed-batch penicillin fermentation process demonstrate that the proposed process monitoring and fault diagnosis approach can effectively detect fault and distinguish fault variables from normal variables. Copyright © 2018 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.

Sideband Algorithm for Automatic Wind Turbine Gearbox Fault Detection and Diagnosis: Preprint

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

Zappala, D.; Tavner, P.; Crabtree, C.

2013-01-01

Improving the availability of wind turbines (WT) is critical to minimize the cost of wind energy, especially for offshore installations. As gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary as turbine load and speed vary continuously with time. Time-consuming and costly manual handling of large amounts of monitoring data representmore » one of the main limitations of most current CMSs, so automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. The algorithm allowed the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation reducing the quantity of information that WT operators must handle.« less

Torsional vibration signal analysis as a diagnostic tool for planetary gear fault detection

NASA Astrophysics Data System (ADS)

Xue, Song; Howard, Ian

2018-02-01

This paper aims to investigate the effectiveness of using the torsional vibration signal as a diagnostic tool for planetary gearbox faults detection. The traditional approach for condition monitoring of the planetary gear uses a stationary transducer mounted on the ring gear casing to measure all the vibration data when the planet gears pass by with the rotation of the carrier arm. However, the time variant vibration transfer paths between the stationary transducer and the rotating planet gear modulate the resultant vibration spectra and make it complex. Torsional vibration signals are theoretically free from this modulation effect and therefore, it is expected to be much easier and more effective to diagnose planetary gear faults using the fault diagnostic information extracted from the torsional vibration. In this paper, a 20 degree of freedom planetary gear lumped-parameter model was developed to obtain the gear dynamic response. In the model, the gear mesh stiffness variations are the main internal vibration generation mechanism and the finite element models were developed for calculation of the sun-planet and ring-planet gear mesh stiffnesses. Gear faults on different components were created in the finite element models to calculate the resultant gear mesh stiffnesses, which were incorporated into the planetary gear model later on to obtain the faulted vibration signal. Some advanced signal processing techniques were utilized to analyses the fault diagnostic results from the torsional vibration. It was found that the planetary gear torsional vibration not only successfully detected the gear fault, but also had the potential to indicate the location of the gear fault. As a result, the planetary gear torsional vibration can be considered an effective alternative approach for planetary gear condition monitoring.

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

2010-01-01

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 [Ed. 's note: See Appendix A-Definitions D 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 perfomu:d average to below average in detection of gear tooth surface pitting failures. The ODM sensor was successful in detecting a significant 8lDOunt 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.

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.

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.

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

Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

PubMed Central

Sun, Hailiang; Zi, Yanyang; He, Zhengjia; Yuan, Jing; Wang, Xiaodong; Chen, Lue

2013-01-01

Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox. PMID:23334609

Software-implemented fault insertion: An FTMP example

NASA Technical Reports Server (NTRS)

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

1987-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

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.

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

Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform

NASA Astrophysics Data System (ADS)

Abd-el-Malek, Mina; Abdelsalam, Ahmed K.; Hassan, Ola E.

2017-09-01

Robustness, low running cost and reduced maintenance lead Induction Motors (IMs) to pioneerly penetrate the industrial drive system fields. Broken rotor bars (BRBs) can be considered as an important fault that needs to be early assessed to minimize the maintenance cost and labor time. The majority of recent BRBs' fault diagnostic techniques focus on differentiating between healthy and faulty rotor cage. In this paper, a new technique is proposed for detecting the location of the broken bar in the rotor. The proposed technique relies on monitoring certain statistical parameters estimated from the analysis of the start-up stator current envelope. The envelope of the signal is obtained using Hilbert Transformation (HT). The proposed technique offers non-invasive, fast computational and accurate location diagnostic process. Various simulation scenarios are presented that validate the effectiveness of the proposed technique.

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

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 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.

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).

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.

Fault detection and diagnosis in a spacecraft attitude determination system

NASA Astrophysics Data System (ADS)

Pirmoradi, F. N.; Sassani, F.; de Silva, C. W.

2009-09-01

This paper presents a new scheme for fault detection and diagnosis (FDD) in spacecraft attitude determination (AD) sensors. An integrated attitude determination system, which includes measurements of rate and angular position using rate gyros and vector sensors, is developed. Measurement data from all sensors are fused by a linearized Kalman filter, which is designed based on the system kinematics, to provide attitude estimation and the values of the gyro bias. Using this information the erroneous sensor measurements are corrected, and unbounded sensor measurement errors are avoided. The resulting bias-free data are used in the FDD scheme. The FDD algorithm uses model-based state estimation, combining the information from the rotational dynamics and kinematics of a spacecraft with the sensor measurements to predict the future sensor outputs. Fault isolation is performed through extended Kalman filters (EKFs). The innovation sequences of EKFs are monitored by several statistical tests to detect the presence of a failure and to localize the failures in all AD sensors. The isolation procedure is developed in two phases. In the first phase, two EKFs are designed, which use subsets of measurements to provide state estimates and form residuals, which are used to verify the source of the fault. In the second phase of isolation, testing of multiple hypotheses is performed. The generalized likelihood ratio test is utilized to identify the faulty components. In the scheme developed in this paper a relatively small number of hypotheses is used, which results in faster isolation and highly distinguishable fault signatures. An important feature of the developed FDD scheme is that it can provide attitude estimations even if only one type of sensors is functioning properly.

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

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.

Operations management system advanced automation: Fault detection isolation and recovery prototyping

NASA Technical Reports Server (NTRS)

Hanson, Matt

1990-01-01

The purpose of this project is to address the global fault detection, isolation and recovery (FDIR) requirements for Operation's Management System (OMS) automation within the Space Station Freedom program. This shall be accomplished by developing a selected FDIR prototype for the Space Station Freedom distributed processing systems. The prototype shall be based on advanced automation methodologies in addition to traditional software methods to meet the requirements for automation. A secondary objective is to expand the scope of the prototyping to encompass multiple aspects of station-wide fault management (SWFM) as discussed in OMS requirements documentation.

Detection of faults and software reliability analysis

NASA Technical Reports Server (NTRS)

Knight, J. C.

1986-01-01

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

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.

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 World Experience With Ion Implant Fault Detection at Freescale Semiconductor

NASA Astrophysics Data System (ADS)

Sing, David C.; Breeden, Terry; Fakhreddine, Hassan; Gladwin, Steven; Locke, Jason; McHugh, Jim; Rendon, Michael

2006-11-01

The Freescale automatic fault detection and classification (FDC) system has logged data from over 3.5 million implants in the past two years. The Freescale FDC system is a low cost system which collects summary implant statistics at the conclusion of each implant run. The data is collected by either downloading implant data log files from the implant tool workstation, or by exporting summary implant statistics through the tool's automation interface. Compared to the traditional FDC systems which gather trace data from sensors on the tool as the implant proceeds, the Freescale FDC system cannot prevent scrap when a fault initially occurs, since the data is collected after the implant concludes. However, the system can prevent catastrophic scrap events due to faults which are not detected for days or weeks, leading to the loss of hundreds or thousands of wafers. At the Freescale ATMC facility, the practical applications of the FD system fall into two categories: PM trigger rules which monitor tool signals such as ion gauges and charge control signals, and scrap prevention rules which are designed to detect specific failure modes that have been correlated to yield loss and scrap. PM trigger rules are designed to detect shifts in tool signals which indicate normal aging of tool systems. For example, charging parameters gradually shift as flood gun assemblies age, and when charge control rules start to fail a flood gun PM is performed. Scrap prevention rules are deployed to detect events such as particle bursts and excessive beam noise, events which have been correlated to yield loss. The FDC system does have tool log-down capability, and scrap prevention rules often use this capability to automatically log the tool into a maintenance state while simultaneously paging the sustaining technician for data review and disposition of the affected product.

Detection, isolation and diagnosability analysis of intermittent faults in stochastic systems

NASA Astrophysics Data System (ADS)

Yan, Rongyi; He, Xiao; Wang, Zidong; Zhou, D. H.

2018-02-01

Intermittent faults (IFs) have the properties of unpredictability, non-determinacy, inconsistency and repeatability, switching systems between faulty and healthy status. In this paper, the fault detection and isolation (FDI) problem of IFs in a class of linear stochastic systems is investigated. For the detection and isolation of IFs, it includes: (1) to detect all the appearing time and the disappearing time of an IF; (2) to detect each appearing (disappearing) time of the IF before the subsequent disappearing (appearing) time; (3) to determine where the IFs happen. Based on the outputs of the observers we designed, a novel set of residuals is constructed by using the sliding-time window technique, and two hypothesis tests are proposed to detect all the appearing time and disappearing time of IFs. The isolation problem of IFs is also considered. Furthermore, within a statistical framework, the definition of the diagnosability of IFs is proposed, and a sufficient condition is brought forward for the diagnosability of IFs. Quantitative performance analysis results for the false alarm rate and missing detection rate are discussed, and the influences of some key parameters of the proposed scheme on performance indices such as the false alarm rate and missing detection rate are analysed rigorously. The effectiveness of the proposed scheme is illustrated via a simulation example of an unmanned helicopter longitudinal control system.

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 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.

Delineating Concealed Faults within Cogdell Oil Field via Earthquake Detection

NASA Astrophysics Data System (ADS)

Aiken, C.; Walter, J. I.; Brudzinski, M.; Skoumal, R.; Savvaidis, A.; Frohlich, C.; Borgfeldt, T.; Dotray, P.

2016-12-01

Cogdell oil field, located within the Permian Basin of western Texas, has experienced several earthquakes ranging from magnitude 1.7 to 4.6, most of which were recorded since 2006. Using the Earthscope USArray, Gan and Frohlich [2013] relocated some of these events and found a positive correlation in the timing of increased earthquake activity and increased CO2 injection volume. However, focal depths of these earthquakes are unknown due to 70 km station spacing of the USArray. Accurate focal depths as well as new detections can delineate subsurface faults and establish whether earthquakes are occurring in the shallow sediments or in the deeper basement. To delineate subsurface fault(s) in this region, we first detect earthquakes not currently listed in the USGS catalog by applying continuous waveform-template matching algorithms to multiple seismic data sets. We utilize seismic data spanning the time frame of 2006 to 2016 - which includes data from the U.S. Geological Survey Global Seismographic Network, the USArray, and the Sweetwater, TX broadband and nodal array located 20-40 km away. The catalog of earthquakes enhanced by template matching reveals events that were well recorded by the large-N Sweetwater array, so we are experimenting with strategies for optimizing template matching using different configurations of many stations. Since earthquake activity in the Cogdell oil field is on-going (a magnitude 2.6 occurred on May 29, 2016), a temporary deployment of TexNet seismometers has been planned for the immediate vicinity of Cogdell oil field in August 2016. Results on focal depths and detection of small magnitude events are pending this small local network deployment.

Ste. Genevieve Fault Zone, Missouri and Illinois. Final report

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

Nelson, W.J.; Lumm, D.K.

1985-07-01

The Ste. Genevieve Fault Zone is a major structural feature which strikes NW-SE for about 190 km on the NE flank of the Ozark Dome. There is up to 900 m of vertical displacement on high angle normal and reverse faults in the fault zone. At both ends the Ste. Genevieve Fault Zone dies out into a monocline. Two periods of faulting occurred. The first was in late Middle Devonian time and the second from latest Mississippian through early Pennsylvanian time, with possible minor post-Pennsylvanian movement. No evidence was found to support the hypothesis that the Ste. Genevieve Fault Zonemore » is part of a northwestward extension of the late Precambrian-early Cambrian Reelfoot Rift. The magnetic and gravity anomalies cited in support of the ''St. Louis arm'' of the Reelfoot Rift possible reflect deep crystal features underlying and older than the volcanic terrain of the St. Francois Mountains (1.2 to 1.5 billion years old). In regard to neotectonics no displacements of Quaternary sediments have been detected, but small earthquakes occur from time to time along the Ste. Genevieve Fault Zone. Many faults in the zone appear capable of slipping under the current stress regime of east-northeast to west-southwest horizontal compression. We conclude that the zone may continue to experience small earth movements, but catastrophic quakes similar to those at New Madrid in 1811-12 are unlikely. 32 figs., 1 tab.« less

Geodetic Finite-Fault-based Earthquake Early Warning Performance for Great Earthquakes Worldwide

NASA Astrophysics Data System (ADS)

Ruhl, C. J.; Melgar, D.; Grapenthin, R.; Allen, R. M.

2017-12-01

GNSS-based earthquake early warning (EEW) algorithms estimate fault-finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because large events are infrequent, algorithms are not regularly exercised and insufficiently tested on few available datasets. The Geodetic Alarm System (G-larmS) is a GNSS-based finite-fault algorithm developed as part of the ShakeAlert EEW system in the western US. Performance evaluations using synthetic earthquakes offshore Cascadia showed that G-larmS satisfactorily recovers magnitude and fault length, providing useful alerts 30-40 s after origin time and timely warnings of ground motion for onshore urban areas. An end-to-end test of the ShakeAlert system demonstrated the need for GNSS data to accurately estimate ground motions in real-time. We replay real data from several subduction-zone earthquakes worldwide to demonstrate the value of GNSS-based EEW for the largest, most damaging events. We compare predicted ground acceleration (PGA) from first-alert-solutions with those recorded in major urban areas. In addition, where applicable, we compare observed tsunami heights to those predicted from the G-larmS solutions. We show that finite-fault inversion based on GNSS-data is essential to achieving the goals of EEW.

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 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.

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.

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

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

High resolution seismics methods in application to fault zone detection

NASA Astrophysics Data System (ADS)

Matula, Rafal; Czaja, Klaudia; Mahmod, Adam Ahmed

2014-05-01

Surveys were carried out along border line between Outer Carpathians, Inner Carpathians and Pieniny Klippen Belt. Main point of interest was imaging transition zone structured by para-conglomerates, sandstone and clays lenses, crossing in near neighbourhood of Stare Bystre, village in the southern part of Poland. Actually geological works states existence of two hypothetical faults, first at the direction NE-SW and second NNW-SSE. Main aim of geological and geophysical investigation was to prove that mentioned fault has a system of smaller discontinuities connected with previous main fault activity. Para-conglomerate exposures, which is localized close to discussed fault is cut by visible system of cracks. That fact provide geological evidences that this system could be the effect of previous fault activity so in other words, it has a continuation up to main discontinuities. What is more part of the same formation para-conglomerates is covered by Neogen river sediments, so non-direct detection methods of cracks azimuth must be applied. Geophysical investigation was located near mentioned exposure and conducted in 3-D variant. Measurements were extremely focused on determining any changes of elevation buried para-conglomerates and velocity variation inside studied sediments. Seismic methods such as refraction and refraction tomography were used to imaging bedrock. Surveys were carried out in non typical acquisition, azimuthal schema. During field works 24- channels seismograph and 4 Hz, 10 Hz and 100 Hz geophones were used. Hypothetical discontinuities were estimated after analysing seismic records and expressed by velocity variation in bedding rocks and additionally evaluated changes in its elevation. Furthermore, in this study attempt of use refraction wave attributes related to loosing rock - para-conglomerates continuity were exposed. The presentation of geophysical data had a volumetric character what was easier to interpret and better related to assumptions

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.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure

PubMed Central

Park, Wookje; Jung, Sikhang

2014-01-01

Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure. Twelve available coefficients among healthy and faulty status data group were used to determine the decision. Similarity measure weighting was done and obtained through random forest algorithm (RFA); RF provides data priority. In order to get a fast response of decision, a limited number of coefficients was also considered. Relation of detection rate and amount of feature data were analyzed and illustrated. By repeated trial of similarity calculation, useful data amount was obtained. PMID:25057508

Joint Seismic-Geodetic Algorithm for Finite-Fault Detection and Slip Inversion in the West Coast ShakeAlert System

NASA Astrophysics Data System (ADS)

Smith, D. E.; Felizardo, C.; Minson, S. E.; Boese, M.; Langbein, J. O.; Murray, J. R.

2016-12-01

Finite-fault source algorithms can greatly benefit earthquake early warning (EEW) systems. Estimates of finite-fault parameters provide spatial information, which can significantly improve real-time shaking calculations and help with disaster response. In this project, we have focused on integrating a finite-fault seismic-geodetic algorithm into the West Coast ShakeAlert framework. The seismic part is FinDer 2, a C++ version of the algorithm developed by Böse et al. (2012). It interpolates peak ground accelerations and calculates the best fault length and strike from template matching. The geodetic part is a C++ version of BEFORES, the algorithm developed by Minson et al. (2014) that uses a Bayesian methodology to search for the most probable slip distribution on a fault of unknown orientation. Ultimately, these two will be used together where FinDer generates a Bayesian prior for BEFORES via the methodology of Minson et al. (2015), and the joint solution will generate estimates of finite-fault extent, strike, dip, best slip distribution, and magnitude. We have created C++ versions of both FinDer and BEFORES using open source libraries and have developed a C++ Application Protocol Interface (API) for them both. Their APIs allow FinDer and BEFORES to contribute to the ShakeAlert system via an open source messaging system, ActiveMQ. FinDer has been receiving real-time data, detecting earthquakes, and reporting messages on the development system for several months. We are also testing FinDer extensively with Earthworm tankplayer files. BEFORES has been tested with ActiveMQ messaging in the ShakeAlert framework, and works off a FinDer trigger. We are finishing the FinDer-BEFORES connections in this framework, and testing this system via seismic-geodetic tankplayer files. This will include actual and simulated data.

Fault detection for discrete-time LPV systems using interval observers

NASA Astrophysics Data System (ADS)

Zhang, Zhi-Hui; Yang, Guang-Hong

2017-10-01

This paper is concerned with the fault detection (FD) problem for discrete-time linear parameter-varying systems subject to bounded disturbances. A parameter-dependent FD interval observer is designed based on parameter-dependent Lyapunov and slack matrices. The design method is presented by translating the parameter-dependent linear matrix inequalities (LMIs) into finite ones. In contrast to the existing results based on parameter-independent and diagonal Lyapunov matrices, the derived disturbance attenuation, fault sensitivity and nonnegative conditions lead to less conservative LMI characterisations. Furthermore, without the need to design the residual evaluation functions and thresholds, the residual intervals generated by the interval observers are used directly for FD decision. Finally, simulation results are presented for showing the effectiveness and superiority of the proposed method.

Fault kinematics and localised inversion within the Troms-Finnmark Fault Complex, SW Barents Sea

NASA Astrophysics Data System (ADS)

Zervas, I.; Omosanya, K. O.; Lippard, S. J.; Johansen, S. E.

2018-04-01

The areas bounding the Troms-Finnmark Fault Complex are affected by complex tectonic evolution. In this work, the history of fault growth, reactivation, and inversion of major faults in the Troms-Finnmark Fault Complex and the Ringvassøy Loppa Fault Complex is interpreted from three-dimensional seismic data, structural maps and fault displacement plots. Our results reveal eight normal faults bounding rotated fault blocks in the Troms-Finnmark Fault Complex. Both the throw-depth and displacement-distance plots show that the faults exhibit complex configurations of lateral and vertical segmentation with varied profiles. Some of the faults were reactivated by dip-linkages during the Late Jurassic and exhibit polycyclic fault growth, including radial, syn-sedimentary, and hybrid propagation. Localised positive inversion is the main mechanism of fault reactivation occurring at the Troms-Finnmark Fault Complex. The observed structural styles include folds associated with extensional faults, folded growth wedges and inverted depocentres. Localised inversion was intermittent with rifting during the Middle Jurassic-Early Cretaceous at the boundaries of the Troms-Finnmark Fault Complex to the Finnmark Platform. Additionally, tectonic inversion was more intense at the boundaries of the two fault complexes, affecting Middle Triassic to Early Cretaceous strata. Our study shows that localised folding is either a product of compressional forces or of lateral movements in the Troms-Finnmark Fault Complex. Regional stresses due to the uplift in the Loppa High and halokinesis in the Tromsø Basin are likely additional causes of inversion in the Troms-Finnmark Fault Complex.

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.

Detection of faults in rotating machinery using periodic time-frequency sparsity

NASA Astrophysics Data System (ADS)

Ding, Yin; He, Wangpeng; Chen, Binqiang; Zi, Yanyang; Selesnick, Ivan W.

2016-11-01

This paper addresses the problem of extracting periodic oscillatory features in vibration signals for detecting faults in rotating machinery. To extract the feature, we propose an approach in the short-time Fourier transform (STFT) domain where the periodic oscillatory feature manifests itself as a relatively sparse grid. To estimate the sparse grid, we formulate an optimization problem using customized binary weights in the regularizer, where the weights are formulated to promote periodicity. In order to solve the proposed optimization problem, we develop an algorithm called augmented Lagrangian majorization-minimization algorithm, which combines the split augmented Lagrangian shrinkage algorithm (SALSA) with majorization-minimization (MM), and is guaranteed to converge for both convex and non-convex formulation. As examples, the proposed approach is applied to simulated data, and used as a tool for diagnosing faults in bearings and gearboxes for real data, and compared to some state-of-the-art methods. The results show that the proposed approach can effectively detect and extract the periodical oscillatory features.

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Automated vehicle for railway track fault detection

NASA Astrophysics Data System (ADS)

Bhushan, M.; Sujay, S.; Tushar, B.; Chitra, P.

2017-11-01

For the safety reasons, railroad tracks need to be inspected on a regular basis for detecting physical defects or design non compliances. Such track defects and non compliances, if not detected in a certain interval of time, may eventually lead to severe consequences such as train derailments. Inspection must happen twice weekly by a human inspector to maintain safety standards as there are hundreds and thousands of miles of railroad track. But in such type of manual inspection, there are many drawbacks that may result in the poor inspection of the track, due to which accidents may cause in future. So to avoid such errors and severe accidents, this automated system is designed.Such a concept would surely introduce automation in the field of inspection process of railway track and can help to avoid mishaps and severe accidents due to faults in the track.

Spontaneous non-volcanic tremor detected in the Anza Seismic Gap of San Jacinto Fault

NASA Astrophysics Data System (ADS)

Hutchison, A. A.; Ghosh, A.

2017-12-01

Non-volcanic tremor (NVT), a type of slow earthquake, is becoming more frequently detected along plate boundaries, particularly in subduction zones, and is also observed along the San Andreas Fault [e.g. Nadeau & Dolenc, 2005]. NVT is typically associated with transient deformation (i.e. slow slip) in the transition zone [e.g. Ide et al., 2007], and at times it is observed with deep creep along faults [e.g. Beroza & Ide, 2011]. Using several independent location and detection methods including multi-beam backprojection [Ghosh et al., 2009a; 2012], envelope cross correlation [Wech & Creager, 2008], spectral analyses and visual inspection of existing network stations and high-density mini seismic array data, we detect multiple discrete spontaneous tremor events in the Anza Gap of the San Jacinto Fault (SJF) in June, 2011. The events occur on the SJF where the Hot Springs Fault terminates, on the northwestern boundary of the Anza Gap, below the inferred seismogenic zone characterized by velocity weakening frictional behavior [e.g. Lindsay et al., 2014]. The location methods provide consistent locations for each event in our catalog. Low slowness values help rule-out surface noise that may result in false detections. Analyses of frequency spectra show these time windows are depleted in high frequency energy in the displacement amplitude spectrum compared to small local regular (fast) earthquakes. This spectral pattern is characteristic of tremor [Shelly et al., 2007]. We interpret this tremor to be a seismic manifestation of slow-slip events below the seismogenic zone. Recently, an independent geodetic study suggests that the 2010 El Mayor-Cucupah earthquake triggered a slow-slip event in the Anza Gap [Inbal et al., 2017]. In addition, multiple studies infer deep creep in the SJF [e.g. Meng & Peng et al., 2016; Jiang & Fialko, 2016] indicating that this fault is capable of producing slow slip events. Transient tectonic behavior like tremor and slow slip may be playing

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

NASA Astrophysics Data System (ADS)

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

2017-07-01

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

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).

Fault detection techniques for complex cable shield topologies

NASA Astrophysics Data System (ADS)

Coonrod, Kurt H.; Davis, Stuart L.; McLemore, Donald P.

1994-09-01

This document presents the results of a basic principles study which investigated technical approaches for developing fault detection techniques for use on cables with complex shielding topologies. The study was limited to those approaches which could realistically be implemented on a fielded cable, i.e., approaches which would require partial disassembly of a cable were not pursued. The general approach used was to start with present transfer impedance measurement techniques and modify their use to achieve the best possible measurement range. An alternative test approach, similar to a sniffer type test, was also investigated.

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 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%.

Effects of Channel Modification on Detection and Dating of Fault Scarps

NASA Astrophysics Data System (ADS)

Sare, R.; Hilley, G. E.

2016-12-01

Template matching of scarp-like features could potentially generate morphologic age estimates for individual scarps over entire regions, but data noise and scarp modification limits detection of fault scarps by this method. Template functions based on diffusion in the cross-scarp direction may fail to accurately date scarps near channel boundaries. Where channels reduce scarp amplitudes, or where cross-scarp noise is significant, signal-to-noise ratios decrease and the scarp may be poorly resolved. In this contribution, we explore the bias in morphologic age of a complex scarp produced by systematic changes in fault scarp curvature. For example, fault scarps may be modified by encroaching channel banks and mass failure, lateral diffusion of material into a channel, or undercutting parallel to the base of a scarp. We quantify such biases on morphologic age estimates using a block offset model subject to two-dimensional linear diffusion. We carry out a synthetic study of the effects of two-dimensional transport on morphologic age calculated using a profile model, and compare these results to a well- studied and constrained site along the San Andreas Fault at Wallace Creek, CA. This study serves as a first step towards defining regions of high confidence in template matching results based on scarp length, channel geometry, and near-scarp topography.

An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

NASA Astrophysics Data System (ADS)

Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang

2016-02-01

Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

Methodology for Designing Fault-Protection Software

NASA Technical Reports Server (NTRS)

Barltrop, Kevin; Levison, Jeffrey; Kan, Edwin

2006-01-01

A document describes a methodology for designing fault-protection (FP) software for autonomous spacecraft. The methodology embodies and extends established engineering practices in the technical discipline of Fault Detection, Diagnosis, Mitigation, and Recovery; and has been successfully implemented in the Deep Impact Spacecraft, a NASA Discovery mission. Based on established concepts of Fault Monitors and Responses, this FP methodology extends the notion of Opinion, Symptom, Alarm (aka Fault), and Response with numerous new notions, sub-notions, software constructs, and logic and timing gates. For example, Monitor generates a RawOpinion, which graduates into Opinion, categorized into no-opinion, acceptable, or unacceptable opinion. RaiseSymptom, ForceSymptom, and ClearSymptom govern the establishment and then mapping to an Alarm (aka Fault). Local Response is distinguished from FP System Response. A 1-to-n and n-to- 1 mapping is established among Monitors, Symptoms, and Responses. Responses are categorized by device versus by function. Responses operate in tiers, where the early tiers attempt to resolve the Fault in a localized step-by-step fashion, relegating more system-level response to later tier(s). Recovery actions are gated by epoch recovery timing, enabling strategy, urgency, MaxRetry gate, hardware availability, hazardous versus ordinary fault, and many other priority gates. This methodology is systematic, logical, and uses multiple linked tables, parameter files, and recovery command sequences. The credibility of the FP design is proven via a fault-tree analysis "top-down" approach, and a functional fault-mode-effects-and-analysis via "bottoms-up" approach. Via this process, the mitigation and recovery strategy(s) per Fault Containment Region scope (width versus depth) the FP architecture.

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.

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.

The evolving contribution of border faults and intra-rift faults in early-stage East African rifts: insights from the Natron (Tanzania) and Magadi (Kenya) basins

NASA Astrophysics Data System (ADS)

Muirhead, J.; Kattenhorn, S. A.; Dindi, E.; Gama, R.

2013-12-01

In the early stages of continental rifting, East African Rift (EAR) basins are conventionally depicted as asymmetric basins bounded on one side by a ~100 km-long border fault. As rifting progresses, strain concentrates into the rift center, producing intra-rift faults. The timing and nature of the transition from border fault to intra-rift-dominated strain accommodation is unclear. Our study focuses on this transitional phase of continental rifting by exploring the spatial and temporal evolution of faulting in the Natron (border fault initiation at ~3 Ma) and Magadi (~7 Ma) basins of northern Tanzania and southern Kenya, respectively. We compare the morphologies and activity histories of faults in each basin using field observations and remote sensing in order to address the relative contributions of border faults and intra-rift faults to crustal strain accommodation as rifting progresses. The ~500 m-high border fault along the western margin of the Natron basin is steep compared to many border faults in the eastern branch of the EAR, indicating limited scarp degradation by mass wasting. Locally, the escarpment shows open fissures and young scarps 10s of meters high and a few kilometers long, implying ongoing border fault activity in this young rift. However, intra-rift faults within ~1 Ma lavas are greatly eroded and fresh scarps are typically absent, implying long recurrence intervals between slip events. Rift-normal topographic profiles across the Natron basin show the lowest elevations in the lake-filled basin adjacent to the border fault, where a number of hydrothermal springs along the border fault system expel water into the lake. In contrast to Natron, a ~1600 m high, densely vegetated, border fault escarpment along the western edge of the Magadi basin is highly degraded; we were unable to identify evidence of recent rupturing. Rift-normal elevation profiles indicate the focus of strain has migrated away from the border fault into the rift center, where

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

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

Kim, Woohyun; Braun, J.

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

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.

Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method

PubMed Central

Jiang, Zhinong; Wang, Zijia; Zhang, Jinjie

2017-01-01

Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable. PMID:29244722

Fault Diagnosis of Internal Combustion Engine Valve Clearance Using the Impact Commencement Detection Method.

PubMed

Jiang, Zhinong; Mao, Zhiwei; Wang, Zijia; Zhang, Jinjie

2017-12-15

Internal combustion engines (ICEs) are widely used in many important fields. The valve train clearance of an ICE usually exceeds the normal value due to wear or faulty adjustment. This work aims at diagnosing the valve clearance fault based on the vibration signals measured on the engine cylinder heads. The non-stationarity of the ICE operating condition makes it difficult to obtain the nominal baseline, which is always an awkward problem for fault diagnosis. This paper overcomes the problem by inspecting the timing of valve closing impacts, of which the referenced baseline can be obtained by referencing design parameters rather than extraction during healthy conditions. To accurately detect the timing of valve closing impact from vibration signals, we carry out a new method to detect and extract the commencement of the impacts. The results of experiments conducted on a twelve-cylinder ICE test rig show that the approach is capable of extracting the commencement of valve closing impact accurately and using only one feature can give a superior monitoring of valve clearance. With the help of this technique, the valve clearance fault becomes detectable even without the comparison to the baseline, and the changing trend of the clearance could be trackable.

Fault Detection, Isolation and Recovery (FDIR) Portable Liquid Oxygen Hardware Demonstrator

NASA Technical Reports Server (NTRS)

Oostdyk, Rebecca L.; Perotti, Jose M.

2011-01-01

The Fault Detection, Isolation and Recovery (FDIR) hardware demonstration will highlight the effort being conducted by Constellation's Ground Operations (GO) to provide the Launch Control System (LCS) with system-level health management during vehicle processing and countdown activities. A proof-of-concept demonstration of the FDIR prototype established the capability of the software to provide real-time fault detection and isolation using generated Liquid Hydrogen data. The FDIR portable testbed unit (presented here) aims to enhance FDIR by providing a dynamic simulation of Constellation subsystems that feed the FDIR software live data based on Liquid Oxygen system properties. The LO2 cryogenic ground system has key properties that are analogous to the properties of an electronic circuit. The LO2 system is modeled using electrical components and an equivalent circuit is designed on a printed circuit board to simulate the live data. The portable testbed is also be equipped with data acquisition and communication hardware to relay the measurements to the FDIR application running on a PC. This portable testbed is an ideal capability to perform FDIR software testing, troubleshooting, training among others.

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.

Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

PubMed Central

Wang, Lijuan; Wu, Lifeng; Guan, Yong; Wang, Guohui

2015-01-01

We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model. PMID:25690553

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

NASA Astrophysics Data System (ADS)

Schlechtingen, Meik; Ferreira Santos, Ilmar

2011-07-01

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

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

PubMed

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

2016-01-01

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

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

PubMed Central

Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun

2016-01-01

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

Digital electronic engine control fault detection and accommodation flight evaluation

NASA Technical Reports Server (NTRS)

Baer-Ruedhart, J. L.

1984-01-01

The capabilities and performance of various fault detection and accommodation (FDA) schemes in existing and projected engine control systems were investigated. Flight tests of the digital electronic engine control (DEEC) in an F-15 aircraft show discrepancies between flight results and predictions based on simulation and altitude testing. The FDA methodology and logic in the DEEC system, and the results of the flight failures which occurred to date are described.

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.

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.

Row fault detection system

DOEpatents

Archer, Charles Jens [Rochester, MN; Pinnow, Kurt Walter [Rochester, MN; Ratterman, Joseph D [Rochester, MN; Smith, Brian Edward [Rochester, MN

2008-10-14

An apparatus, program product and method checks for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

Row fault detection system

DOEpatents

Archer, Charles Jens [Rochester, MN; Pinnow, Kurt Walter [Rochester, MN; Ratterman, Joseph D [Rochester, MN; Smith, Brian Edward [Rochester, MN

2012-02-07

An apparatus, program product and method check for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

Row fault detection system

DOEpatents

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

2010-02-23

An apparatus and program product check for nodal faults in a row of nodes by causing each node in the row to concurrently communicate with its adjacent neighbor nodes in the row. The communications are analyzed to determine a presence of a faulty node or connection.

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.

Evaluation of MEMS-Based Wireless Accelerometer Sensors in Detecting Gear Tooth Faults in Helicopter Transmissions

NASA Technical Reports Server (NTRS)

Lewicki, David George; Lambert, Nicholas A.; Wagoner, Robert S.

2015-01-01

The diagnostics capability of micro-electro-mechanical systems (MEMS) based rotating accelerometer sensors in detecting gear tooth crack failures in helicopter main-rotor transmissions was evaluated. MEMS sensors were installed on a pre-notched OH-58C spiral-bevel pinion gear. Endurance tests were performed and the gear was run to tooth fracture failure. Results from the MEMS sensor were compared to conventional accelerometers mounted on the transmission housing. Most of the four stationary accelerometers mounted on the gear box housing and most of the CI's used gave indications of failure at the end of the test. The MEMS system performed well and lasted the entire test. All MEMS accelerometers gave an indication of failure at the end of the test. The MEMS systems performed as well, if not better, than the stationary accelerometers mounted on the gear box housing with regards to gear tooth fault detection. For both the MEMS sensors and stationary sensors, the fault detection time was not much sooner than the actual tooth fracture time. The MEMS sensor spectrum data showed large first order shaft frequency sidebands due to the measurement rotating frame of reference. The method of constructing a pseudo tach signal from periodic characteristics of the vibration data was successful in deriving a TSA signal without an actual tach and proved as an effective way to improve fault detection for the MEMS.

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

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.

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.

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

Experimental Measurements of Permeability Evolution along Faults during Progressive Slip

NASA Astrophysics Data System (ADS)

Strutz, M.; Mitchell, T. M.; Renner, J.

2010-12-01

Little is currently known about the dynamic changes in fault-parallel permeability along rough faults during progressive slip. With increasing slip, asperities are worn to produce gouge which can dramatically reduce along fault permeability within the slip zone. However, faults can have a range of roughness which can affect both the porosity and both the amount and distribution of fault wear material produced in the slipping zone during the early stages of fault evolution. In this novel study we investigate experimentally the evolution of permeability along a fault plane in granite sawcut sliding blocks with a variety of intial roughnesses in a triaxial apparatus. Drillholes in the samples allow the permeability to be measured along the fault plane during loading and subsequent fault displacement. Use of the pore pressure oscillation technique (PPO) allows the continuous measurement of permeability without having to stop loading. To achieve a range of intial starting roughnesses, faults sawcut surfaces were prepared using a variety of corundum powders ranging from 10 µm to 220 µm, and for coarser roughness were air-blasted with glass beads up to 800µm in size. Fault roughness has been quantified with a laser profileometer. During sliding, we measure the acoustic emissions in order to detect grain cracking and asperity shearing which may relate to both the mechanical and permeability data. Permeability shows relative reductions of up to over 4 orders of magnitude during stable sliding as asperities are sheared to produce a fine fault gouge. This variation in permeability is greatest for the roughest faults, reducing as fault roughness decreases. The onset of permeability reduction is contemporaneous with a dramatic reduction in the amount of detected acoustic emissions, where a continuous layer of fault gouge has developed. The amount of fault gouge produced is related to the initial roughness, with the rough faults showing larger fault gouge layers at the end of

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.

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.

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

NASA Astrophysics Data System (ADS)

Naderi, E.; Khorasani, K.

2018-02-01

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

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

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

Discrete Data Qualification System and Method Comprising Noise Series Fault Detection

NASA Technical Reports Server (NTRS)

Fulton, Christopher; Wong, Edmond; Melcher, Kevin; Bickford, Randall

2013-01-01

A Sensor Data Qualification (SDQ) function has been developed that allows the onboard flight computers on NASA s launch vehicles to determine the validity of sensor data to ensure that critical safety and operational decisions are not based on faulty sensor data. This SDQ function includes a novel noise series fault detection algorithm for qualification of the output data from LO2 and LH2 low-level liquid sensors. These sensors are positioned in a launch vehicle s propellant tanks in order to detect propellant depletion during a rocket engine s boost operating phase. This detection capability can prevent the catastrophic situation where the engine operates without propellant. The output from each LO2 and LH2 low-level liquid sensor is a discrete valued signal that is expected to be in either of two states, depending on whether the sensor is immersed (wet) or exposed (dry). Conventional methods for sensor data qualification, such as threshold limit checking, are not effective for this type of signal due to its discrete binary-state nature. To address this data qualification challenge, a noise computation and evaluation method, also known as a noise fault detector, was developed to detect unreasonable statistical characteristics in the discrete data stream. The method operates on a time series of discrete data observations over a moving window of data points and performs a continuous examination of the resulting observation stream to identify the presence of anomalous characteristics. If the method determines the existence of anomalous results, the data from the sensor is disqualified for use by other monitoring or control functions.

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

NASA Technical Reports Server (NTRS)

He, Yuning

2015-01-01

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

Fault geometries in basement-induced wrench faulting under different initial stress states

NASA Astrophysics Data System (ADS)

Naylor, M. A.; Mandl, G.; Supesteijn, C. H. K.

Scaled sandbox experiments were used to generate models for relative ages, dip, strike and three-dimensional shape of faults in basement-controlled wrench faulting. The basic fault sequence runs from early en échelon Riedel shears and splay faults through 'lower-angle' shears to P shears. The Riedel shears are concave upwards and define a tulip structure in cross-section. In three dimensions, each Riedel shear has a helicoidal form. The sequence of faults and three-dimensional geometry are rationalized in terms of the prevailing stress field and Coulomb-Mohr theory of shear failure. The stress state in the sedimentary overburden before wrenching begins has a substantial influence on the fault geometries and on the final complexity of the fault zone. With the maximum compressive stress (∂ 1) initially parallel to the basement fault (transtension), Riedel shears are only slightly en échelon, sub-parallel to the basement fault, steeply dipping with a reduced helicoidal aspect. Conversely, with ∂ 1 initially perpendicular to the basement fault (transpression), Riedel shears are strongly oblique to the basement fault strike, have lower dips and an exaggerated helicoidal form; the final fault zone is both wide and complex. We find good agreement between the models and both mechanical theory and natural examples of wrench faulting.

Scheme for predictive fault diagnosis in photo-voltaic modules using thermal imaging

NASA Astrophysics Data System (ADS)

Jaffery, Zainul Abdin; Dubey, Ashwani Kumar; Irshad; Haque, Ahteshamul

2017-06-01

Degradation of PV modules can cause excessive overheating which results in a reduced power output and eventually failure of solar panel. To maintain the long term reliability of solar modules and maximize the power output, faults in modules need to be diagnosed at an early stage. This paper provides a comprehensive algorithm for fault diagnosis in solar modules using infrared thermography. Infrared Thermography (IRT) is a reliable, non-destructive, fast and cost effective technique which is widely used to identify where and how faults occurred in an electrical installation. Infrared images were used for condition monitoring of solar modules and fuzzy logic have been used to incorporate intelligent classification of faults. An automatic approach has been suggested for fault detection, classification and analysis. IR images were acquired using an IR camera. To have an estimation of thermal condition of PV module, the faulty panel images were compared to a healthy PV module thermal image. A fuzzy rule-base was used to classify faults automatically. Maintenance actions have been advised based on type of faults.

Early Detection of Sporadic Pancreatic Cancer

PubMed Central

Chari, Suresh T.; Kelly, Kimberly; Hollingsworth, Michael A.; Thayer, Sarah P.; Ahlquist, David A.; Andersen, Dana K.; Batra, Surinder K.; Brentnall, Teresa A.; Canto, Marcia; Cleeter, Deborah F.; Firpo, Matthew A.; Gambhir, Sanjiv Sam; Go, Vay Liang W.; Hines, O. Joe; Kenner, Barbara J.; Klimstra, David S.; Lerch, Markus M.; Levy, Michael J.; Maitra, Anirban; Mulvihill, Sean J.; Petersen, Gloria M.; Rhim, Andrew D.; Simeone, Diane M.; Srivastava, Sudhir; Tanaka, Masao; Vinik, Aaron I.; Wong, David

2015-01-01

Abstract Pancreatic cancer (PC) is estimated to become the second leading cause of cancer death in the United States by 2020. Early detection is the key to improving survival in PC. Addressing this urgent need, the Kenner Family Research Fund conducted the inaugural Early Detection of Sporadic Pancreatic Cancer Summit Conference in 2014 in conjunction with the 45th Anniversary Meeting of the American Pancreatic Association and Japan Pancreas Society. This seminal convening of international representatives from science, practice, and clinical research was designed to facilitate challenging interdisciplinary conversations to generate innovative ideas leading to the creation of a defined collaborative strategic pathway for the future of the field. An in-depth summary of current efforts in the field, analysis of gaps in specific areas of expertise, and challenges that exist in early detection is presented within distinct areas of inquiry: Case for Early Detection: Definitions, Detection, Survival, and Challenges; Biomarkers for Early Detection; Imaging; and Collaborative Studies. In addition, an overview of efforts in familial PC is presented in an addendum to this article. It is clear from the summit deliberations that only strategically designed collaboration among investigators, institutions, and funders will lead to significant progress in early detection of sporadic PC. PMID:25931254

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

PubMed

Bustillo, Andres; Correa, Maritza; Reñones, Anibal

2011-01-01

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

Seismic Monitoring Prior to and During DFDP-2 Drilling, Alpine Fault, New Zealand: Matched-Filter Detection Testing and the Real-Time Monitoring System

NASA Astrophysics Data System (ADS)

Boese, C. M.; Chamberlain, C. J.; Townend, J.

2015-12-01

In preparation for the second stage of the Deep Fault Drilling Project (DFDP) and as part of related research projects, borehole and surface seismic stations were installed near the intended DFDP-2 drill-site in the Whataroa Valley from late 2008. The final four borehole stations were installed within 1.2 km of the drill-site in early 2013 to provide near-field observations of any seismicity that occurred during drilling and thus provide input into operational decision-making processes if required. The basis for making operational decisions in response to any detected seismicity had been established as part of a safety review conducted in early 2014 and was implemented using a "traffic light" system, a communications plan, and other operational documents. Continuous real-time earthquake monitoring took place throughout the drilling period, between September and late December 2014, and involved a team of up to 15 seismologists working in shifts near the drill-site and overseas. Prior to drilling, records from 55 local earthquakes and 14 quarry blasts were used as master templates in a matched-filter detection algorithm to test the capabilities of the seismic network for detecting seismicity near the drill site. The newly detected microseismicity was clustered near the DFDP-1 drill site at Gaunt Creek, 7.4 km southwest of DFDP-2. Relocations of these detected events provide more information about the fault geometry in this area. Although no detectable seismicity occurred within 5 km of the drill site during the drilling period, the region is capable of generating earthquakes that would have required an operational response had they occurred while drilling was underway (including a M2.9 event northwest of Gaunt Creek on 15 August 2014). The largest event to occur while drilling was underway was of M4.5 and occurred approximately 40 km east of the DFDP-2 drill site. In this presentation, we summarize the setup and operations of the seismic network and discuss key

Fault detection monitor circuit provides ''self-heal capability'' in electronic modules - A concept

NASA Technical Reports Server (NTRS)

Kennedy, J. J.

1970-01-01

Self-checking technique detects defective solid state modules used in electronic test and checkout instrumentation. A ten bit register provides failure monitor and indication for 1023 comparator circuits, and the automatic fault-isolation capability permits the electronic subsystems to be repaired by replacing the defective module.

The Border Ranges fault system in Glacier Bay National Park, Alaska: Evidence for major early Cenozoic dextral strike-slip motion

USGS Publications Warehouse

Smart, K.J.; Pavlis, T.L.; Sisson, V.B.; Roeske, S.M.; Snee, L.W.

1996-01-01

The Border Ranges fault system of southern Alaska, the fundamental break between the arc basement and the forearc accretionary complex, is the boundary between the Peninsular-Alexander-Wrangellia terrane and the Chugach terrane. The fault system separates crystalline rocks of the Alexander terrane from metamorphic rocks of the Chugach terrane in Glacier Bay National Park. Mylonitic rocks in the zone record abundant evidence for dextral strike-slip motion along north-northwest-striking subvertical surfaces. Geochronologic data together with regional correlations of Chugach terrane rocks involved in the deformation constrain this movement between latest Cretaceous and Early Eocene (???50 Ma). These findings are in agreement with studies to the northwest and southeast along the Border Ranges fault system which show dextral strike-slip motion occurring between 58 and 50 Ma. Correlations between Glacier Bay plutons and rocks of similar ages elsewhere along the Border Ranges fault system suggest that as much as 700 km of dextral motion may have been accommodated by this structure. These observations are consistent with oblique convergence of the Kula plate during early Cenozoic and forearc slivering above an ancient subduction zone following late Mesozoic accretion of the Peninsular-Alexander-Wrangellia terrane to North America.

AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection

PubMed Central

Jin, Shan; Cui, Wen; Jin, Zhigang; Wang, Ying

2015-01-01

Wireless Sensor Networks (WSNs) have been utilized for node fault diagnosis in the fire detection field since the 1990s. However, the traditional methods have some problems, including complicated system structures, intensive computation needs, unsteady data detection and local minimum values. In this paper, a new diagnosis mechanism for WSN nodes is proposed, which is based on fuzzy theory and an Adaptive Fuzzy Discrete Hopfield Neural Network (AF-DHNN). First, the original status of each sensor over time is obtained with two features. One is the root mean square of the filtered signal (FRMS), the other is the normalized summation of the positive amplitudes of the difference spectrum between the measured signal and the healthy one (NSDS). Secondly, distributed fuzzy inference is introduced. The evident abnormal nodes’ status is pre-alarmed to save time. Thirdly, according to the dimensions of the diagnostic data, an adaptive diagnostic status system is established with a Fuzzy C-Means Algorithm (FCMA) and Sorting and Classification Algorithm to reducing the complexity of the fault determination. Fourthly, a Discrete Hopfield Neural Network (DHNN) with iterations is improved with the optimization of the sensors’ detected status information and standard diagnostic levels, with which the associative memory is achieved, and the search efficiency is improved. The experimental results show that the AF-DHNN method can diagnose abnormal WSN node faults promptly and effectively, which improves the WSN reliability. PMID:26193280

A nonlinear quality-related fault detection approach based on modified kernel partial least squares.

PubMed

Jiao, Jianfang; Zhao, Ning; Wang, Guang; Yin, Shen

2017-01-01

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

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.

FINDS: A fault inferring nonlinear detection system programmers manual, version 3.0

NASA Technical Reports Server (NTRS)

Lancraft, R. E.

1985-01-01

Detailed software documentation of the digital computer program FINDS (Fault Inferring Nonlinear Detection System) Version 3.0 is provided. FINDS is a highly modular and extensible computer program designed to monitor and detect sensor failures, while at the same time providing reliable state estimates. In this version of the program the FINDS methodology is used to detect, isolate, and compensate for failures in simulated avionics sensors used by the Advanced Transport Operating Systems (ATOPS) Transport System Research Vehicle (TSRV) in a Microwave Landing System (MLS) environment. It is intended that this report serve as a programmers guide to aid in the maintenance, modification, and revision of the FINDS software.

Cell boundary fault detection system

DOEpatents

Archer, Charles Jens [Rochester, MN; Pinnow, Kurt Walter [Rochester, MN; Ratterman, Joseph D [Rochester, MN; Smith, Brian Edward [Rochester, MN

2009-05-05

A method determines a nodal fault along the boundary, or face, of a computing cell. Nodes on adjacent cell boundaries communicate with each other, and the communications are analyzed to determine if a node or connection is faulty.

Analytic Confusion Matrix Bounds for Fault Detection and Isolation Using a Sum-of-Squared- Residuals Approach

NASA Technical Reports Server (NTRS)

Simon, Dan; Simon, Donald L.

2009-01-01

Given a system which can fail in 1 or n different ways, a fault detection and isolation (FDI) algorithm uses sensor data in order to determine which fault is the most likely to have occurred. The effectiveness of an FDI algorithm can be quantified by a confusion matrix, which i ndicates the probability that each fault is isolated given that each fault has occurred. Confusion matrices are often generated with simulation data, particularly for complex systems. In this paper we perform FDI using sums of squares of sensor residuals (SSRs). We assume that the sensor residuals are Gaussian, which gives the SSRs a chi-squared distribution. We then generate analytic lower and upper bounds on the confusion matrix elements. This allows for the generation of optimal sensor sets without numerical simulations. The confusion matrix bound s are verified with simulated aircraft engine data.

Fault-tolerant cooperative output regulation for multi-vehicle systems with sensor faults

NASA Astrophysics Data System (ADS)

Qin, Liguo; He, Xiao; Zhou, D. H.

2017-10-01

This paper presents a unified framework of fault diagnosis and fault-tolerant cooperative output regulation (FTCOR) for a linear discrete-time multi-vehicle system with sensor faults. The FTCOR control law is designed through three steps. A cooperative output regulation (COR) controller is designed based on the internal mode principle when there are no sensor faults. A sufficient condition on the existence of the COR controller is given based on the discrete-time algebraic Riccati equation (DARE). Then, a decentralised fault diagnosis scheme is designed to cope with sensor faults occurring in followers. A residual generator is developed to detect sensor faults of each follower, and a bank of fault-matching estimators are proposed to isolate and estimate sensor faults of each follower. Unlike the current distributed fault diagnosis for multi-vehicle systems, the presented decentralised fault diagnosis scheme in each vehicle reduces the communication and computation load by only using the information of the vehicle. By combing the sensor fault estimation and the COR control law, an FTCOR controller is proposed. Finally, the simulation results demonstrate the effectiveness of the FTCOR controller.

Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis

NASA Astrophysics Data System (ADS)

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

2013-07-01

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

Cell boundary fault detection system

DOEpatents

Archer, Charles Jens [Rochester, MN; Pinnow, Kurt Walter [Rochester, MN; Ratterman, Joseph D [Rochester, MN; Smith, Brian Edward [Rochester, MN

2011-04-19

An apparatus and program product determine a nodal fault along the boundary, or face, of a computing cell. Nodes on adjacent cell boundaries communicate with each other, and the communications are analyzed to determine if a node or connection is faulty.

Two Trees: Migrating Fault Trees to Decision Trees for Real Time Fault Detection on International Space Station

NASA Technical Reports Server (NTRS)

Lee, Charles; Alena, Richard L.; Robinson, Peter

2004-01-01

We started from ISS fault trees example to migrate to decision trees, presented a method to convert fault trees to decision trees. The method shows that the visualizations of root cause of fault are easier and the tree manipulating becomes more programmatic via available decision tree programs. The visualization of decision trees for the diagnostic shows a format of straight forward and easy understands. For ISS real time fault diagnostic, the status of the systems could be shown by mining the signals through the trees and see where it stops at. The other advantage to use decision trees is that the trees can learn the fault patterns and predict the future fault from the historic data. The learning is not only on the static data sets but also can be online, through accumulating the real time data sets, the decision trees can gain and store faults patterns in the trees and recognize them when they come.

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Fault zone property near Xinfengjiang Reservoir using dense, across-fault seismic array

NASA Astrophysics Data System (ADS)

Lee, M. H. B.; Yang, H.; Sun, X.

2017-12-01

Properties of fault zones are important to the understanding of earthquake process. Around the fault zone is a damaged zone which is characterised by a lower seismic velocity. This is detectable as a low velocity zone and measure some physical property of the fault zone, which is otherwise difficult sample directly. A dense, across-fault array of short period seismometer is deployed on an inactive fault near Xinfengjiang Reservoir. Local events were manually picked. By computing the synthetic arrival time, we were able to constrain the parameters of the fault zone Preliminary result shows that the fault zone is around 350 m wide with a P and S velocity increase of around 10%. The fault is geologically inferred, and this result suggested that it may be a geological layer. The other possibility is that the higher velocity is caused by a combination of fault zone healing and fluid intrusion. Whilst the result was not able to tell us the nature of the fault, it demonstrated that this method is able to derive properties from a fault zone.

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).

A Virtual Sensor for Online Fault Detection of Multitooth-Tools

PubMed Central

Bustillo, Andres; Correa, Maritza; Reñones, Anibal

2011-01-01

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

A windowing and mapping strategy for gear tooth fault detection of a planetary gearbox

NASA Astrophysics Data System (ADS)

Liang, Xihui; Zuo, Ming J.; Liu, Libin

2016-12-01

When there is a single cracked tooth in a planet gear, the cracked tooth is enmeshed for very short time duration in comparison to the total time of a full revolution of the planet gear. The fault symptom generated by the single cracked tooth may be very weak. This study aims to develop a windowing and mapping strategy to interpret the vibration signal of a planetary gear at the tooth level. The fault symptoms generated by a single cracked tooth of the planet gear of interest can be extracted. The health condition of the planet gear can be assessed by comparing the differences among the signals of all teeth of the planet gear. The proposed windowing and mapping strategy is tested with both simulated vibration signals and experimental vibration signals. The tooth signals can be successfully decomposed and a single tooth fault on a planet gear can be effectively detected.

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

Fast computation of the kurtogram for the detection of transient faults

NASA Astrophysics Data System (ADS)

Antoni, Jérôme

2007-01-01

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

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

PubMed Central

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

2014-01-01

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

Fault detection for singular switched linear systems with multiple time-varying delay in finite frequency domain

NASA Astrophysics Data System (ADS)

Zhai, Ding; Lu, Anyang; Li, Jinghao; Zhang, Qingling

2016-10-01

This paper deals with the problem of the fault detection (FD) for continuous-time singular switched linear systems with multiple time-varying delay. In this paper, the actuator fault is considered. Besides, the systems faults and unknown disturbances are assumed in known frequency domains. Some finite frequency performance indices are initially introduced to design the switched FD filters which ensure that the filtering augmented systems under switching signal with average dwell time are exponentially admissible and guarantee the fault input sensitivity and disturbance robustness. By developing generalised Kalman-Yakubovic-Popov lemma and using Parseval's theorem and Fourier transform, finite frequency delay-dependent sufficient conditions for the existence of such a filter which can guarantee the finite-frequency H- and H∞ performance are derived and formulated in terms of linear matrix inequalities. Four examples are provided to illustrate the effectiveness of the proposed finite frequency method.

Ground Deformation near active faults in the Kinki district, southwest Japan, detected by InSAR

NASA Astrophysics Data System (ADS)

Hashimoto, M.; Ozawa, T.

2016-12-01

The Kinki district, southwest Japan, consists of ranges and plains between which active faults reside. The Osaka plain is in the middle of this district and is surrounded by the Rokko, Arima-Takatsuki, Ikoma, Kongo and Median Tectonic Line fault zones in the clockwise order. These faults are considered to be capable to generate earthquakes of larger magnitude than 7. The 1995 Kobe earthquake is the most recent activity of the Rokko fault (NE-SW trending dextral fault). Therefore the monitoring of ground deformation with high spatial resolution is essential to evaluate seismic hazards in this area. We collected and analyzed available SAR images such as ERS-1/2, Envisat, JERS-1, TerraSAR-X, ALOS/PALSAR and ALOS-2/PALSAR-2 to reveal ground deformation during these 20 years. We made DInSAR and PSInSAR analyses of these images using ASTER-GDEM ver.2. We detected three spots of subsidence along the Arima-Takatsuki fault (ENE-WSW trending dextral fault, east neighbor of the Rokko fault) after the Kobe earthquake, which continued up to 2010. Two of them started right after the Kobe earthquake, while the easternmost one was observed after 2000. However, we did not find them in the interferograms of ALOS-2/PALSAR-2 acquired during 2014 - 2016. Marginal uplift was recognized along the eastern part of the Rokko fault. PS-InSAR results of ALOS/PALSAR also revealed slight uplift north of the Rokko Mountain that uplift by 20 cm coseismically. These observations suggest that the Rokko Mountain might have uplifted during the postseismic period. We found subsidence on the eastern frank of the Kongo Mountain, where the Kongo fault (N-S trending reverse fault) exits. In the southern neighbor of the Median Tectonic Line (ENE-WSW trending dextral fault), uplift of > 5 mm/yr was found by Envisat and ALOS/PALSAR images. This area is shifted westward by 4 mm/yr as well. Since this area is located east of a seismically active area in the northwestern Wakayama prefecture, this deformation

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

NASA Technical Reports Server (NTRS)

Smyth, Padhraic

1994-01-01

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

Early Detection | Division of Cancer Prevention

Cancer.gov

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Incipient fault detection and power system protection for spaceborne systems

NASA Technical Reports Server (NTRS)

Russell, B. Don; Hackler, Irene M.

1987-01-01

A program was initiated to study the feasibility of using advanced terrestrial power system protection techniques for spacecraft power systems. It was designed to enhance and automate spacecraft power distribution systems in the areas of safety, reliability and maintenance. The proposed power management/distribution system is described as well as security assessment and control, incipient and low current fault detection, and the proposed spaceborne protection system. It is noted that the intelligent remote power controller permits the implementation of digital relaying algorithms with both adaptive and programmable characteristics.

Crustal Strain Patterns in Magmatic and Amagmatic Early Stage Rifts: Border Faults, Magma Intrusion, and Volatiles

NASA Astrophysics Data System (ADS)

Ebinger, C. J.; Keir, D.; Roecker, S. W.; Tiberi, C.; Aman, M.; Weinstein, A.; Lambert, C.; Drooff, C.; Oliva, S. J. C.; Peterson, K.; Bourke, J. R.; Rodzianko, A.; Gallacher, R. J.; Lavayssiere, A.; Shillington, D. J.; Khalfan, M.; Mulibo, G. D.; Ferdinand-Wambura, R.; Palardy, A.; Albaric, J.; Gautier, S.; Muirhead, J.; Lee, H.

2015-12-01

migration may be critical to strength reduction of initially cold, strong cratonic lithosphere. Our comparisons suggest that large offset border faults that develop very early in rift history create fluid pathways that maintain the initial along-axis segmentation until magma (if available), reaches mid-crustal levels.

Fault detection and diagnosis for refrigerator from compressor sensor

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

Keres, Stephen L.; Gomes, Alberto Regio; Litch, Andrew D.

A refrigerator, a sealed refrigerant system, and method are provided where the refrigerator includes at least a refrigerated compartment and a sealed refrigerant system including an evaporator, a compressor, a condenser, a controller, an evaporator fan, and a condenser fan. The method includes monitoring a frequency of the compressor, and identifying a fault condition in the at least one component of the refrigerant sealed system in response to the compressor frequency. The method may further comprise calculating a compressor frequency rate based upon the rate of change of the compressor frequency, wherein a fault in the condenser fan is identifiedmore » if the compressor frequency rate is positive and exceeds a condenser fan fault threshold rate, and wherein a fault in the evaporator fan is identified if the compressor frequency rate is negative and exceeds an evaporator fan fault threshold rate.« less

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

PubMed

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

2017-05-01

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

Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

NASA Astrophysics Data System (ADS)

Gadsden, S. Andrew; Kirubarajan, T.

2017-05-01

Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

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.

An imbalance fault detection method based on data normalization and EMD for marine current turbines.

PubMed

Zhang, Milu; Wang, Tianzhen; Tang, Tianhao; Benbouzid, Mohamed; Diallo, Demba

2017-05-01

This paper proposes an imbalance fault detection method based on data normalization and Empirical Mode Decomposition (EMD) for variable speed direct-drive Marine Current Turbine (MCT) system. The method is based on the MCT stator current under the condition of wave and turbulence. The goal of this method is to extract blade imbalance fault feature, which is concealed by the supply frequency and the environment noise. First, a Generalized Likelihood Ratio Test (GLRT) detector is developed and the monitoring variable is selected by analyzing the relationship between the variables. Then, the selected monitoring variable is converted into a time series through data normalization, which makes the imbalance fault characteristic frequency into a constant. At the end, the monitoring variable is filtered out by EMD method to eliminate the effect of turbulence. The experiments show that the proposed method is robust against turbulence through comparing the different fault severities and the different turbulence intensities. Comparison with other methods, the experimental results indicate the feasibility and efficacy of the proposed method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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.

Investigations into early rift development and geothermal resources in the Pyramid Lake fault zone, Western Nevada

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

Eisses, A.; Kell, A.; Kent, G.

A. K. Eisses, A. M. Kell, G. Kent, N. W. Driscoll, R. E. Karlin, R. L. Baskin, J. N. Louie, S. Pullammanappallil, 2010, Investigations into early rift development and geothermal resources in the Pyramid Lake fault zone, Western Nevada: Abstract T33C-2278 presented at 2010 Fall Meeting, AGU, San Francisco, Calif., 13-17 Dec.

Multi-directional fault detection system

DOEpatents

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

2010-11-23

An apparatus, program product and method checks for nodal faults in a group of nodes comprising a center node and all adjacent nodes. The center node concurrently communicates with the immediately adjacent nodes in three dimensions. The communications are analyzed to determine a presence of a faulty node or connection.

Multi-directional fault detection system

DOEpatents

Archer, Charles Jens [Rochester, MN; Pinnow, Kurt Walter [Rochester, MN; Ratterman, Joseph D [Rochester, MN; Smith, Brian Edward [Rochester, MN

2009-03-17

An apparatus, program product and method checks for nodal faults in a group of nodes comprising a center node and all adjacent nodes. The center node concurrently communicates with the immediately adjacent nodes in three dimensions. The communications are analyzed to determine a presence of a faulty node or connection.

Multi-directional fault detection system

DOEpatents

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

2010-06-29

An apparatus, program product and method checks for nodal faults in a group of nodes comprising a center node and all adjacent nodes. The center node concurrently communicates with the immediately adjacent nodes in three dimensions. The communications are analyzed to determine a presence of a faulty node or connection.

Acoustic Detection of Faults and Degradation in a High-Bypass Turbofan Engine during VIPR Phase III Testing

NASA Technical Reports Server (NTRS)

Boyle, Devin K.

2017-01-01

The Vehicle Integrated Propulsion Research (VIPR) Phase III project was executed at Edwards Air Force Base, California, by the National Aeronautics and Space Administration and several industry, academic, and government partners in the summer of 2015. One of the research objectives was to use external radial acoustic microphone arrays to detect changes in the noise characteristics produced by the research engine during volcanic ash ingestion and seeded fault insertion scenarios involving bleed air valves. Preliminary results indicate the successful acoustic detection of suspected degradation as a result of cumulative exposure to volcanic ash. This detection is shown through progressive changes, particularly in the high-frequency content, as a function of exposure to greater cumulative quantities of ash. Additionally, detection of the simulated failure of the 14th stage stability bleed valve and, to a lesser extent, the station 2.5 stability bleed valve, to their fully-open fail-safe positions was achieved by means of spectral comparisons between nominal (normal valve operation) and seeded fault scenarios.

Marketability requirements for fault detection and diagnostics in commercial buildings

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

Heinemeier, K.H.

Fault Detection and Diagnostics (FDD) is a technology that has a great potential for improving performance and reducing energy consumed in commercial buildings, and is rapidly becoming feasible for the buildings sector. Scientists have developed algorithms for FDD, and are making plans for field-testing and demonstration of these methods in real buildings. These efforts will provide a sound technical basis for FDD product offerings. FDD has the potential to dramatically improve the quality of operation of buildings. However, progress on technical issues is only one step towards implementing FDD in the market. FDD cannot be expected to have a majormore » impact on buildings unless market issues are addressed. Many questions will have to be answered regarding the users of FDD systems, the usability of the product, the market for FDD, and the nature of possible FDD offerings. It is crucial to consider marketing issues in parallel with the more technical issues. Constraints and opportunities that will be faced in marketing the products must be recognized early in technology development, and addressed and integrated into designs to ensure an appropriate system design. This paper identified a number of key questions that will arise in addressing marketability issues. These questions will have to be answered individually by technology developers and entities intending to market FDD. This paper presents some of the considerations that must go into the answering the questions, and provides a framework for analyzing the market requirements.« less

Detection of faults and software reliability analysis

NASA Technical Reports Server (NTRS)

Knight, John C.

1987-01-01

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

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.

Early Detection of Sporadic Pancreatic Cancer

PubMed Central

Kenner, Barbara J.; Chari, Suresh T.; Cleeter, Deborah F.; Go, Vay Liang W.

2015-01-01

Abstract Innovation leading to significant advances in research and subsequent translation to clinical practice is urgently necessary in early detection of sporadic pancreatic cancer. Addressing this need, the Early Detection of Sporadic Pancreatic Cancer Summit Conference was conducted by Kenner Family Research Fund in conjunction with the 2014 American Pancreatic Association and Japan Pancreas Society Meeting. International interdisciplinary scientific representatives engaged in strategic facilitated conversations based on distinct areas of inquiry: Case for Early Detection: Definitions, Detection, Survival, and Challenges; Biomarkers for Early Detection; Imaging; and Collaborative Studies. Ideas generated from the summit have led to the development of a Strategic Map for Innovation built upon 3 components: formation of an international collaborative effort, design of an actionable strategic plan, and implementation of operational standards, research priorities, and first-phase initiatives. Through invested and committed efforts of leading researchers and institutions, philanthropic partners, government agencies, and supportive business entities, this endeavor will change the future of the field and consequently the survival rate of those diagnosed with pancreatic cancer. PMID:25938853

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.

Stafford fault system: 120 million year fault movement history of northern Virginia

USGS Publications Warehouse

Powars, David S.; Catchings, Rufus D.; Horton, J. Wright; Schindler, J. Stephen; Pavich, Milan J.

2015-01-01

The Stafford fault system, located in the mid-Atlantic coastal plain of the eastern United States, provides the most complete record of fault movement during the past ~120 m.y. across the Virginia, Washington, District of Columbia (D.C.), and Maryland region, including displacement of Pleistocene terrace gravels. The Stafford fault system is close to and aligned with the Piedmont Spotsylvania and Long Branch fault zones. The dominant southwest-northeast trend of strong shaking from the 23 August 2011, moment magnitude Mw 5.8 Mineral, Virginia, earthquake is consistent with the connectivity of these faults, as seismic energy appears to have traveled along the documented and proposed extensions of the Stafford fault system into the Washington, D.C., area. Some other faults documented in the nearby coastal plain are clearly rooted in crystalline basement faults, especially along terrane boundaries. These coastal plain faults are commonly assumed to have undergone relatively uniform movement through time, with average slip rates from 0.3 to 1.5 m/m.y. However, there were higher rates during the Paleocene–early Eocene and the Pliocene (4.4–27.4 m/m.y), suggesting that slip occurred primarily during large earthquakes. Further investigation of the Stafford fault system is needed to understand potential earthquake hazards for the Virginia, Maryland, and Washington, D.C., area. The combined Stafford fault system and aligned Piedmont faults are ~180 km long, so if the combined fault system ruptured in a single event, it would result in a significantly larger magnitude earthquake than the Mineral earthquake. Many structures most strongly affected during the Mineral earthquake are along or near the Stafford fault system and its proposed northeastward extension.

Identifiability of Additive Actuator and Sensor Faults by State Augmentation

NASA Technical Reports Server (NTRS)

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

2014-01-01

A class of fault detection and identification (FDI) methods for bias-type actuator and sensor faults is explored in detail from the point of view of fault identifiability. The methods use state augmentation along with banks of Kalman-Bucy filters for fault detection, fault pattern determination, and fault value estimation. A complete characterization of conditions for identifiability of bias-type actuator faults, sensor faults, and simultaneous actuator and sensor faults is presented. It is shown that FDI of simultaneous actuator and sensor faults is not possible using these methods when all sensors have unknown biases. The fault identifiability conditions are demonstrated via numerical examples. The analytical and numerical results indicate that caution must be exercised to ensure fault identifiability for different fault patterns when using such methods.

Microearthquake detection at 2012 M4.9 Qiaojia earthquake source area , the north of the Xiaojiang Fault in Yunnan, China

NASA Astrophysics Data System (ADS)

Li, Y.; Yang, H.; Zhou, S.; Yan, C.

2016-12-01

We perform a comprehensive analysis in Yunnan area based on continuous seismic data of 38 stations of Qiaojia Network in Xiaojiang Fault from 2012.3 to 2015.2. We use an effective method: Match and Locate (M&L, Zhang&Wen, 2015) to detect and locate microearthquakes to conduct our research. We first study dynamic triggering around the Xiaojiang Fault in Yunnan. The triggered earthquakes are identified as two impulsive seismic arrivals in 2Hz-highpass-filtered velocity seismograms during the passage of surface waves of large teleseismic earthquakes. We only find two earthquakes that may have triggered regional earthquakes through inspecting their spectrograms: Mexico Mw7.4 earthquake in 03/20/2012 and El Salvador Mw7.3 earthquake in 10/14/2014. To confirm the two earthquakes are triggered instead of coincidence, we use M&L to search if there are any repeating earthquakes. The result of the coefficients shows that it is a coincidence during the surface waves of El Salvador earthquake and whether 2012 Mexico have triggered earthquake is under discussion. We then visually inspect the 2-8Hz-bandpass-filterd velocity envelopes of these years to search for non-volcanic tremor. We haven't detected any signals similar to non-volcanic tremors yet. In the following months, we are going to study the 2012 M4.9 Qiaojia earthquake. It occurred only 30km west of the epicenter of the 2014 M6.5 Ludian earthquake. We use Match and Locate (M&L) technique to detect and relocate microearthquakes that occurred 2 days before and 3 days after the mainshock. Through this, we could obtain several times more events than listed in the catalogs provided by NEIC and reduce the magnitude of completeness Mc. We will also detect microearthquakes along Xiaojiang Fault using template earthquakes listed in the catalogs to learn more about fault shape and other properties of Xiaojiang Fault. Analyzing seismicity near Xiaojiang Fault systematically may cast insight on our understanding of the features of

Fault-Detection Tool Has Companies 'Mining' Own Business

NASA Technical Reports Server (NTRS)

2005-01-01

A successful launching of NASA's Space Shuttle hinges heavily on the three Space Shuttle Main Engines (SSME) that power the orbiter. These critical components must be monitored in real time, with sensors, and compared against expected behaviors that could scrub a launch or, even worse, cause in- flight hazards. Since 1981, SSME faults have caused 23 scrubbed launches and 29 percent of total Space Shuttle downtime, according to a compilation of analysis reports. The most serious cases typically occur in the last few seconds before ignition; a launch scrub that late in the countdown usually means a period of investigation of a month or more. For example, during the launch attempt of STS-41D in 1984, an anomaly was detected in the number three engine, causing the mission to be scrubbed at T-4 seconds. This not only affected STS-41D, but forced the cancellation of another mission and caused a 2-month flight delay. In 2002, NASA s Kennedy Space Center, the Florida Institute of Technology, and Interface & Control Systems, Inc., worked together to attack this problem by creating a system that could automate the detection of mechanical failures in the SSMEs fuel control valves.

Fault Injection Campaign for a Fault Tolerant Duplex Framework

NASA Technical Reports Server (NTRS)

Sacco, Gian Franco; Ferraro, Robert D.; von llmen, Paul; Rennels, Dave A.

2007-01-01

Fault tolerance is an efficient approach adopted to avoid or reduce the damage of a system failure. In this work we present the results of a fault injection campaign we conducted on the Duplex Framework (DF). The DF is a software developed by the UCLA group [1, 2] that uses a fault tolerant approach and allows to run two replicas of the same process on two different nodes of a commercial off-the-shelf (COTS) computer cluster. A third process running on a different node, constantly monitors the results computed by the two replicas, and eventually restarts the two replica processes if an inconsistency in their computation is detected. This approach is very cost efficient and can be adopted to control processes on spacecrafts where the fault rate produced by cosmic rays is not very high.

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.

Liquefaction along Late Pleistocene to early Holocene Faults as Revealed by Lidar in Northwest Tasmania, Australia

NASA Astrophysics Data System (ADS)

Webb, J.; Gardner, T.

2016-12-01

In northwest Tasmania well-preserved mid-Holocene beach ridges with maximum radiocarbon ages of 5.25 ka occur along the coast; inland are a parallel set of lower relief beach ridges of probable MIS 5e age. The latter are cut by northeast-striking faults clearly visible on LIDAR images, with a maximum vertical displacement (evident as difference in topographic elevation) of 3 m. Also distinct on the LIDAR images are large sand boils along the fault lines; they are up to 5 m in diameter and 2-3 m high and mostly occur on the hanging wall close to the fault traces. Without LIDAR it would have been almost impossible to distinguish either the fault scarps or the sand boils. Excavations through the sand boils show that they are massive, with no internal structure, suggesting that they formed in a single event. They are composed of well-sorted, very fine white sand, identical to the sand in the underlying beach ridges. The sand boils overlie a peaty paleosol; this formed in the tea-tree swamp that formerly covered the area, and has been offset along the faults. Radiocarbon dating of the buried organic-rich paleosol gave ages of 14.8-7.2 ka, suggesting that the faulting is latest Pleistocene to early Holocene in age; it occurred prior to deposition of the mid-Holocene beach ridges, which are not offset. The beach ridge sediments are up to 7 m thick and contain an iron-cemented hard pan 1-3 m below the surface. The water table is very shallow and close to the ground surface, so the sands of the beach ridges are mostly saturated. During faulting these sands experienced extensive liquefaction. The resulting sand boils rose to a substantial height of 2-3 m, probably possibly reflecting the elevation of the potentiometric surface within the confined part of the beach ridge sediments below the iron-cemented hard pan. Motion on the faults was predominantly dip slip (shown by an absence of horizontal offset) and probably reverse, which is consistent with the present-day northwest

Reconnaissance study of late quaternary faulting along cerro GoDen fault zone, western Puerto Rico

USGS Publications Warehouse

Mann, P.; Prentice, C.S.; Hippolyte, J.-C.; Grindlay, N.R.; Abrams, L.J.; Lao-Davila, D.

2005-01-01

The Cerro GoDen fault zone is associated with a curvilinear, continuous, and prominent topographic lineament in western Puerto Rico. The fault varies in strike from northwest to west. In its westernmost section, the fault is ???500 m south of an abrupt, curvilinear mountain front separating the 270- to 361-m-high La CaDena De San Francisco range from the Rio A??asco alluvial valley. The Quaternary fault of the A??asco Valley is in alignment with the bedrock fault mapped by D. McIntyre (1971) in the Central La Plata quadrangle sheet east of A??asco Valley. Previous workers have postulated that the Cerro GoDen fault zone continues southeast from the A??asco Valley and merges with the Great Southern Puerto Rico fault zone of south-central Puerto Rico. West of the A??asco Valley, the fault continues offshore into the Mona Passage (Caribbean Sea) where it is characterized by offsets of seafloor sediments estimated to be of late Quaternary age. Using both 1:18,500 scale air photographs taken in 1936 and 1:40,000 scale photographs taken by the U.S. Department of Agriculture in 1986, we iDentified geomorphic features suggestive of Quaternary fault movement in the A??asco Valley, including aligned and Deflected drainages, apparently offset terrace risers, and mountain-facing scarps. Many of these features suggest right-lateral displacement. Mapping of Paleogene bedrock units in the uplifted La CaDena range adjacent to the Cerro GoDen fault zone reveals the main tectonic events that have culminated in late Quaternary normal-oblique displacement across the Cerro GoDen fault. Cretaceous to Eocene rocks of the La CaDena range exhibit large folds with wavelengths of several kms. The orientation of folds and analysis of fault striations within the folds indicate that the folds formed by northeast-southwest shorTening in present-day geographic coordinates. The age of Deformation is well constrained as late Eocene-early Oligocene by an angular unconformity separating folDed, Deep

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.

Life expectancy and the value of early detection.

PubMed

Howard, David H

2005-09-01

This paper presents a model of the benefits and costs of early detection of asymptomatic disease as they vary by age. The benefits of early detection tend toward zero as the risk of death from competing causes increases. Costs per detected case also decline with age, assuming that disease incidence rises with age, but are always strictly positive. On balance, there is always an age limit beyond which the costs associated with early detection outweigh the benefits. Application of the model to prostate cancer screening suggests that early detection above age 70 or so is not cost-effective.

Fault-scale controls on rift geometry: the Bilila-Mtakataka Fault, Malawi

NASA Astrophysics Data System (ADS)

Hodge, M.; Fagereng, A.; Biggs, J.; Mdala, H. S.

2017-12-01

Border faults that develop during initial stages of rifting determine the geometry of rifts and passive margins. At outcrop and regional scales, it has been suggested that border fault orientation may be controlled by reactivation of pre-existing weaknesses. Here, we perform a multi-scale investigation on the influence of anisotropic fabrics along a major developing border fault in the southern East African Rift, Malawi. The 130 km long Bilila-Mtakataka fault has been proposed to have slipped in a single MW 8 earthquake with 10 m of normal displacement. The fault is marked by an 11±7 m high scarp with an average trend that is oblique to the current plate motion. Variations in scarp height are greatest at lithological boundaries and where the scarp switches between following and cross-cutting high-grade metamorphic foliation. Based on the scarp's geometry and morphology, we define 6 geometrically distinct segments. We suggest that the segments link to at least one deeper structure that strikes parallel to the average scarp trend, an orientation consistent with the kinematics of an early phase of rift initiation. The slip required on a deep fault(s) to match the height of the current scarp suggests multiple earthquakes along the fault. We test this hypothesis by studying the scarp morphology using high-resolution satellite data. Our results suggest that during the earthquake(s) that formed the current scarp, the propagation of the fault toward the surface locally followed moderately-dipping foliation well oriented for reactivation. In conclusion, although well oriented pre-existing weaknesses locally influence shallow fault geometry, large-scale border fault geometry appears primarily controlled by the stress field at the time of fault initiation.

Early detection: the impact of genomics.

PubMed

van Lanschot, M C J; Bosch, L J W; de Wit, M; Carvalho, B; Meijer, G A

2017-08-01

The field of genomics has shifted our view on disease development by providing insights in the molecular and functional processes encoded in the genome. In the case of cancer, many alterations in the DNA accumulate that enable tumor growth or even metastatic dissemination. Identification of molecular signatures that define different stages of progression towards cancer can enable early tumor detection. In this review, the impact of genomics will be addressed using early detection of colorectal cancer (CRC) as an example. Increased understanding of the adenoma-to-carcinoma progression has led to the discovery of several diagnostic biomarkers. This combined with technical advancements, has facilitated the development of molecular tests for non-invasive early CRC detection in stool and blood samples. Even though several tests have already made it to clinical practice, sensitivity and specificity for the detection of precancerous lesions still need improvement. Besides the diagnostic qualities, also the accuracy of the intermediate endpoint is an important issue on how the effectiveness of a novel test is perceived. Here, progression biomarkers may provide a more precise measure than the currently used morphologically based features. Similar developments in biomarker use for early detection have taken place in other cancer types.

Fault Detection/Isolation Verification,

DTIC Science & Technology

1982-08-01

63 - A I MCC ’I UNCLASSIFIED SECURITY CLASSIPICATION OP THIS PAGE tMh*f Dal f&mered, REPORT D00CUMENTATION PAGE " .O ORM 1. REPORT NUM.9ft " 2. GOVT...test the performance of th .<ver) DO 2" 1473 EoIoTON OP iNov os i OSoLTe UNCLASSIFIED SECURITY CLASSIPICATION 0 T"IS PAGE (P 3 . at Sted) I...UNCLASSIFIED Acumy, C .AMICATIN Of THIS PAGS. (m ... DO&.m , Algorithm on these netowrks , several different fault scenarios were designed for each network. Each

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.

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.

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.

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.

3D seismic detection of shallow faults and fluid migration pathways offshore Southern Costa Rica: Application of neural-network meta-attributes

NASA Astrophysics Data System (ADS)

Kluesner, J. W.; Silver, E. A.; Nale, S. M.; Bangs, N. L.; McIntosh, K. D.

2013-12-01

We employ a seismic meta-attribute workflow to detect and analyze probable faults and fluid-pathways in 3D within the sedimentary section offshore Southern Costa Rica. During the CRISP seismic survey in 2011 we collected an 11 x 55 km grid of 3D seismic reflection data and high-resolvability EM122 multibeam data, with coverage extending from the incoming plate to the outer-shelf. We mapped numerous seafloor seep indicators, with distributions ranging from the lower-slope to ~15 km landward of the shelf break [Kluesner et al., 2013, G3, doi:10.1002/ggge.20058; Silver et al., this meeting]. We used the OpendTect software package to calculate meta-attribute volumes from the 3D seismic data in order to detect and visualize seismic discontinuities in 3D. This methodology consists of dip-steered filtering to pre-condition the data, followed by combining a set of advanced dip-steered seismic attributes into a single object probability attribute using a user-trained neural-network pattern-recognition algorithm. The parameters of the advanced seismic attributes are set for optimal detection of the desired geologic discontinuity (e.g. faults or fluid-pathways). The product is a measure of probability for the desired target that ranges between 0 and 1, with 1 representing the highest probability. Within the sedimentary section of the CRISP survey the results indicate focused fluid-migration pathways along dense networks of intersecting normal faults with approximately N-S and E-W trends. This pattern extends from the middle slope to the outer-shelf region. Dense clusters of fluid-migration pathways are located above basement highs and deeply rooted reverse faults [see Bangs et al., this meeting], including a dense zone of fluid-pathways imaged below IODP Site U1413. In addition, fault intersections frequently show an increased signal of fluid-migration and these zones may act as major conduits for fluid-flow through the sedimentary cover. Imaged fluid pathways root into high

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.

Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection

NASA Astrophysics Data System (ADS)

McDonald, Geoff L.; Zhao, Qing

2017-01-01

Minimum Entropy Deconvolution (MED) has been applied successfully to rotating machine fault detection from vibration data, however this method has limitations. A convolution adjustment to the MED definition and solution is proposed in this paper to address the discontinuity at the start of the signal - in some cases causing spurious impulses to be erroneously deconvolved. A problem with the MED solution is that it is an iterative selection process, and will not necessarily design an optimal filter for the posed problem. Additionally, the problem goal in MED prefers to deconvolve a single-impulse, while in rotating machine faults we expect one impulse-like vibration source per rotational period of the faulty element. Maximum Correlated Kurtosis Deconvolution was proposed to address some of these problems, and although it solves the target goal of multiple periodic impulses, it is still an iterative non-optimal solution to the posed problem and only solves for a limited set of impulses in a row. Ideally, the problem goal should target an impulse train as the output goal, and should directly solve for the optimal filter in a non-iterative manner. To meet these goals, we propose a non-iterative deconvolution approach called Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA). MOMEDA proposes a deconvolution problem with an infinite impulse train as the goal and the optimal filter solution can be solved for directly. From experimental data on a gearbox with and without a gear tooth chip, we show that MOMEDA and its deconvolution spectrums according to the period between the impulses can be used to detect faults and study the health of rotating machine elements effectively.

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.

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.

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

PubMed

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

2016-08-19

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.

Jurassic faults of southwest Alabama and offshore areas

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

Mink, R.M.; Tew, B.H.; Bearden, B.L.

1991-03-01

Four fault groups affecting Jurassic strata occur in the southwest and offshore Alabama areas. They include the regional basement rift trend, the regional peripheral fault trend, the Mobile graben fault system, and the Lower Mobile Bay fault system. The regional basement system rift and regional peripheral fault trends are distinct and rim the inner margin of the eastern Gulf Coastal Plain. The regional basement rift trend is genetically related to the breakup of Pangea and the opening of the Gulf of Mexico in the Late Triassic-Early Jurassic. This fault trend is thought to have formed contemporaneously with deposition of Latemore » Triassic-Early Jurassic Eagle Mills Formation and to displace pre-Mesozoic rocks. The regional peripheral fault trend consists of a group of en echelon extensional faults that are parallel or subparallel to regional strike of Gulf Coastal Plain strata and correspond to the approximate updip limit of thick Louann Salt. Nondiapiric salt features are associated with the trend and maximum structural development is exhibited in the Haynesville-Smackover section. No hydrocarbon accumulations have been documented in the pre-Jurassic strata of southwest and offshore Alabama. Productive hydrocarbon reservoirs occur in Jurassic strata along the trends of the fault groups, suggesting a significant relationship between structural development in the Jurassic and hydrocarbon accumulation. Hydrocarbon traps are generally structural or contain a major structural component and include salt anticlines, faulted salt anticlines, and extensional fault traps. All of the major hydrocarbon accumulations are associated with movement of the Louann Salt along the regional peripheral fault trend, the Mobile graben fault system, or the Lower Mobile Bay fault system.« less

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.

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.

Detection of postseismic fault-zone collapse following the Landers earthquake

USGS Publications Warehouse

Massonnet, D.; Thatcher, W.; Vadon, H.

1996-01-01

Stress changes caused by fault movement in an earthquake induce transient aseismic crustal movements in the earthquake source region that continue for months to decades following large events. These motions reflect aseismic adjustments of the fault zone and/or bulk deformation of the surroundings in response to applied stresses, and supply information regarding the inelastic behaviour of the Earth's crust. These processes are imperfectly understood because it is difficult to infer what occurs at depth using only surface measurements, which are in general poorly sampled. Here we push satellite radar interferometry to near its typical artefact level, to obtain a map of the postseismic deformation field in the three years following the 28 June 1992 Landers, California earthquake. From the map, we deduce two distinct types of deformation: afterslip at depth on the fault that ruptured in the earthquake, and shortening normal to the fault zone. The latter movement may reflect the closure of dilatant cracks and fluid expulsion from a transiently over-pressured fault zone.

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.

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.

Early detection of sporadic pancreatic cancer: summative review.

PubMed

Chari, Suresh T; Kelly, Kimberly; Hollingsworth, Michael A; Thayer, Sarah P; Ahlquist, David A; Andersen, Dana K; Batra, Surinder K; Brentnall, Teresa A; Canto, Marcia; Cleeter, Deborah F; Firpo, Matthew A; Gambhir, Sanjiv Sam; Go, Vay Liang W; Hines, O Joe; Kenner, Barbara J; Klimstra, David S; Lerch, Markus M; Levy, Michael J; Maitra, Anirban; Mulvihill, Sean J; Petersen, Gloria M; Rhim, Andrew D; Simeone, Diane M; Srivastava, Sudhir; Tanaka, Masao; Vinik, Aaron I; Wong, David

2015-07-01

Pancreatic cancer (PC) is estimated to become the second leading cause of cancer death in the United States by 2020. Early detection is the key to improving survival in PC. Addressing this urgent need, the Kenner Family Research Fund conducted the inaugural Early Detection of Sporadic Pancreatic Cancer Summit Conference in 2014 in conjunction with the 45th Anniversary Meeting of the American Pancreatic Association and Japan Pancreas Society. This seminal convening of international representatives from science, practice, and clinical research was designed to facilitate challenging interdisciplinary conversations to generate innovative ideas leading to the creation of a defined collaborative strategic pathway for the future of the field. An in-depth summary of current efforts in the field, analysis of gaps in specific areas of expertise, and challenges that exist in early detection is presented within distinct areas of inquiry: Case for Early Detection: Definitions, Detection, Survival, and Challenges; Biomarkers for Early Detection; Imaging; and Collaborative Studies. In addition, an overview of efforts in familial PC is presented in an addendum to this article. It is clear from the summit deliberations that only strategically designed collaboration among investigators, institutions, and funders will lead to significant progress in early detection of sporadic PC.

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.

Gastrointestinal Cancers: Screening and Early Detection.

PubMed

Griffin-Sobel, Joyce P

2017-05-01

To present an overview of current practices in the screening and early detection of gastrointestinal cancers. Literature reviews. Screening for gastrointestinal cancers is less than desirable, particularly in underserved populations. There are inadequate methods of screening for early detection of esophageal and gastric cancers. Education of patients is needed to reinforce the importance of screening for gastrointestinal cancers. Copyright © 2017 Elsevier Inc. All rights reserved.

Adaptive Fault Detection on Liquid Propulsion Systems with Virtual Sensors: Algorithms and Architectures

NASA Technical Reports Server (NTRS)

Matthews, Bryan L.; Srivastava, Ashok N.

2010-01-01

Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.

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.

Evidence for a Battle Mountain-Eureka crustal fault zone, north-central Nevada, and its relation to Neoproterozoic-Early Paleozoic continental breakup

USGS Publications Warehouse

Grauch, V.J.S.; Rodriguez, B.D.; Bankey, V.; Wooden, J.L.

2003-01-01

Combined evidence from gravity, radiogenic isotope, and magnetotelluric (MT) data indicates a crustal fault zone that coincides with the northwest-trending Battle Mountain-Eureka (BME) mineral trend in north-central Nevada, USA. The BME crustal fault zone likely originated during Neoproterozoic-Early Paleozoic rifting of the continent and had a large influence on subsequent tectonic events, such as emplacement of allochthons and episodic deformation, magmatism, and mineralization throughout the Phanerozoic. MT models show the fault zone is about 10 km wide, 130-km long, and extends from 1 to 5 km below the surface to deep crustal levels. Isotope data and gravity models imply the fault zone separates crust of fundamentally different character. Geophysical evidence for such a long-lived structure, likely inherited from continental breakup, defies conventional wisdom that structures this old have been destroyed by Cenozoic extensional processes. Moreover, the coincidence with the alignment of mineral deposits supports the assertion by many economic geologists that these alignments are indicators of buried regional structures.

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

Fault-Magma Interactions during Early Continental Rifting: Seismicity of the Magadi-Natron-Manyara basins, Africa

NASA Astrophysics Data System (ADS)

Weinstein, A.; Oliva, S. J.; Ebinger, C.; Aman, M.; Lambert, C.; Roecker, S. W.; Tiberi, C.; Muirhead, J.

2017-12-01

Although magmatism may occur during the earliest stages of continental rifting, its role in strain accommodation remains weakly constrained by largely 2D studies. We analyze seismicity data from a 13-month, 39-station broadband seismic array to determine the role of magma intrusion on state-of-stress and strain localization, and their along-strike variations. Precise earthquake locations using cluster analyses and a new 3D velocity model reveal lower crustal earthquakes along projections of steep border faults that degas CO2. Seismicity forms several disks interpreted as sills at 6-10 km below a monogenetic cone field. The sills overlie a lower crustal magma chamber that may feed eruptions at Oldoinyo Lengai volcano. After determining a new ML scaling relation, we determine a b-value of 0.87 ± 0.03. Focal mechanisms for 66 earthquakes, and a longer time period of relocated earthquakes from global arrays reveal an along-axis stress rotation of 50 o ( N150 oE) in the magmatically active zone. Using Kostrov summation of local and teleseismic mechanisms, we find opening directions of N122ºE and N92ºE north and south of the magmatically active zone. The stress rotation facilitates strain transfer from border fault systems, the locus of early stage deformation, to the zone of magma intrusion in the central rift. Our seismic, structural, and geochemistry results indicate that frequent lower crustal earthquakes are promoted by elevated pore pressures from volatile degassing along border faults, and hydraulic fracture around the margins of magma bodies. Earthquakes are largely driven by stress state around inflating magma bodies, and more dike intrusions with surface faulting, eruptions, and earthquakes are expected.

Particle Filters for Real-Time Fault Detection in Planetary Rovers

NASA Technical Reports Server (NTRS)

Dearden, Richard; Clancy, Dan; Koga, Dennis (Technical Monitor)

2001-01-01

Planetary rovers provide a considerable challenge for robotic systems in that they must operate for long periods autonomously, or with relatively little intervention. To achieve this, they need to have on-board fault detection and diagnosis capabilities in order to determine the actual state of the vehicle, and decide what actions are safe to perform. Traditional model-based diagnosis techniques are not suitable for rovers due to the tight coupling between the vehicle's performance and its environment. Hybrid diagnosis using particle filters is presented as an alternative, and its strengths and weakeners are examined. We also present some extensions to particle filters that are designed to make them more suitable for use in diagnosis problems.

Fault Detection and Safety in Closed-Loop Artificial Pancreas Systems

PubMed Central

2014-01-01

Continuous subcutaneous insulin infusion pumps and continuous glucose monitors enable individuals with type 1 diabetes to achieve tighter blood glucose control and are critical components in a closed-loop artificial pancreas. Insulin infusion sets can fail and continuous glucose monitor sensor signals can suffer from a variety of anomalies, including signal dropout and pressure-induced sensor attenuations. In addition to hardware-based failures, software and human-induced errors can cause safety-related problems. Techniques for fault detection, safety analyses, and remote monitoring techniques that have been applied in other industries and applications, such as chemical process plants and commercial aircraft, are discussed and placed in the context of a closed-loop artificial pancreas. PMID:25049365

Detection of postseismic fault-zone collapse following the Landers earthquake

NASA Astrophysics Data System (ADS)

Massonnet, Didier; Thatcher, Wayne; Vadon, Hélèna

1996-08-01

STRESS changes caused by fault movement in an earthquake induce transient aseismic crustal movements in the earthquake source region that continue for months to decades following large events1-4. These motions reflect aseismic adjustments of the fault zone and/or bulk deformation of the surroundings in response to applied stresses2,5-7, and supply information regarding the inelastic behaviour of the Earth's crust. These processes are imperfectly understood because it is difficult to infer what occurs at depth using only surface measurements2, which are in general poorly sampled. Here we push satellite radar interferometry to near its typical artefact level, to obtain a map of the postseismic deformation field in the three years following the 28 June 1992 Landers, California earthquake. From the map, we deduce two distinct types of deformation: afterslip at depth on the fault that ruptured in the earthquake, and shortening normal to the fault zone. The latter movement may reflect the closure of dilatant cracks and fluid expulsion from a transiently over-pressured fault zone6-8.

Fault reactivation: The Picuris-Pecos fault system of north-central New Mexico

NASA Astrophysics Data System (ADS)

McDonald, David Wilson

The PPFS is a N-trending fault system extending over 80 km in the Sangre de Cristo Mountains of northern New Mexico. Precambrian basement rocks are offset 37 km in a right-lateral sense; however, this offset includes dextral strike-slip (Precambrian), mostly normal dip-slip (Pennsylvanian), mostly reverse dip-slip (Early Laramide), limited strike-slip (Late Laramide) and mostly normal dip-slip (Cenozoic). The PPFS is broken into at least 3 segments by the NE-trending Embudo fault and by several Laramide age NW-trending tear faults. These segments are (from N to S): the Taos, the Picuris, and the Pecos segments. On the east side of the Picuris segment in the Picuris Mountains, the Oligocene-Miocene age Miranda graben developed and represents a complex extension zone south of the Embudo fault. Regional analysis of remotely sensed data and geologic maps indicate that lineaments subparallel to the trace of the PPFS are longer and less frequent than lineaments that trend orthogonal to the PPFS. Significant cross cutting faults and subtle changes in fault trends in each segment are clear in the lineament data. Detailed mapping in the eastern Picuris Mountains showed that the favorably oriented Picuris segment was not reactivated in the Tertiary development of the Rio Grande rift. Segmentation of the PPFS and post-Laramide annealing of the Picuris segment are interpreted to have resulted in the development of the subparallel La Serna fault. The Picuris segment of the PPFS is offset by several E-ESE trending faults. These faults are Late Cenozoic in age and interpreted to be related to the uplift of the Picuris Mountains and the continuing sinistral motion on the Embudo fault. Differential subsidence within the Miranda graben caused the development of several synthetic and orthogonal faults between the bounding La Serna and Miranda faults. Analysis of over 10,000 outcrop scale brittle structures reveals a strong correlation between faults and fracture systems. The dominant

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

Tools for Evaluating Fault Detection and Diagnostic Methods for HVAC Secondary Systems

NASA Astrophysics Data System (ADS)

Pourarian, Shokouh

Although modern buildings are using increasingly sophisticated energy management and control systems that have tremendous control and monitoring capabilities, building systems routinely fail to perform as designed. More advanced building control, operation, and automated fault detection and diagnosis (AFDD) technologies are needed to achieve the goal of net-zero energy commercial buildings. Much effort has been devoted to develop such technologies for primary heating ventilating and air conditioning (HVAC) systems, and some secondary systems. However, secondary systems, such as fan coil units and dual duct systems, although widely used in commercial, industrial, and multifamily residential buildings, have received very little attention. This research study aims at developing tools that could provide simulation capabilities to develop and evaluate advanced control, operation, and AFDD technologies for these less studied secondary systems. In this study, HVACSIM+ is selected as the simulation environment. Besides developing dynamic models for the above-mentioned secondary systems, two other issues related to the HVACSIM+ environment are also investigated. One issue is the nonlinear equation solver used in HVACSIM+ (Powell's Hybrid method in subroutine SNSQ). It has been found from several previous research projects (ASRHAE RP 825 and 1312) that SNSQ is especially unstable at the beginning of a simulation and sometimes unable to converge to a solution. Another issue is related to the zone model in the HVACSIM+ library of components. Dynamic simulation of secondary HVAC systems unavoidably requires an interacting zone model which is systematically and dynamically interacting with building surrounding. Therefore, the accuracy and reliability of the building zone model affects operational data generated by the developed dynamic tool to predict HVAC secondary systems function. The available model does not simulate the impact of direct solar radiation that enters a zone

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

Detection and localization of building insulation faults using optical-fiber DTS system

NASA Astrophysics Data System (ADS)

Papes, Martin; Liner, Andrej; Koudelka, Petr; Siska, Petr; Cubik, Jakub; Kepak, Stanislav; Jaros, Jakub; Vasinek, Vladimir

2013-05-01

Nowadays the trends in the construction industry are changing at an incredible speed. The new technologies are still emerging on the market. Sphere of building insulation is not an exception as well. One of the major problems in building insulation is usually its failure, whether caused by unwanted mechanical intervention or improper installation. The localization of these faults is quite difficult, often impossible without large intervention into the construction. As a proper solution for this problem might be utilization of Optical-Fiber DTS system based on stimulated Raman scattering. Used DTS system is primary designed for continuous measurement of the temperature along the optical fiber. This system is using standard optical fiber as a sensor, which brings several advantages in its application. First, the optical fiber is relatively inexpensive, which allows to cover a quite large area for a small cost. The other main advantages of the optical fiber are electromagnetic resistance, small size, safety operation in inflammable or explosive area, easy installation, etc. This article is dealing with the detection and localization of building insulation faults using mentioned system.

Fault detection in digital and analog circuits using an i(DD) temporal analysis technique

NASA Technical Reports Server (NTRS)

Beasley, J.; Magallanes, D.; Vridhagiri, A.; Ramamurthy, Hema; Deyong, Mark

1993-01-01

An i(sub DD) temporal analysis technique which is used to detect defects (faults) and fabrication variations in both digital and analog IC's by pulsing the power supply rails and analyzing the temporal data obtained from the resulting transient rail currents is presented. A simple bias voltage is required for all the inputs, to excite the defects. Data from hardware tests supporting this technique are presented.

Methods for Fault Detection, Diagnostics and Prognostics for Building Systems - A Review Part I

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

Katipamula, Srinivas; Brambley, Michael R.

This paper provides an overview of fault detection, diagnostics, and prognostics (FDD&P) starting with descriptions of the fundamental processes and some important definitions. This is followed by a review of FDD&P research in the HVAC&R field, and the paper concludes with discussions of the current state of applications in buildings and likely contributions to operating and maintaining buildings in the future.

Aftershocks illuminate the 2011 Mineral, Virginia, earthquake causative fault zone and nearby active faults