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.

Autonomous power expert system advanced development

NASA Technical Reports Server (NTRS)

Quinn, Todd M.; Walters, Jerry L.

1991-01-01

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

Fault detection 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).

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

DTIC Science & Technology

2014-12-21

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

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

NASA Technical Reports Server (NTRS)

Rinehart, Aidan W.; Simon, Donald L.

2015-01-01

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

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

NASA Technical Reports Server (NTRS)

Rinehart, Aidan W.; Simon, Donald L.

2014-01-01

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

Common faults and their impacts for rooftop air conditioners

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

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

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

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

PubMed Central

Swetapadma, Aleena; Yadav, Anamika

2015-01-01

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

Applications of Logic Coverage Criteria and Logic Mutation to Software Testing

ERIC Educational Resources Information Center

Kaminski, Garrett K.

2011-01-01

Logic is an important component of software. Thus, software logic testing has enjoyed significant research over a period of decades, with renewed interest in the last several years. One approach to detecting logic faults is to create and execute tests that satisfy logic coverage criteria. Another approach to detecting faults is to perform mutation…

Maneuver Classification for Aircraft Fault Detection

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.

2003-01-01

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, identifying all possible faulty and proper operating modes is clearly impossible. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.

Classification of Aircraft Maneuvers for Fault Detection

NASA Technical Reports Server (NTRS)

Oza, Nikunj; Tumer, Irem Y.; Tumer, Kagan; Huff, Edward M.; Koga, Dennis (Technical Monitor)

2002-01-01

Automated fault detection is an increasingly important problem in aircraft maintenance and operation. Standard methods of fault detection assume the availability of either data produced during all possible faulty operation modes or a clearly-defined means to determine whether the data provide a reasonable match to known examples of proper operation. In the domain of fault detection in aircraft, the first assumption is unreasonable and the second is difficult to determine. We envision a system for online fault detection in aircraft, one part of which is a classifier that predicts the maneuver being performed by the aircraft as a function of vibration data and other available data. To develop such a system, we use flight data collected under a controlled test environment, subject to many sources of variability. We explain where our classifier fits into the envisioned fault detection system as well as experiments showing the promise of this classification subsystem.

An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors

NASA Astrophysics Data System (ADS)

Li, De Z.; Wang, Wilson; Ismail, Fathy

2017-11-01

Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM air-gap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.

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

NASA Technical Reports Server (NTRS)

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

1985-01-01

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

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.

Hardware fault insertion and instrumentation system: Mechanization and validation

NASA Technical Reports Server (NTRS)

Benson, J. W.

1987-01-01

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

Advanced information processing system: Fault injection study and results

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

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.

A Novel Arc Fault Detector for Early Detection of Electrical Fires

PubMed Central

Yang, Kai; Zhang, Rencheng; Yang, Jianhong; Liu, Canhua; Chen, Shouhong; Zhang, Fujiang

2016-01-01

Arc faults can produce very high temperatures and can easily ignite combustible materials; thus, they represent one of the most important causes of electrical fires. The application of arc fault detection, as an emerging early fire detection technology, is required by the National Electrical Code to reduce the occurrence of electrical fires. However, the concealment, randomness and diversity of arc faults make them difficult to detect. To improve the accuracy of arc fault detection, a novel arc fault detector (AFD) is developed in this study. First, an experimental arc fault platform is built to study electrical fires. A high-frequency transducer and a current transducer are used to measure typical load signals of arc faults and normal states. After the common features of these signals are studied, high-frequency energy and current variations are extracted as an input eigenvector for use by an arc fault detection algorithm. Then, the detection algorithm based on a weighted least squares support vector machine is designed and successfully applied in a microprocessor. Finally, an AFD is developed. The test results show that the AFD can detect arc faults in a timely manner and interrupt the circuit power supply before electrical fires can occur. The AFD is not influenced by cross talk or transient processes, and the detection accuracy is very high. Hence, the AFD can be installed in low-voltage circuits to monitor circuit states in real-time to facilitate the early detection of electrical fires. PMID:27070618

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.

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.

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

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

NASA Technical Reports Server (NTRS)

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

1977-01-01

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

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.

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

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions.

PubMed

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-06-20

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions.

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

PubMed Central

Xue, Lang; Li, Naipeng; Lei, Yaguo; Li, Ningbo

2017-01-01

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. PMID:28773035

Automatic Detection of Electric Power Troubles (ADEPT)

NASA Technical Reports Server (NTRS)

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

1988-01-01

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

Automatic Detection of Electric Power Troubles (ADEPT)

NASA Astrophysics Data System (ADS)

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

1988-11-01

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

A fault injection experiment using the AIRLAB Diagnostic Emulation Facility

NASA Technical Reports Server (NTRS)

Baker, Robert; Mangum, Scott; Scheper, Charlotte

1988-01-01

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

VCSEL fault location apparatus and method

DOEpatents

Keeler, Gordon A [Albuquerque, NM; Serkland, Darwin K [Albuquerque, NM

2007-05-15

An apparatus for locating a fault within an optical fiber is disclosed. The apparatus, which can be formed as a part of a fiber-optic transmitter or as a stand-alone instrument, utilizes a vertical-cavity surface-emitting laser (VCSEL) to generate a test pulse of light which is coupled into an optical fiber under test. The VCSEL is subsequently reconfigured by changing a bias voltage thereto and is used as a resonant-cavity photodetector (RCPD) to detect a portion of the test light pulse which is reflected or scattered from any fault within the optical fiber. A time interval .DELTA.t between an instant in time when the test light pulse is generated and the time the reflected or scattered portion is detected can then be used to determine the location of the fault within the optical fiber.

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.

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

PubMed

Sauer, Juergen; Chavaillaz, Alain; Wastell, David

2016-06-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

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.

Enhanced data validation strategy of air quality monitoring network.

PubMed

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

2018-01-01

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

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.

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.

Automatically generated acceptance test: A software reliability experiment

NASA Technical Reports Server (NTRS)

Protzel, Peter W.

1988-01-01

This study presents results of a software reliability experiment investigating the feasibility of a new error detection method. The method can be used as an acceptance test and is solely based on empirical data about the behavior of internal states of a program. The experimental design uses the existing environment of a multi-version experiment previously conducted at the NASA Langley Research Center, in which the launch interceptor problem is used as a model. This allows the controlled experimental investigation of versions with well-known single and multiple faults, and the availability of an oracle permits the determination of the error detection performance of the test. Fault interaction phenomena are observed that have an amplifying effect on the number of error occurrences. Preliminary results indicate that all faults examined so far are detected by the acceptance test. This shows promise for further investigations, and for the employment of this test method on other applications.

Discrete Wavelet Transform for Fault Locations in Underground Distribution System

NASA Astrophysics Data System (ADS)

Apisit, C.; Ngaopitakkul, A.

2010-10-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

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.

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.

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.

Implementation of an experimental fault-tolerant memory system

NASA Technical Reports Server (NTRS)

Carter, W. C.; Mccarthy, C. E.

1976-01-01

The experimental fault-tolerant memory system described in this paper has been designed to enable the modular addition of spares, to validate the theoretical fault-secure and self-testing properties of the translator/corrector, to provide a basis for experiments using the new testing and correction processes for recovery, and to determine the practicality of such systems. The hardware design and implementation are described, together with methods of fault insertion. The hardware/software interface, including a restricted single error correction/double error detection (SEC/DED) code, is specified. Procedures are carefully described which, (1) test for specified physical faults, (2) ensure that single error corrections are not miscorrections due to triple faults, and (3) enable recovery from double errors.

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

Impulse Testing of Corporate-Fed Patch Array Antennas

NASA Technical Reports Server (NTRS)

Chamberlain, Neil F.

2011-01-01

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

Automatic Detection of Electric Power Troubles (ADEPT)

NASA Technical Reports Server (NTRS)

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

1988-01-01

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

Detecting Gear Tooth Fatigue Cracks in Advance of Complete Fracture

NASA Technical Reports Server (NTRS)

Zakrajsek, James J.; Lewicki, David G.

1996-01-01

Results of using vibration-based methods to detect gear tooth fatigue cracks are presented. An experimental test rig was used to fail a number of spur gear specimens through bending fatigue. The gear tooth fatigue crack in each test was initiated through a small notch in the fillet area of a tooth on the gear. The primary purpose of these tests was to verify analytical predictions of fatigue crack propagation direction and rate as a function of gear rim thickness. The vibration signal from a total of three tests was monitored and recorded for gear fault detection research. The damage consisted of complete rim fracture on the two thin rim gears and single tooth fracture on the standard full rim test gear. Vibration-based fault detection methods were applied to the vibration signal both on-line and after the tests were completed. The objectives of this effort were to identify methods capable of detecting the fatigue crack and to determine how far in advance of total failure positive detection was given. Results show that the fault detection methods failed to respond to the fatigue crack prior to complete rim fracture in the thin rim gear tests. In the standard full rim gear test all of the methods responded to the fatigue crack in advance of tooth fracture; however, only three of the methods responded to the fatigue crack in the early stages of crack propagation.

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

NASA Technical Reports Server (NTRS)

Glass, B. J. (Editor)

1992-01-01

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

Validation of Helicopter Gear Condition Indicators Using Seeded Fault Tests

NASA Technical Reports Server (NTRS)

Dempsey, Paula; Brandon, E. Bruce

2013-01-01

A "seeded fault test" in support of a rotorcraft condition based maintenance program (CBM), is an experiment in which a component is tested with a known fault while health monitoring data is collected. These tests are performed at operating conditions comparable to operating conditions the component would be exposed to while installed on the aircraft. Performance of seeded fault tests is one method used to provide evidence that a Health Usage Monitoring System (HUMS) can replace current maintenance practices required for aircraft airworthiness. Actual in-service experience of the HUMS detecting a component fault is another validation method. This paper will discuss a hybrid validation approach that combines in service-data with seeded fault tests. For this approach, existing in-service HUMS flight data from a naturally occurring component fault will be used to define a component seeded fault test. An example, using spiral bevel gears as the targeted component, will be presented. Since the U.S. Army has begun to develop standards for using seeded fault tests for HUMS validation, the hybrid approach will be mapped to the steps defined within their Aeronautical Design Standard Handbook for CBM. This paper will step through their defined processes, and identify additional steps that may be required when using component test rig fault tests to demonstrate helicopter CI performance. The discussion within this paper will provide the reader with a better appreciation for the challenges faced when defining a seeded fault test for HUMS validation.

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.

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.

Test pattern generation for ILA sequential circuits

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

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

NASA Astrophysics Data System (ADS)

Le, Huy Xuan; Matunaga, Saburo

2014-12-01

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

Fault tolerant system based on IDDQ testing

NASA Astrophysics Data System (ADS)

Guibane, Badi; Hamdi, Belgacem; Mtibaa, Abdellatif; Bensalem, Brahim

2018-06-01

Offline test is essential to ensure good manufacturing quality. However, for permanent or transient faults that occur during the use of the integrated circuit in an application, an online integrated test is needed as well. This procedure should ensure the detection and possibly the correction or the masking of these faults. This requirement of self-correction is sometimes necessary, especially in critical applications that require high security such as automotive, space or biomedical applications. We propose a fault-tolerant design for analogue and mixed-signal design complementary metal oxide (CMOS) circuits based on the quiescent current supply (IDDQ) testing. A defect can cause an increase in current consumption. IDDQ testing technique is based on the measurement of power supply current to distinguish between functional and failed circuits. The technique has been an effective testing method for detecting physical defects such as gate-oxide shorts, floating gates (open) and bridging defects in CMOS integrated circuits. An architecture called BICS (Built In Current Sensor) is used for monitoring the supply current (IDDQ) of the connected integrated circuit. If the measured current is not within the normal range, a defect is signalled and the system switches connection from the defective to a functional integrated circuit. The fault-tolerant technique is composed essentially by a double mirror built-in current sensor, allowing the detection of abnormal current consumption and blocks allowing the connection to redundant circuits, if a defect occurs. Spices simulations are performed to valid the proposed design.

Efficient fault diagnosis of helicopter gearboxes

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Development and evaluation of a fault-tolerant multiprocessor (FTMP) computer. Volume 4: FTMP executive summary

NASA Technical Reports Server (NTRS)

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

1984-01-01

The FTMP architecture is a high reliability computer concept modeled after a homogeneous multiprocessor architecture. Elements of the FTMP are operated in tight synchronism with one another and hardware fault-detection and fault-masking is provided which is transparent to the software. Operating system design and user software design is thus greatly simplified. Performance of the FTMP is also comparable to that of a simplex equivalent due to the efficiency of fault handling hardware. The FTMP project constructed an engineering module of the FTMP, programmed the machine and extensively tested the architecture through fault injection and other stress testing. This testing confirmed the soundness of the FTMP concepts.

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.

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

NASA Technical Reports Server (NTRS)

Glass, B. J. (Editor)

1992-01-01

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

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

NASA Astrophysics Data System (ADS)

Glass, B. J.

1992-10-01

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

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.

Health Monitoring of a Satellite System

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

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

NASA Technical Reports Server (NTRS)

Chen, Paul Peichuan

1993-01-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

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.

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.

Non-Invasive Detection of CH-46 AFT Gearbox Faults Using Digital Pattern Recognition and Classification Techniques

DTIC Science & Technology

1999-05-05

processing and artificial neural network (ANN) technology. The detector will classify incipient faults based on real-tine vibration data taken from the...provided the vibration data necessary to develop and test the feasibility of en artificial neural network for fault classification. This research

Demonstrating the Safety and Reliability of a New System or Spacecraft: Incorporating Analyses and Reviews of the Design and Processing in Determining the Number of Tests to be Conducted

NASA Technical Reports Server (NTRS)

Vesely, William E.; Colon, Alfredo E.

2010-01-01

Design Safety/Reliability is associated with the probability of no failure-causing faults existing in a design. Confidence in the non-existence of failure-causing faults is increased by performing tests with no failure. Reliability-Growth testing requirements are based on initial assurance and fault detection probability. Using binomial tables generally gives too many required tests compared to reliability-growth requirements. Reliability-Growth testing requirements are based on reliability principles and factors and should be used.

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.

Model-Based Fault Diagnosis for Turboshaft Engines

NASA Technical Reports Server (NTRS)

Green, Michael D.; Duyar, Ahmet; Litt, Jonathan S.

1998-01-01

Tests are described which, when used to augment the existing periodic maintenance and pre-flight checks of T700 engines, can greatly improve the chances of uncovering a problem compared to the current practice. These test signals can be used to expose and differentiate between faults in various components by comparing the responses of particular engine variables to the expected. The responses can be processed on-line in a variety of ways which have been shown to reveal and identify faults. The combination of specific test signals and on-line processing methods provides an ad hoc approach to the isolation of faults which might not otherwise be detected during pre-flight checkout.

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

NASA Technical Reports Server (NTRS)

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

2009-01-01

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

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

NASA Astrophysics Data System (ADS)

Wang, H.; Jing, X. J.

2017-07-01

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

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.

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.

Reliability Assessment for Low-cost Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Freeman, Paul Michael

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

On-board fault management for autonomous spacecraft

NASA Technical Reports Server (NTRS)

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

1991-01-01

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

Transmission line relay mis-operation detection based on time-synchronized field data

DOE PAGES

Esmaeilian, Ahad; Popovic, Tomo; Kezunovic, Mladen

2015-05-04

In this paper, a real-time tool to detect transmission line relay mis-operation is implemented. The tool uses time-synchronized measurements obtained from both ends of the line during disturbances. The proposed fault analysis tool comes into the picture only after the protective device has operated and tripped the line. The proposed methodology is able not only to detect, classify, and locate transmission line faults, but also to accurately confirm whether the line was tripped due to a mis-operation of protective relays. The analysis report includes either detailed description of the fault type and location or detection of relay mis-operation. As such,more » it can be a source of very useful information to support the system restoration. The focus of the paper is on the implementation requirements that allow practical application of the methodology, which is illustrated using the field data obtained the real power system. Testing and validation is done using the field data recorded by digital fault recorders and protective relays. The test data included several hundreds of event records corresponding to both relay mis-operations and actual faults. The discussion of results addresses various challenges encountered during the implementation and validation of the presented methodology.« less

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

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.

Framework for a space shuttle main engine health monitoring system

NASA Technical Reports Server (NTRS)

Hawman, Michael W.; Galinaitis, William S.; Tulpule, Sharayu; Mattedi, Anita K.; Kamenetz, Jeffrey

1990-01-01

A framework developed for a health management system (HMS) which is directed at improving the safety of operation of the Space Shuttle Main Engine (SSME) is summarized. An emphasis was placed on near term technology through requirements to use existing SSME instrumentation and to demonstrate the HMS during SSME ground tests within five years. The HMS framework was developed through an analysis of SSME failure modes, fault detection algorithms, sensor technologies, and hardware architectures. A key feature of the HMS framework design is that a clear path from the ground test system to a flight HMS was maintained. Fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and demonstrated on data from SSME ground test failures. The fault detection algorithms exhibited 100 percent detection of faults, had an extremely low false alarm rate, and were robust to sensor loss. These algorithms were incorporated into a hierarchical decision making strategy for overall assessment of SSME health. A preliminary design for a hardware architecture capable of supporting real time operation of the HMS functions was developed. Utilizing modular, commercial off-the-shelf components produced a reliable low cost design with the flexibility to incorporate advances in algorithm and sensor technology as they become available.

Integrated Application of Active Controls (IAAC) technology to an advanced subsonic transport project: Test act system validation

NASA Technical Reports Server (NTRS)

1985-01-01

The primary objective of the Test Active Control Technology (ACT) System laboratory tests was to verify and validate the system concept, hardware, and software. The initial lab tests were open loop hardware tests of the Test ACT System as designed and built. During the course of the testing, minor problems were uncovered and corrected. Major software tests were run. The initial software testing was also open loop. These tests examined pitch control laws, wing load alleviation, signal selection/fault detection (SSFD), and output management. The Test ACT System was modified to interface with the direct drive valve (DDV) modules. The initial testing identified problem areas with DDV nonlinearities, valve friction induced limit cycling, DDV control loop instability, and channel command mismatch. The other DDV issue investigated was the ability to detect and isolate failures. Some simple schemes for failure detection were tested but were not completely satisfactory. The Test ACT System architecture continues to appear promising for ACT/FBW applications in systems that must be immune to worst case generic digital faults, and be able to tolerate two sequential nongeneric faults with no reduction in performance. The challenge in such an implementation would be to keep the analog element sufficiently simple to achieve the necessary reliability.

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.

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.

Pattern classifier for health monitoring of helicopter gearboxes

NASA Technical Reports Server (NTRS)

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

1993-01-01

The application of a newly developed diagnostic method to a helicopter gearbox is demonstrated. This method is a pattern classifier which uses a multi-valued influence matrix (MVIM) as its diagnostic model. The method benefits from a fast learning algorithm, based on error feedback, that enables it to estimate gearbox health from a small set of measurement-fault data. The MVIM method can also assess the diagnosability of the system and variability of the fault signatures as the basis to improve fault signatures. This method was tested on vibration signals reflecting various faults in an OH-58A main rotor transmission gearbox. The vibration signals were then digitized and processed by a vibration signal analyzer to enhance and extract various features of the vibration data. The parameters obtained from this analyzer were utilized to train and test the performance of the MVIM method in both detection and diagnosis. The results indicate that the MVIM method provided excellent detection results when the full range of faults effects on the measurements were included in training, and it had a correct diagnostic rate of 95 percent when the faults were included in training.

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

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

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

2013-02-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

A 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.

Sixty-four-Channel Inline Cable Tester

NASA Technical Reports Server (NTRS)

2008-01-01

Faults in wiring are a serious concern for the aerospace and aeronautics (commercial, military, and civil) industries. A number of accidents have occurred because faulty wiring created shorts or opens that resulted in the loss of control of the aircraft or because arcing led to fires and explosions. Some of these accidents have resulted in the massive loss of lives (such as in the TWA Flight 800 accident). Circuits on the Space Shuttle have also failed because of faulty insulation on wiring. STS-93 lost power when a primary power circuit in one engine failed and a second engine had a backup power circuit fault. Cables are usually tested on the ground after the crew reports a fault encountered during flight. Often such failures result from vibration and cannot be replicated while the aircraft is stationary. It is therefore important to monitor faults while the aircraft is in operation, when cables are more likely to fail. Work is in progress to develop a cable fault tester capable of monitoring up to 64 individual wires simultaneously. Faults can be monitored either inline or offline. In the inline mode of operation, the monitoring is performed without disturbing the normal operation of the wires under test. That is, the operations are performed unintrusively and are essentially undetectable for the test signal levels are below the noise floor. A cable can be monitored several times per second in the offline mode and once a second in the inline mode. The 64-channel inline cable tester not only detects the occurrence of a fault, but also determines the type of fault (short/open) and the location of the fault. This will enable the detection of intermittent faults that can be repaired before they become serious problems.

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

NASA Astrophysics Data System (ADS)

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

2013-02-01

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

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

PubMed

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

2013-02-01

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

Flight test results of the Strapdown hexad Inertial Reference Unit (SIRU). Volume 1: Flight test summary

NASA Technical Reports Server (NTRS)

Hruby, R. J.; Bjorkman, W. S.

1977-01-01

Flight test results of the strapdown inertial reference unit (SIRU) navigation system are presented. The fault-tolerant SIRU navigation system features a redundant inertial sensor unit and dual computers. System software provides for detection and isolation of inertial sensor failures and continued operation in the event of failures. Flight test results include assessments of the system's navigational performance and fault tolerance.

(Quickly) Testing the Tester via Path Coverage

NASA Technical Reports Server (NTRS)

Groce, Alex

2009-01-01

The configuration complexity and code size of an automated testing framework may grow to a point that the tester itself becomes a significant software artifact, prone to poor configuration and implementation errors. Unfortunately, testing the tester by using old versions of the software under test (SUT) may be impractical or impossible: test framework changes may have been motivated by interface changes in the tested system, or fault detection may become too expensive in terms of computing time to justify running until errors are detected on older versions of the software. We propose the use of path coverage measures as a "quick and dirty" method for detecting many faults in complex test frameworks. We also note the possibility of using techniques developed to diversify state-space searches in model checking to diversify test focus, and an associated classification of tester changes into focus-changing and non-focus-changing modifications.

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

NASA Astrophysics Data System (ADS)

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

2018-07-01

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

Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

PubMed Central

Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2011-01-01

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

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.

Delay test generation for synchronous sequential circuits

NASA Astrophysics Data System (ADS)

Devadas, Srinivas

1989-05-01

We address the problem of generating tests for delay faults in non-scan synchronous sequential circuits. Delay test generation for sequential circuits is a considerably more difficult problem than delay testing of combinational circuits and has received much less attention. In this paper, we present a method for generating test sequences to detect delay faults in sequential circuits using the stuck-at fault sequential test generator STALLION. The method is complete in that it will generate a delay test sequence for a targeted fault given sufficient CPU time, if such a sequence exists. We term faults for which no delay test sequence exists, under out test methodology, sequentially delay redundant. We describe means of eliminating sequential delay redundancies in logic circuits. We present a partial-scan methodology for enhancing the testability of difficult-to-test of untestable sequential circuits, wherein a small number of flip-flops are selected and made controllable/observable. The selection process guarantees the elimination of all sequential delay redundancies. We show that an intimate relationship exists between state assignment and delay testability of a sequential machine. We describe a state assignment algorithm for the synthesis of sequential machines with maximal delay fault testability. Preliminary experimental results using the test generation, partial-scan and synthesis algorithm are presented.

Comparison of Test Stand and Helicopter Oil Cooler Bearing Condition Indicators

NASA Technical Reports Server (NTRS)

Dempsey, Paula J.; Branning, Jeremy; Wade, Damiel R.; Bolander, Nathan

2010-01-01

The focus of this paper was to compare the performance of HUMS condition indicators (CI) when detecting a bearing fault in a test stand or on a helicopter. This study compared data from two sources: first, CI data collected from accelerometers installed on two UH-60 Black Hawk helicopters when oil cooler bearing faults occurred, along with data from helicopters with no bearing faults; and second, CI data that was collected from ten cooler bearings, healthy and faulted, that were removed from fielded helicopters and installed in a test stand. A method using Receiver Operating Characteristic (ROC) curves to compare CI performance was demonstrated. Results indicated the bearing energy CI responded differently for the helicopter and the test stand. Future research is required if test stand data is to be used validate condition indicator performance on a helicopter.

PDSS/IMC requirements and functional specifications

NASA Technical Reports Server (NTRS)

1983-01-01

The system (software and hardware) requirements for the Payload Development Support System (PDSS)/Image Motion Compensator (IMC) are provided. The PDSS/IMC system provides the capability for performing Image Motion Compensator Electronics (IMCE) flight software test, checkout, and verification and provides the capability for monitoring the IMC flight computer system during qualification testing for fault detection and fault isolation.

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

The Application of Time-Frequency Methods to HUMS

NASA Technical Reports Server (NTRS)

Pryor, Anna H.; Mosher, Marianne; Lewicki, David G.; Norvig, Peter (Technical Monitor)

2001-01-01

This paper reports the study of four time-frequency transforms applied to vibration signals and presents a new metric for comparing them for fault detection. The four methods to be described and compared are the Short Time Frequency Transform (STFT), the Choi-Williams Distribution (WV-CW), the Continuous Wavelet Transform (CWT) and the Discrete Wavelet Transform (DWT). Vibration data of bevel gear tooth fatigue cracks, under a variety of operating load levels, are analyzed using these methods. The new metric for automatic fault detection is developed and can be produced from any systematic numerical representation of the vibration signals. This new metric reveals indications of gear damage with all of the methods on this data set. Analysis with the CWT detects mechanical problems with the test rig not found with the other transforms. The WV-CW and CWT use considerably more resources than the STFT and the DWT. More testing of the new metric is needed to determine its value for automatic fault detection and to develop methods of setting the threshold for the metric.

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.

Scientific Research Program for Power, Energy, and Thermal Technologies. Task Order 0002: Power, Thermal and Control Technologies and Processes Experimental Research. Subtask: Laboratory Test Set-up to Evaluate Electromechanical Actuation Systems for Aircraft Flight Control

DTIC Science & Technology

2015-08-01

faults are incorporated into the system in order to better understand the EMA reliability, and to aid in designing fault detection software for real...to a fixed angle repeatedly and accurately [16]. The motor in the EHA is used to drive a reversible pump tied to a hydraulic cylinder which moves...24] [25] [26]. These test stands are used for the prognostic testing of EMAS that have had mechanical or electrical faults injected into them. The

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.

Flight test results of the strapdown hexad inertial reference unit (SIRU). Volume 2: Test report

NASA Technical Reports Server (NTRS)

Hruby, R. J.; Bjorkman, W. S.

1977-01-01

Results of flight tests of the Strapdown Inertial Reference Unit (SIRU) navigation system are presented. The fault tolerant SIRU navigation system features a redundant inertial sensor unit and dual computers. System software provides for detection and isolation of inertial sensor failures and continued operation in the event of failures. Flight test results include assessments of the system's navigational performance and fault tolerance. Performance shortcomings are analyzed.

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.

The effects of transistor source-to-gate bridging faults in complex CMOS gates

NASA Astrophysics Data System (ADS)

Visweswaran, G. S.; Ali, Akhtar-Uz-Zaman M.; Lala, Parag K.; Hartmann, Carlos R. P.

1991-06-01

A study of the effect of gate-to-source bridging faults in the pull-up section of a complex CMOS gate is presented. The manifestation of these faults depends on the resistance value of the connection causing the bridging. It is shown that such faults manifest themselves either as stuck-at or stuck-open faults and can be detected by tests for stuck-at and stuck-open faults generated for the equivalent logic current. It is observed that for transistor channel lengths larger than 1 microns there exists a range of values of the bridging resistance for which the fault behaves as a pseudo-stuck-open fault.

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.

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

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.

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.

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.

Weak fault detection and health degradation monitoring using customized standard multiwavelets

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Ferrimagnetic resonance signal produced by frictional heating: A new indicator of paleoseismicity

NASA Astrophysics Data System (ADS)

Fukuchi, Tatsuro; Mizoguchi, Kazuo; Shimamoto, Toshihiko

2005-12-01

High-speed fault slips during earthquakes may generate sufficient frictional heat to produce fused fault rocks such as pseudotachylyte. We have carried out high-speed slip tests using natural fault gouge to judge whether or not frictional heating universally occurs during seismic fault slips. In our shearing tests, natural fault gouge is put between two cylindrical silica glasses and sheared under a fixed axial stress of 0.61 MPa. Despite such a low stress near the Earth's surface, a darkened cohesive material resembling pseudotachylyte is made from the fault gouge along the edge of a circular shear plane when shearing at a high speed of 1500 rpm (the maximum slip rate reaches ˜1.96 m/s at the edge). Electron spin resonance measurements reveal that the darkened cohesive material has a strong ferrimagnetic resonance (FMR) signal, which is derived from bulky trivalent iron ions in ferrimagnetic iron oxides (γ-Fe2O3). The FMR signal is produced by the thermal dehydration of antiferromagnetic iron oxides (γ-FeOOH) in the fault gouge. This may be applicable to the detection of past heating during seismic fault slip. We thus attempt to reconstruct the temperature of frictional heat generated on the Nojima fault plane in the 1995 Kobe earthquake (M = 7.3) by inversion using the FMR signal. The computer simulation indicates that the frictional heat generated on the Nojima fault plane at ˜390 m depth may have attained ˜390°C during the 1995 Kobe earthquake. The temperature in the fault plane may have returned to its initial state after ˜1 year. This result suggests that a heat flow anomaly generated by faulting may be difficult to detect.

FERMILAB CRYOMODULE TEST STAND RF INTERLOCK SYSTEM

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

Petersen, Troy; Diamond, J. S.; McDowell, D.

2016-10-12

An interlock system has been designed for the Fermilab Cryo-module Test Stand (CMTS), a test bed for the cryo- modules to be used in the upcoming Linac Coherent Light Source 2 (LCLS-II) project at SLAC. The interlock system features 8 independent subsystems, one per superconducting RF cavity and solid state amplifier (SSA) pair. Each system monitors several devices to detect fault conditions such as arcing in the waveguides or quenching of the SRF system. Additionally each system can detect fault conditions by monitoring the RF power seen at the cavity coupler through a directional coupler. In the event of amore » fault condition, each system is capable of removing RF signal to the amplifier (via a fast RF switch) as well as turning off the SSA. Additionally, each input signal is available for re- mote viewing and recording via a Fermilab designed digitizer board and MVME 5500 processor.« less

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.

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.

Development of a low cost test rig for standalone WECS subject to electrical faults.

PubMed

Himani; Dahiya, Ratna

2016-11-01

In this paper, a contribution to the development of low-cost wind turbine (WT) test rig for stator fault diagnosis of wind turbine generator is proposed. The test rig is developed using a 2.5kW, 1750 RPM DC motor coupled to a 1.5kW, 1500 RPM self-excited induction generator interfaced with a WT mathematical model in LabVIEW. The performance of the test rig is benchmarked with already proven wind turbine test rigs. In order to detect the stator faults using non-stationary signals in self-excited induction generator, an online fault diagnostic technique of DWT-based multi-resolution analysis is proposed. It has been experimentally proven that for varying wind conditions wavelet decomposition allows good differentiation between faulty and healthy conditions leading to an effective diagnostic procedure for wind turbine condition monitoring. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

A Generalised Fault Protection Structure Proposed for Uni-grounded Low-Voltage AC Microgrids

NASA Astrophysics Data System (ADS)

Bui, Duong Minh; Chen, Shi-Lin; Lien, Keng-Yu; Jiang, Jheng-Lun

2016-04-01

This paper presents three main configurations of uni-grounded low-voltage AC microgrids. Transient situations of a uni-grounded low-voltage (LV) AC microgrid (MG) are simulated through various fault tests and operation transition tests between grid-connected and islanded modes. Based on transient simulation results, available fault protection methods are proposed for main and back-up protection of a uni-grounded AC microgrid. In addition, concept of a generalised fault protection structure of uni-grounded LVAC MGs is mentioned in the paper. As a result, main contributions of the paper are: (i) definition of different uni-grounded LVAC MG configurations; (ii) analysing transient responses of a uni-grounded LVAC microgrid through line-to-line faults, line-to-ground faults, three-phase faults and a microgrid operation transition test, (iii) proposing available fault protection methods for uni-grounded microgrids, such as: non-directional or directional overcurrent protection, under/over voltage protection, differential current protection, voltage-restrained overcurrent protection, and other fault protection principles not based on phase currents and voltages (e.g. total harmonic distortion detection of currents and voltages, using sequence components of current and voltage, 3I0 or 3V0 components), and (iv) developing a generalised fault protection structure with six individual protection zones to be suitable for different uni-grounded AC MG configurations.

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

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

PubMed

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

2016-11-02

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

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

PubMed Central

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

2016-01-01

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

Practical Issues in Implementing Software Reliability Measurement

NASA Technical Reports Server (NTRS)

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

1999-01-01

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

Measuring and Specifying Combinatorial Coverage of Test Input Configurations

PubMed Central

Kuhn, D. Richard; Kacker, Raghu N.; Lei, Yu

2015-01-01

A key issue in testing is how many tests are needed for a required level of coverage or fault detection. Estimates are often based on error rates in initial testing, or on code coverage. For example, tests may be run until a desired level of statement or branch coverage is achieved. Combinatorial methods present an opportunity for a different approach to estimating required test set size, using characteristics of the test set. This paper describes methods for estimating the coverage of, and ability to detect, t-way interaction faults of a test set based on a covering array. We also develop a connection between (static) combinatorial coverage and (dynamic) code coverage, such that if a specific condition is satisfied, 100% branch coverage is assured. Using these results, we propose practical recommendations for using combinatorial coverage in specifying test requirements. PMID:28133442

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine

PubMed Central

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

2016-01-01

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

Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine.

PubMed

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

2016-02-02

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

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

NASA Technical Reports Server (NTRS)

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

1996-01-01

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

Development and validation of techniques for improving software dependability

NASA Technical Reports Server (NTRS)

Knight, John C.

1992-01-01

A collection of document abstracts are presented on the topic of improving software dependability through NASA grant NAG-1-1123. Specific topics include: modeling of error detection; software inspection; test cases; Magnetic Stereotaxis System safety specifications and fault trees; and injection of synthetic faults into software.

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.

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.

Real-Time Curvature Defect Detection on Outer Surfaces Using Best-Fit Polynomial Interpolation

PubMed Central

Golkar, Ehsan; Prabuwono, Anton Satria; Patel, Ahmed

2012-01-01

This paper presents a novel, real-time defect detection system, based on a best-fit polynomial interpolation, that inspects the conditions of outer surfaces. The defect detection system is an enhanced feature extraction method that employs this technique to inspect the flatness, waviness, blob, and curvature faults of these surfaces. The proposed method has been performed, tested, and validated on numerous pipes and ceramic tiles. The results illustrate that the physical defects such as abnormal, popped-up blobs are recognized completely, and that flames, waviness, and curvature faults are detected simultaneously. PMID:23202186

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

NASA Astrophysics Data System (ADS)

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

2015-07-01

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

Failure detection and identification for a reconfigurable flight control system

NASA Technical Reports Server (NTRS)

Dallery, Francois

1987-01-01

Failure detection and identification logic for a fault-tolerant longitudinal control system were investigated. Aircraft dynamics were based upon the cruise condition for a hypothetical transonic business jet transport configuration. The fault-tolerant control system consists of conventional control and estimation plus a new outer loop containing failure detection, identification, and reconfiguration (FDIR) logic. It is assumed that the additional logic has access to all measurements, as well as to the outputs of the control and estimation logic. The pilot may also command the FDIR logic to perform special tests.

Development of an accurate transmission line fault locator using the global positioning system satellites

NASA Technical Reports Server (NTRS)

Lee, Harry

1994-01-01

A highly accurate transmission line fault locator based on the traveling-wave principle was developed and successfully operated within B.C. Hydro. A transmission line fault produces a fast-risetime traveling wave at the fault point which propagates along the transmission line. This fault locator system consists of traveling wave detectors located at key substations which detect and time tag the leading edge of the fault-generated traveling wave as if passes through. A master station gathers the time-tagged information from the remote detectors and determines the location of the fault. Precise time is a key element to the success of this system. This fault locator system derives its timing from the Global Positioning System (GPS) satellites. System tests confirmed the accuracy of locating faults to within the design objective of +/-300 meters.

Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

PubMed Central

Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong

2014-01-01

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458

Final Technical Report: PV Fault Detection Tool.

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

King, Bruce Hardison; Jones, Christian Birk

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

Neural net diagnostics for VLSI test

NASA Technical Reports Server (NTRS)

Lin, T.; Tseng, H.; Wu, A.; Dogan, N.; Meador, J.

1990-01-01

This paper discusses the application of neural network pattern analysis algorithms to the IC fault diagnosis problem. A fault diagnostic is a decision rule combining what is known about an ideal circuit test response with information about how it is distorted by fabrication variations and measurement noise. The rule is used to detect fault existence in fabricated circuits using real test equipment. Traditional statistical techniques may be used to achieve this goal, but they can employ unrealistic a priori assumptions about measurement data. Our approach to this problem employs an adaptive pattern analysis technique based on feedforward neural networks. During training, a feedforward network automatically captures unknown sample distributions. This is important because distributions arising from the nonlinear effects of process variation can be more complex than is typically assumed. A feedforward network is also able to extract measurement features which contribute significantly to making a correct decision. Traditional feature extraction techniques employ matrix manipulations which can be particularly costly for large measurement vectors. In this paper we discuss a software system which we are developing that uses this approach. We also provide a simple example illustrating the use of the technique for fault detection in an operational amplifier.

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

NASA Astrophysics Data System (ADS)

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

2013-05-01

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

Evaluation of Cepstrum Algorithm with Impact Seeded Fault Data of Helicopter Oil Cooler Fan Bearings and Machine Fault Simulator Data

DTIC Science & Technology

2013-02-01

of a bearing must be put into practice. There are many potential methods, the most traditional being the use of statistical time-domain features...accelerate degradation to test multiples bearings to gain statistical relevance and extrapolate results to scale for field conditions. Temperature...as time statistics , frequency estimation to improve the fault frequency detection. For future investigations, one can further explore the

High-frequency imaging of elastic contrast and contact area with implications for naturally observed changes in fault properties

USGS Publications Warehouse

Nagata, Kohei; Kilgore, Brian D.; Beeler, Nicholas M.; Nakatani, Masao

2014-01-01

During localized slip of a laboratory fault we simultaneously measure the contact area and the dynamic fault normal elastic stiffness. One objective is to determine conditions where stiffness may be used to infer changes in area of contact during sliding on nontransparent fault surfaces. Slip speeds between 0.01 and 10 µm/s and normal stresses between 1 and 2.5 MPa were imposed during velocity step, normal stress step, and slide-hold-slide tests. Stiffness and contact area have a linear interdependence during rate stepping tests and during the hold portion of slide-hold-slide tests. So long as linearity holds, measured fault stiffness can be used on nontransparent materials to infer changes in contact area. However, there are conditions where relations between contact area and stiffness are nonlinear and nonunique. A second objective is to make comparisons between the laboratory- and field-measured changes in fault properties. Time-dependent changes in fault zone normal stiffness made in stress relaxation tests imply postseismic wave speed changes on the order of 0.3% to 0.8% per year in the two or more years following an earthquake; these are smaller than postseismic increases seen within natural damage zones. Based on scaling of the experimental observations, natural postseismic fault normal contraction could be accommodated within a few decimeter wide fault core. Changes in the stiffness of laboratory shear zones exceed 10% per decade and might be detectable in the field postseismically.

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.

Fault detection on a sewer network by a combination of a Kalman filter and a binary sequential probability ratio test

NASA Astrophysics Data System (ADS)

Piatyszek, E.; Voignier, P.; Graillot, D.

2000-05-01

One of the aims of sewer networks is the protection of population against floods and the reduction of pollution rejected to the receiving water during rainy events. To meet these goals, managers have to equip the sewer networks with and to set up real-time control systems. Unfortunately, a component fault (leading to intolerable behaviour of the system) or sensor fault (deteriorating the process view and disturbing the local automatism) makes the sewer network supervision delicate. In order to ensure an adequate flow management during rainy events it is essential to set up procedures capable of detecting and diagnosing these anomalies. This article introduces a real-time fault detection method, applicable to sewer networks, for the follow-up of rainy events. This method consists in comparing the sensor response with a forecast of this response. This forecast is provided by a model and more precisely by a state estimator: a Kalman filter. This Kalman filter provides not only a flow estimate but also an entity called 'innovation'. In order to detect abnormal operations within the network, this innovation is analysed with the binary sequential probability ratio test of Wald. Moreover, by crossing available information on several nodes of the network, a diagnosis of the detected anomalies is carried out. This method provided encouraging results during the analysis of several rains, on the sewer network of Seine-Saint-Denis County, France.

Diagnosis of Misalignment in Overhung Rotor using the K-S Statistic and A2 Test

NASA Astrophysics Data System (ADS)

Garikapati, Diwakar; Pacharu, RaviKumar; Munukurthi, Rama Satya Satyanarayana

2018-02-01

Vibration measurement at the bearings of rotating machinery has become a useful technique for diagnosing incipient fault conditions. In particular, vibration measurement can be used to detect unbalance in rotor, bearing failure, gear problems or misalignment between a motor shaft and coupled shaft. This is a particular problem encountered in turbines, ID fans and FD fans used for power generation. For successful fault diagnosis, it is important to adopt motor current signature analysis (MCSA) techniques capable of identifying the faults. It is also useful to develop techniques for inferring information such as the severity of fault. It is proposed that modeling the cumulative distribution function of motor current signals with respect to appropriate theoretical distributions, and quantifying the goodness of fit with the Kolmogorov-Smirnov (KS) statistic and A2 test offers a suitable signal feature for diagnosis. This paper demonstrates the successful comparison of the K-S feature and A2 test for discriminating the misalignment fault from normal function.

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.

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

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

NASA Astrophysics Data System (ADS)

Arriola, David; Thielecke, Frank

2017-09-01

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

Analytical and experimental vibration analysis of a faulty gear system

NASA Astrophysics Data System (ADS)

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

1994-10-01

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

Analytical and experimental vibration analysis of a faulty gear system

NASA Astrophysics Data System (ADS)

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

1994-10-01

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

Analytical and Experimental Vibration Analysis of a Faulty Gear System

NASA Technical Reports Server (NTRS)

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

1994-01-01

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

Preliminary Study on Acoustic Detection of Faults Experienced by a High-Bypass Turbofan Engine

NASA Technical Reports Server (NTRS)

Boyle, Devin K.

2014-01-01

The vehicle integrated propulsion research (VIPR) effort conducted by NASA and several partners provided an unparalleled opportunity to test a relatively low TRL concept regarding the use of far field acoustics to identify faults occurring in a high bypass turbofan engine. Though VIPR Phase II ground based aircraft installed engine testing wherein a multitude of research sensors and methods were evaluated, an array of acoustic microphones was used to determine the viability of such an array to detect failures occurring in a commercially representative high bypass turbofan engine. The failures introduced during VIPR testing included commanding the engine's low pressure compressor (LPC) exit and high pressure compressor (HPC) 14th stage bleed values abruptly to their failsafe positions during steady state

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.

Extended Testability Analysis Tool

NASA Technical Reports Server (NTRS)

Melcher, Kevin; Maul, William A.; Fulton, Christopher

2012-01-01

The Extended Testability Analysis (ETA) Tool is a software application that supports fault management (FM) by performing testability analyses on the fault propagation model of a given system. Fault management includes the prevention of faults through robust design margins and quality assurance methods, or the mitigation of system failures. Fault management requires an understanding of the system design and operation, potential failure mechanisms within the system, and the propagation of those potential failures through the system. The purpose of the ETA Tool software is to process the testability analysis results from a commercial software program called TEAMS Designer in order to provide a detailed set of diagnostic assessment reports. The ETA Tool is a command-line process with several user-selectable report output options. The ETA Tool also extends the COTS testability analysis and enables variation studies with sensor sensitivity impacts on system diagnostics and component isolation using a single testability output. The ETA Tool can also provide extended analyses from a single set of testability output files. The following analysis reports are available to the user: (1) the Detectability Report provides a breakdown of how each tested failure mode was detected, (2) the Test Utilization Report identifies all the failure modes that each test detects, (3) the Failure Mode Isolation Report demonstrates the system s ability to discriminate between failure modes, (4) the Component Isolation Report demonstrates the system s ability to discriminate between failure modes relative to the components containing the failure modes, (5) the Sensor Sensor Sensitivity Analysis Report shows the diagnostic impact due to loss of sensor information, and (6) the Effect Mapping Report identifies failure modes that result in specified system-level effects.

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

PubMed

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

2015-10-23

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

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

PubMed

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

2017-09-01

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

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.

Development of Hydrologic Characterization Technology of Fault Zones (in Japanese; English)

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

Karasaki, Kenzi; Onishi, Tiemi; Wu, Yu-Shu

2008-03-31

Through an extensive literature survey we find that there is very limited amount of work on fault zone hydrology, particularly in the field using borehole testing. The common elements of a fault include a core, and damage zones. The core usually acts as a barrier to the flow across it, whereas the damage zone controls the flow either parallel to the strike or dip of a fault. In most of cases the damage zone isthe one that is controlling the flow in the fault zone and the surroundings. The permeability of damage zone is in the range of two tomore » three orders of magnitude higher than the protolith. The fault core can have permeability up to seven orders of magnitude lower than the damage zone. The fault types (normal, reverse, and strike-slip) by themselves do not appear to be a clear classifier of the hydrology of fault zones. However, there still remains a possibility that other additional geologic attributes and scaling relationships can be used to predict or bracket the range of hydrologic behavior of fault zones. AMT (Audio frequency Magneto Telluric) and seismic reflection techniques are often used to locate faults. Geochemical signatures and temperature distributions are often used to identify flow domains and/or directions. ALSM (Airborne Laser Swath Mapping) or LIDAR (Light Detection and Ranging) method may prove to be a powerful tool for identifying lineaments in place of the traditional photogrammetry. Nonetheless not much work has been done to characterize the hydrologic properties of faults by directly testing them using pump tests. There are some uncertainties involved in analyzing pressure transients of pump tests: both low permeability and high permeability faults exhibit similar pressure responses. A physically based conceptual and numerical model is presented for simulating fluid and heat flow and solute transport through fractured fault zones using a multiple-continuum medium approach. Data from the Horonobe URL site are analyzed to demonstrate the proposed approach and to examine the flow direction and magnitude on both sides of a suspected fault. We describe a strategy for effective characterization of fault zone hydrology. We recommend conducting a long term pump test followed by a long term buildup test. We do not recommend isolating the borehole into too many intervals. We do recommend ensuring durability and redundancy for long term monitoring.« less

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.

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

NASA Astrophysics Data System (ADS)

Kim, D.; Youn, J.; Kim, C.

2017-08-01

As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

PubMed Central

Jing, Luyang; Wang, Taiyong; Zhao, Ming; Wang, Peng

2017-01-01

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment. PMID:28230767

Mechatronics technology in predictive maintenance method

NASA Astrophysics Data System (ADS)

Majid, Nurul Afiqah A.; Muthalif, Asan G. A.

2017-11-01

This paper presents recent mechatronics technology that can help to implement predictive maintenance by combining intelligent and predictive maintenance instrument. Vibration Fault Simulation System (VFSS) is an example of mechatronics system. The focus of this study is the prediction on the use of critical machines to detect vibration. Vibration measurement is often used as the key indicator of the state of the machine. This paper shows the choice of the appropriate strategy in the vibration of diagnostic process of the mechanical system, especially rotating machines, in recognition of the failure during the working process. In this paper, the vibration signature analysis is implemented to detect faults in rotary machining that includes imbalance, mechanical looseness, bent shaft, misalignment, missing blade bearing fault, balancing mass and critical speed. In order to perform vibration signature analysis for rotating machinery faults, studies have been made on how mechatronics technology is used as predictive maintenance methods. Vibration Faults Simulation Rig (VFSR) is designed to simulate and understand faults signatures. These techniques are based on the processing of vibrational data in frequency-domain. The LabVIEW-based spectrum analyzer software is developed to acquire and extract frequency contents of faults signals. This system is successfully tested based on the unique vibration fault signatures that always occur in a rotating machinery.

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.

OBIST methodology incorporating modified sensitivity of pulses for active analogue filter components

NASA Astrophysics Data System (ADS)

Khade, R. H.; Chaudhari, D. S.

2018-03-01

In this paper, oscillation-based built-in self-test method is used to diagnose catastrophic and parametric faults in integrated circuits. Sallen-Key low pass filter and high pass filter circuits with different gains are used to investigate defects. Variation in seven parameters of operational amplifier (OP-AMP) like gain, input impedance, output impedance, slew rate, input bias current, input offset current, input offset voltage and catastrophic as well as parametric defects in components outside OP-AMP are introduced in the circuit and simulation results are analysed. Oscillator output signal is converted to pulses which are used to generate a signature of the circuit. The signature and pulse count changes with the type of fault present in the circuit under test (CUT). The change in oscillation frequency is observed for fault detection. Designer has flexibility to predefine tolerance band of cut-off frequency and range of pulses for which circuit should be accepted. The fault coverage depends upon the required tolerance band of the CUT. We propose a modification of sensitivity of parameter (pulses) to avoid test escape and enhance yield. Result shows that the method provides 100% fault coverage for catastrophic faults.

Comparison of Fault Detection Algorithms for Real-time Diagnosis in Large-Scale System. Appendix E

NASA Technical Reports Server (NTRS)

Kirubarajan, Thiagalingam; Malepati, Venkat; Deb, Somnath; Ying, Jie

2001-01-01

In this paper, we present a review of different real-time capable algorithms to detect and isolate component failures in large-scale systems in the presence of inaccurate test results. A sequence of imperfect test results (as a row vector of I's and O's) are available to the algorithms. In this case, the problem is to recover the uncorrupted test result vector and match it to one of the rows in the test dictionary, which in turn will isolate the faults. In order to recover the uncorrupted test result vector, one needs the accuracy of each test. That is, its detection and false alarm probabilities are required. In this problem, their true values are not known and, therefore, have to be estimated online. Other major aspects in this problem are the large-scale nature and the real-time capability requirement. Test dictionaries of sizes up to 1000 x 1000 are to be handled. That is, results from 1000 tests measuring the state of 1000 components are available. However, at any time, only 10-20% of the test results are available. Then, the objective becomes the real-time fault diagnosis using incomplete and inaccurate test results with online estimation of test accuracies. It should also be noted that the test accuracies can vary with time --- one needs a mechanism to update them after processing each test result vector. Using Qualtech's TEAMS-RT (system simulation and real-time diagnosis tool), we test the performances of 1) TEAMSAT's built-in diagnosis algorithm, 2) Hamming distance based diagnosis, 3) Maximum Likelihood based diagnosis, and 4) HidderMarkov Model based diagnosis.

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.

Flight test of a full authority Digital Electronic Engine Control system in an F-15 aircraft

NASA Technical Reports Server (NTRS)

Barrett, W. J.; Rembold, J. P.; Burcham, F. W.; Myers, L.

1981-01-01

The Digital Electronic Engine Control (DEEC) system considered is a relatively low cost digital full authority control system containing selectively redundant components and fault detection logic with capability for accommodating faults to various levels of operational capability. The DEEC digital control system is built around a 16-bit, 1.2 microsecond cycle time, CMOS microprocessor, microcomputer system with approximately 14 K of available memory. Attention is given to the control mode, component bench testing, closed loop bench testing, a failure mode and effects analysis, sea-level engine testing, simulated altitude engine testing, flight testing, the data system, cockpit, and real time display.

Test aspects of the JPL Viterbi decoder

NASA Technical Reports Server (NTRS)

Breuer, M. A.

1989-01-01

The generation of test vectors and design-for-test aspects of the Jet Propulsion Laboratory (JPL) Very Large Scale Integration (VLSI) Viterbi decoder chip is discussed. Each processor integrated circuit (IC) contains over 20,000 gates. To achieve a high degree of testability, a scan architecture is employed. The logic has been partitioned so that very few test vectors are required to test the entire chip. In addition, since several blocks of logic are replicated numerous times on this chip, test vectors need only be generated for each block, rather than for the entire circuit. These unique blocks of logic have been identified and test sets generated for them. The approach employed for testing was to use pseudo-exhaustive test vectors whenever feasible. That is, each cone of logid is tested exhaustively. Using this approach, no detailed logic design or fault model is required. All faults which modify the function of a block of combinational logic are detected, such as all irredundant single and multiple stuck-at faults.

A soft computing scheme incorporating ANN and MOV energy in fault detection, classification and distance estimation of EHV transmission line with FSC.

PubMed

Khadke, Piyush; Patne, Nita; Singh, Arvind; Shinde, Gulab

2016-01-01

In this article, a novel and accurate scheme for fault detection, classification and fault distance estimation for a fixed series compensated transmission line is proposed. The proposed scheme is based on artificial neural network (ANN) and metal oxide varistor (MOV) energy, employing Levenberg-Marquardt training algorithm. The novelty of this scheme is the use of MOV energy signals of fixed series capacitors (FSC) as input to train the ANN. Such approach has never been used in any earlier fault analysis algorithms in the last few decades. Proposed scheme uses only single end measurement energy signals of MOV in all the 3 phases over one cycle duration from the occurrence of a fault. Thereafter, these MOV energy signals are fed as input to ANN for fault distance estimation. Feasibility and reliability of the proposed scheme have been evaluated for all ten types of fault in test power system model at different fault inception angles over numerous fault locations. Real transmission system parameters of 3-phase 400 kV Wardha-Aurangabad transmission line (400 km) with 40 % FSC at Power Grid Wardha Substation, India is considered for this research. Extensive simulation experiments show that the proposed scheme provides quite accurate results which demonstrate complete protection scheme with high accuracy, simplicity and robustness.

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.

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.

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

NASA Technical Reports Server (NTRS)

Wilson, Edward (Inventor)

2008-01-01

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

Fault detection using a two-model test for changes in the parameters of an autoregressive time series

NASA Technical Reports Server (NTRS)

Scholtz, P.; Smyth, P.

1992-01-01

This article describes an investigation of a statistical hypothesis testing method for detecting changes in the characteristics of an observed time series. The work is motivated by the need for practical automated methods for on-line monitoring of Deep Space Network (DSN) equipment to detect failures and changes in behavior. In particular, on-line monitoring of the motor current in a DSN 34-m beam waveguide (BWG) antenna is used as an example. The algorithm is based on a measure of the information theoretic distance between two autoregressive models: one estimated with data from a dynamic reference window and one estimated with data from a sliding reference window. The Hinkley cumulative sum stopping rule is utilized to detect a change in the mean of this distance measure, corresponding to the detection of a change in the underlying process. The basic theory behind this two-model test is presented, and the problem of practical implementation is addressed, examining windowing methods, model estimation, and detection parameter assignment. Results from the five fault-transition simulations are presented to show the possible limitations of the detection method, and suggestions for future implementation are given.

Fault 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.

NHPP-Based Software Reliability Models Using Equilibrium Distribution

NASA Astrophysics Data System (ADS)

Xiao, Xiao; Okamura, Hiroyuki; Dohi, Tadashi

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

Development of monitoring and diagnostic methods for robots used in remediation of waste sites. 1997 annual progress report

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

Tecza, J.

1998-06-01

'Safe and efficient clean up of hazardous and radioactive waste sites throughout the DOE complex will require extensive use of robots. This research effort focuses on developing Monitoring and Diagnostic (M and D) methods for robots that will provide early detection, isolation, and tracking of impending faults before they result in serious failure. The utility and effectiveness of applying M and D methods to hydraulic robots has never been proven. The present research program is utilizing seeded faults in a laboratory test rig that is representative of an existing hydraulically-powered remediation robot. This report summarizes activity conducted in the firstmore » 9 months of the project. The research team has analyzed the Rosie Mobile Worksystem as a representative hydraulic robot, developed a test rig for implanted fault testing, developed a test plan and agenda, and established methods for acquiring and analyzing the test data.'« less

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

PubMed

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

2014-09-01

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

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

NASA Technical Reports Server (NTRS)

Keesler, E. L.

1974-01-01

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

Dynamic assertion testing of flight control software

NASA Technical Reports Server (NTRS)

Andrews, D. M.; Mahmood, A.; Mccluskey, E. J.

1985-01-01

An experiment in using assertions to dynamically test fault tolerant flight software is described. The experiment showed that 87% of typical errors introduced into the program would be detected by assertions. Detailed analysis of the test data showed that the number of assertions needed to detect those errors could be reduced to a minimal set. The analysis also revealed that the most effective assertions tested program parameters that provided greater indirect (collateral) testing of other parameters.

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.

A Novel Transient Fault Current Sensor Based on the PCB Rogowski Coil for Overhead Transmission Lines

PubMed Central

Liu, Yadong; Xie, Xiaolei; Hu, Yue; Qian, Yong; Sheng, Gehao; Jiang, Xiuchen

2016-01-01

The accurate detection of high-frequency transient fault currents in overhead transmission lines is the basis of malfunction detection and diagnosis. This paper proposes a novel differential winding printed circuit board (PCB) Rogowski coil for the detection of transient fault currents in overhead transmission lines. The interference mechanism of the sensor surrounding the overhead transmission line is analyzed and the guideline for the interference elimination is obtained, and then a differential winding printed circuit board (PCB) Rogowski coil is proposed, where the branch and return line of the PCB coil were designed to be strictly symmetrical by using a joining structure of two semi-rings and collinear twisted pair differential windings in each semi-ring. A serial test is conducted, including the frequency response, linearity, and anti-interference performance as well as a comparison with commercial sensors. Results show that a PCB Rogowski coil has good linearity and resistance to various external magnetic field interferences, thus enabling it to be widely applied in fault-current-collecting devices. PMID:27213402

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.

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

NASA Technical Reports Server (NTRS)

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

2012-01-01

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

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.

Linking fault pattern with groundwater flow in crystalline rocks at the Grimsel Test Site (Switzerland)

NASA Astrophysics Data System (ADS)

Schneeberger, Raphael; Berger, Alfons; Mäder, Urs K.; Niklaus Waber, H.; Kober, Florian; Herwegh, Marco

2017-04-01

Water flow across crystalline bedrock is of major interest for deep-seated geothermal energy projects as well as for underground disposal of radioactive waste. In crystalline rocks enhanced fluid flow is related to zones of increased permeability, i.e. to fractures that are associated to fault zones. The flow regime around the Grimsel Test Site (GTS, Central Aar massif) was assessed by establishing a 3D fault zone pattern on a local scale in the GTS underground facility (deca-meter scale) and on a regional scale at the surface (km-scale). The study reveals the existence of a dense fault zone network consisting of several km long and few tens of cm to meter wide, sub-vertically oriented major faults that are connected by tens to hundreds of meters long minor bridging faults. This geometrical information was used as input for the generation of a 3D fault zone network model. The faults originate from ductile shear zones that were reactivated as brittle faults under retrograde conditions during exhumation. Embrittlement and associated dilatancy along the faults provide the pathways for today's groundwater flow. Detection of the actual 3D flow paths is, however, challenging since flow seem to be not planar but rather tube-like. Two strategies are applied to constrain the 3D geometry of the flow tubes: (i) Characterization of the groundwater infiltrating into the GTS (location, yield, hydraulic head, and chemical composition) and (ii) stress modelling on the base of the 3D structural model to unravel potential domains of enhanced fluid flow such as fault plane intersections and domains of dilatancy. At the Grimsel Test Site, hydraulic and structural data demonstrate that the groundwater flow is head-driven from the surface towards the GTS located some 450 m below the surface. The residence time of the groundwater in this surface-near section is >60 years as evidenced by absence of detectable tritium. However, hydraulic heads obtained from interval pressure measurements within boreholes are variable and do not correspond to the overburden above the interval. Underground mapping revealed close spatial relation between water inflow points and faults, major water inflows occur in strongly deformed areas of the GTS. Furthermore, persistent differences in the groundwater chemical composition between infiltration points indicate that connectivity between different water flow paths is poor. Different findings indicate complex flow path geometries. However, domains of enhanced dilatancy and domains with increased number of fault intersections correlate with areas in the underground with 'high' water inflow.

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

Graph-based real-time fault diagnostics

NASA Technical Reports Server (NTRS)

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

1988-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-07-01

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

Detecting Tooth Damage in Geared Drive Trains

NASA Technical Reports Server (NTRS)

Nachtsheim, Philip R.

1997-01-01

This paper describes a method that was developed to detect gear tooth damage that does not require a priori knowledge of the frequency characteristic of the fault. The basic idea of the method is that a few damaged teeth will cause transient load fluctuations unlike the normal tooth load fluctuations. The method attempts to measure the energy in the lower side bands of the modulated signal caused by the transient load fluctuations. The method monitors the energy in the frequency interval which excludes the frequency of the lowest dominant normal tooth load fluctuation and all frequencies above it. The method reacted significantly to the tooth fracture damage results documented in the Lewis data sets which were obtained from tests of the OH-58A transmission and tests of high contact ratio spiral bevel gears. The method detected gear tooth fractures in all four of the high contact ratio spiral bevel gear runs. Published results indicate other detection methods were only able to detect faults for three out of four runs.

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

NASA Technical Reports Server (NTRS)

Wong, Derek; Poll, Scott; KrishnaKumar, Kalmanje

2005-01-01

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

Preliminary design of the redundant software experiment

NASA Technical Reports Server (NTRS)

Campbell, Roy; Deimel, Lionel; Eckhardt, Dave, Jr.; Kelly, John; Knight, John; Lauterbach, Linda; Lee, Larry; Mcallister, Dave; Mchugh, John

1985-01-01

The goal of the present experiment is to characterize the fault distributions of highly reliable software replicates, constructed using techniques and environments which are similar to those used in comtemporary industrial software facilities. The fault distributions and their effect on the reliability of fault tolerant configurations of the software will be determined through extensive life testing of the replicates against carefully constructed randomly generated test data. Each detected error will be carefully analyzed to provide insight in to their nature and cause. A direct objective is to develop techniques for reducing the intensity of coincident errors, thus increasing the reliability gain which can be achieved with fault tolerance. Data on the reliability gains realized, and the cost of the fault tolerant configurations can be used to design a companion experiment to determine the cost effectiveness of the fault tolerant strategy. Finally, the data and analysis produced by this experiment will be valuable to the software engineering community as a whole because it will provide a useful insight into the nature and cause of hard to find, subtle faults which escape standard software engineering validation techniques and thus persist far into the software life cycle.

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-11-18

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

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

PubMed Central

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

2015-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

Method of Fault Detection and Rerouting

NASA Technical Reports Server (NTRS)

Gibson, Tracy L. (Inventor); Medelius, Pedro J. (Inventor); Lewis, Mark E. (Inventor)

2013-01-01

A system and method for detecting damage in an electrical wire, including delivering at least one test electrical signal to an outer electrically conductive material in a continuous or non-continuous layer covering an electrically insulative material layer that covers an electrically conductive wire core. Detecting the test electrical signals in the outer conductive material layer to obtain data that is processed to identify damage in the outer electrically conductive material layer.

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.

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

NASA Astrophysics Data System (ADS)

Gangsar, Purushottam; Tiwari, Rajiv

2017-09-01

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

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry.

PubMed

Gligorijevic, Jovan; Gajic, Dragoljub; Brkovic, Aleksandar; Savic-Gajic, Ivana; Georgieva, Olga; Di Gennaro, Stefano

2016-03-01

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings.

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry

PubMed Central

Gligorijevic, Jovan; Gajic, Dragoljub; Brkovic, Aleksandar; Savic-Gajic, Ivana; Georgieva, Olga; Di Gennaro, Stefano

2016-01-01

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings. PMID:26938541

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

NASA Technical Reports Server (NTRS)

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

2010-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Fan fault diagnosis based on symmetrized dot pattern analysis and image matching

NASA Astrophysics Data System (ADS)

Xu, Xiaogang; Liu, Haixiao; Zhu, Hao; Wang, Songling

2016-07-01

To detect the mechanical failure of fans, a new diagnostic method based on the symmetrized dot pattern (SDP) analysis and image matching is proposed. Vibration signals of 13 kinds of running states are acquired on a centrifugal fan test bed and reconstructed by the SDP technique. The SDP pattern templates of each running state are established. An image matching method is performed to diagnose the fault. In order to improve the diagnostic accuracy, the single template, multiple templates and clustering fault templates are used to perform the image matching.

Advanced microprocessor based power protection system using artificial neural network techniques

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

Chen, Z.; Kalam, A.; Zayegh, A.

This paper describes an intelligent embedded microprocessor based system for fault classification in power system protection system using advanced 32-bit microprocessor technology. The paper demonstrates the development of protective relay to provide overcurrent protection schemes for fault detection. It also describes a method for power fault classification in three-phase system based on the use of neural network technology. The proposed design is implemented and tested on a single line three phase power system in power laboratory. Both the hardware and software development are described in detail.

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.

Study of fault tolerant software technology for dynamic systems

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Zacharias, G. L.

1985-01-01

The major aim of this study is to investigate the feasibility of using systems-based failure detection isolation and compensation (FDIC) techniques in building fault-tolerant software and extending them, whenever possible, to the domain of software fault tolerance. First, it is shown that systems-based FDIC methods can be extended to develop software error detection techniques by using system models for software modules. In particular, it is demonstrated that systems-based FDIC techniques can yield consistency checks that are easier to implement than acceptance tests based on software specifications. Next, it is shown that systems-based failure compensation techniques can be generalized to the domain of software fault tolerance in developing software error recovery procedures. Finally, the feasibility of using fault-tolerant software in flight software is investigated. In particular, possible system and version instabilities, and functional performance degradation that may occur in N-Version programming applications to flight software are illustrated. Finally, a comparative analysis of N-Version and recovery block techniques in the context of generic blocks in flight software is presented.

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.

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.

A fault-tolerant control architecture for unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Drozeski, Graham R.

Research has presented several approaches to achieve varying degrees of fault-tolerance in unmanned aircraft. Approaches in reconfigurable flight control are generally divided into two categories: those which incorporate multiple non-adaptive controllers and switch between them based on the output of a fault detection and identification element, and those that employ a single adaptive controller capable of compensating for a variety of fault modes. Regardless of the approach for reconfigurable flight control, certain fault modes dictate system restructuring in order to prevent a catastrophic failure. System restructuring enables active control of actuation not employed by the nominal system to recover controllability of the aircraft. After system restructuring, continued operation requires the generation of flight paths that adhere to an altered flight envelope. The control architecture developed in this research employs a multi-tiered hierarchy to allow unmanned aircraft to generate and track safe flight paths despite the occurrence of potentially catastrophic faults. The hierarchical architecture increases the level of autonomy of the system by integrating five functionalities with the baseline system: fault detection and identification, active system restructuring, reconfigurable flight control; reconfigurable path planning, and mission adaptation. Fault detection and identification algorithms continually monitor aircraft performance and issue fault declarations. When the severity of a fault exceeds the capability of the baseline flight controller, active system restructuring expands the controllability of the aircraft using unconventional control strategies not exploited by the baseline controller. Each of the reconfigurable flight controllers and the baseline controller employ a proven adaptive neural network control strategy. A reconfigurable path planner employs an adaptive model of the vehicle to re-shape the desired flight path. Generation of the revised flight path is posed as a linear program constrained by the response of the degraded system. Finally, a mission adaptation component estimates limitations on the closed-loop performance of the aircraft and adjusts the aircraft mission accordingly. A combination of simulation and flight test results using two unmanned helicopters validates the utility of the hierarchical architecture.

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 hidden in vibration signals and performs well for bearing fault diagnosis.

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.

New methods for the condition monitoring of level crossings

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

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

NASA Technical Reports Server (NTRS)

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

2013-01-01

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

Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2003-01-01

In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.

Functional requirements for an intelligent RPC. [remote power controller for spaceborne electrical distribution system

NASA Technical Reports Server (NTRS)

Aucoin, B. M.; Heller, R. P.

1990-01-01

An intelligent remote power controller (RPC) based on microcomputer technology can implement advanced functions for the accurate and secure detection of all types of faults on a spaceborne electrical distribution system. The intelligent RPC will implement conventional protection functions such as overcurrent, under-voltage, and ground fault protection. Advanced functions for the detection of soft faults, which cannot presently be detected, can also be implemented. Adaptive overcurrent protection changes overcurrent settings based on connected load. Incipient and high-impedance fault detection provides early detection of arcing conditions to prevent fires, and to clear and reconfigure circuits before soft faults progress to a hard-fault condition. Power electronics techniques can be used to implement fault current limiting to prevent voltage dips during hard faults. It is concluded that these techniques will enhance the overall safety and reliability of the distribution system.

An empirical study of flight control software reliability

NASA Technical Reports Server (NTRS)

Dunham, J. R.; Pierce, J. L.

1986-01-01

The results of a laboratory experiment in flight control software reliability are reported. The experiment tests a small sample of implementations of a pitch axis control law for a PA28 aircraft with over 14 million pitch commands with varying levels of additive input and feedback noise. The testing which uses the method of n-version programming for error detection surfaced four software faults in one implementation of the control law. The small number of detected faults precluded the conduct of the error burst analyses. The pitch axis problem provides data for use in constructing a model in the prediction of the reliability of software in systems with feedback. The study is undertaken to find means to perform reliability evaluations of flight control software.

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.

Operation Of The X-29A Digital Flight-Control System

NASA Technical Reports Server (NTRS)

Chacon, Vince; Mcbride, David

1990-01-01

Report reviews program of testing and evaluation of digital flight-control system for X-29A airplane, with emphasis on operation during tests. Topics include design of system, special electronic testing equipment designed to aid in daily operations, and aspects of testing, including detection of faults.

Modeling of a latent fault detector in a digital system

NASA Technical Reports Server (NTRS)

Nagel, P. M.

1978-01-01

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

A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan W.

2014-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

A Model-Based Anomaly Detection Approach for Analyzing Streaming Aircraft Engine Measurement Data

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Rinehart, Aidan Walker

2015-01-01

This paper presents a model-based anomaly detection architecture designed for analyzing streaming transient aircraft engine measurement data. The technique calculates and monitors residuals between sensed engine outputs and model predicted outputs for anomaly detection purposes. Pivotal to the performance of this technique is the ability to construct a model that accurately reflects the nominal operating performance of the engine. The dynamic model applied in the architecture is a piecewise linear design comprising steady-state trim points and dynamic state space matrices. A simple curve-fitting technique for updating the model trim point information based on steadystate information extracted from available nominal engine measurement data is presented. Results from the application of the model-based approach for processing actual engine test data are shown. These include both nominal fault-free test case data and seeded fault test case data. The results indicate that the updates applied to improve the model trim point information also improve anomaly detection performance. Recommendations for follow-on enhancements to the technique are also presented and discussed.

Detecting gear tooth fracture in a high contact ratio face gear mesh

NASA Technical Reports Server (NTRS)

Zakrajsek, James J.; Handschuh, Robert F.; Lewicki, David G.; Decker, Harry J.

1995-01-01

This paper summarized the results of a study in which three different vibration diagnostic methods were used to detect gear tooth fracture in a high contact ratio face gear mesh. The NASA spiral bevel gear fatigue test rig was used to produce unseeded fault, natural failures of four face gear specimens. During the fatigue tests, which were run to determine load capacity and primary failure mechanisms for face gears, vibration signals were monitored and recorded for gear diagnostic purposes. Gear tooth bending fatigue and surface pitting were the primary failure modes found in the tests. The damage ranged from partial tooth fracture on a single tooth in one test to heavy wear, severe pitting, and complete tooth fracture of several teeth on another test. Three gear fault detection techniques, FM4, NA4*, and NB4, were applied to the experimental data. These methods use the signal average in both the time and frequency domain. Method NA4* was able to conclusively detect the gear tooth fractures in three out of the four fatigue tests, along with gear tooth surface pitting and heavy wear. For multiple tooth fractures, all of the methods gave a clear indication of the damage. It was also found that due to the high contact ratio of the face gear mesh, single tooth fractures did not significantly affect the vibration signal, making this type of failure difficult to detect.

Engine Data Interpretation System (EDIS), phase 2

NASA Technical Reports Server (NTRS)

Cost, Thomas L.; Hofmann, Martin O.

1991-01-01

A prototype of an expert system was developed which applies qualitative constraint-based reasoning to the task of post-test analysis of data resulting from a rocket engine firing. Data anomalies are detected and corresponding faults are diagnosed. Engine behavior is reconstructed using measured data and knowledge about engine behavior. Knowledge about common faults guides but does not restrict the search for the best explanation in terms of hypothesized faults. The system contains domain knowledge about the behavior of common rocket engine components and was configured for use with the Space Shuttle Main Engine (SSME). A graphical user interface allows an expert user to intimately interact with the system during diagnosis. The system was applied to data taken during actual SSME tests where data anomalies were observed.

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

NASA Astrophysics Data System (ADS)

Sun, Junhua; Xiao, Zhongwen

2016-10-01

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

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

PubMed

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

2017-11-01

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

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

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.

Flight elements: Fault detection and fault management

NASA Technical Reports Server (NTRS)

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

1990-01-01

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

A study of the use of vibration and stress wave sensing for the detection of bearing failure

NASA Technical Reports Server (NTRS)

Ensor, L. C.; Feng, C. C.

1975-01-01

Results from an experimental study of vibrations and stress waves emitted from ball bearings are presented. Fatique tests were run with both high quality bearings and man faulted bearings, all of one size. Tests were instrumented with different sensors to detect the noises from 10 Hz to 1 MHz. Frequency spectrum plots are presented. The modulation characteristics of the ultrasonic noises were analyzed, and acoustic emission type measurements were conducted. Results are presented which show that there are usable acoustic signal levels even beyond 500 KHz. These signal levels are modulated by a low frequency carrier which is a function of the stress loading and acoustic transmissibility. The results were correlated to fault size in the bearings. The correlation shows that the sensor used for signals from 100 KHz to 1 MHz gave the best sensitivity and detected the generation of very small spalls or pits.

SSME fault monitoring and diagnosis expert system

NASA Technical Reports Server (NTRS)

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

1989-01-01

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

Software fault tolerance using data diversity

NASA Technical Reports Server (NTRS)

Knight, John C.

1991-01-01

Research on data diversity is discussed. Data diversity relies on a different form of redundancy from existing approaches to software fault tolerance and is substantially less expensive to implement. Data diversity can also be applied to software testing and greatly facilitates the automation of testing. Up to now it has been explored both theoretically and in a pilot study, and has been shown to be a promising technique. The effectiveness of data diversity as an error detection mechanism and the application of data diversity to differential equation solvers are discussed.

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

NASA Technical Reports Server (NTRS)

Collins, Emmanuel G.

2000-01-01

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

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

NASA Astrophysics Data System (ADS)

Ghanbari, Teymoor; Samet, Haidar

2017-11-01

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

An uncertainty-based distributed fault detection mechanism for wireless sensor networks.

PubMed

Yang, Yang; Gao, Zhipeng; Zhou, Hang; Qiu, Xuesong

2014-04-25

Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio.

A Game Theoretic Fault Detection Filter

NASA Technical Reports Server (NTRS)

Chung, Walter H.; Speyer, Jason L.

1995-01-01

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

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.

On testing VLSI chips for the big Viterbi decoder

NASA Technical Reports Server (NTRS)

Hsu, I. S.

1989-01-01

A general technique that can be used in testing very large scale integrated (VLSI) chips for the Big Viterbi Decoder (BVD) system is described. The test technique is divided into functional testing and fault-coverage testing. The purpose of functional testing is to verify that the design works functionally. Functional test vectors are converted from outputs of software simulations which simulate the BVD functionally. Fault-coverage testing is used to detect and, in some cases, to locate faulty components caused by bad fabrication. This type of testing is useful in screening out bad chips. Finally, design for testability, which is included in the BVD VLSI chip design, is described in considerable detail. Both the observability and controllability of a VLSI chip are greatly enhanced by including the design for the testability feature.

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.

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.

Analysis of Space Shuttle Ground Support System Fault Detection, Isolation, and Recovery Processes and Resources

NASA Technical Reports Server (NTRS)

Gross, Anthony R.; Gerald-Yamasaki, Michael; Trent, Robert P.

2009-01-01

As part of the FDIR (Fault Detection, Isolation, and Recovery) Project for the Constellation Program, a task was designed within the context of the Constellation Program FDIR project called the Legacy Benchmarking Task to document as accurately as possible the FDIR processes and resources that were used by the Space Shuttle ground support equipment (GSE) during the Shuttle flight program. These results served as a comparison with results obtained from the new FDIR capability. The task team assessed Shuttle and EELV (Evolved Expendable Launch Vehicle) historical data for GSE-related launch delays to identify expected benefits and impact. This analysis included a study of complex fault isolation situations that required a lengthy troubleshooting process. Specifically, four elements of that system were considered: LH2 (liquid hydrogen), LO2 (liquid oxygen), hydraulic test, and ground special power.

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.

Haul truck tire dynamics due to tire condition

NASA Astrophysics Data System (ADS)

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

2012-05-01

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

State-of-the-Art Assessment of Testing and Testability of Custom LSI/VLSI Circuits. Volume VI. Redundancy, Testing Circuits, and Codes.

DTIC Science & Technology

1982-10-01

e.g., providing voters in TMR systems and detection-switching requirements in standby-sparing sys- tems. The application of mathematical thoery of...and time redundancy required for error detection and correction, are interrelated. Mathematical modeling, when applied to fault tolerant systems, can...9 1.1 Some Fundamental Principles............................. 11 1.2 Mathematical Theory of

Phase editing as a signal pre-processing step for automated bearing fault detection

NASA Astrophysics Data System (ADS)

Barbini, L.; Ompusunggu, A. P.; Hillis, A. J.; du Bois, J. L.; Bartic, A.

2017-07-01

Scheduled maintenance and inspection of bearing elements in industrial machinery contributes significantly to the operating costs. Savings can be made through automatic vibration-based damage detection and prognostics, to permit condition-based maintenance. However automation of the detection process is difficult due to the complexity of vibration signals in realistic operating environments. The sensitivity of existing methods to the choice of parameters imposes a requirement for oversight from a skilled operator. This paper presents a novel approach to the removal of unwanted vibrational components from the signal: phase editing. The approach uses a computationally-efficient full-band demodulation and requires very little oversight. Its effectiveness is tested on experimental data sets from three different test-rigs, and comparisons are made with two state-of-the-art processing techniques: spectral kurtosis and cepstral pre- whitening. The results from the phase editing technique show a 10% improvement in damage detection rates compared to the state-of-the-art while simultaneously improving on the degree of automation. This outcome represents a significant contribution in the pursuit of fully automatic fault detection.

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

Developing interpretable models with optimized set reduction for identifying high risk software components

NASA Technical Reports Server (NTRS)

Briand, Lionel C.; Basili, Victor R.; Hetmanski, Christopher J.

1993-01-01

Applying equal testing and verification effort to all parts of a software system is not very efficient, especially when resources are limited and scheduling is tight. Therefore, one needs to be able to differentiate low/high fault frequency components so that testing/verification effort can be concentrated where needed. Such a strategy is expected to detect more faults and thus improve the resulting reliability of the overall system. This paper presents the Optimized Set Reduction approach for constructing such models, intended to fulfill specific software engineering needs. Our approach to classification is to measure the software system and build multivariate stochastic models for predicting high risk system components. We present experimental results obtained by classifying Ada components into two classes: is or is not likely to generate faults during system and acceptance test. Also, we evaluate the accuracy of the model and the insights it provides into the error making process.

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

NASA Astrophysics Data System (ADS)

Fajriani; Srigutomo, Wahyu; Pratomo, Prihandhanu M.

2017-04-01

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

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.

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.

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

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

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 Negative Selection Immune System Inspired Methodology for Fault Diagnosis of Wind Turbines.

PubMed

Alizadeh, Esmaeil; Meskin, Nader; Khorasani, Khashayar

2017-11-01

High operational and maintenance costs represent as major economic constraints in the wind turbine (WT) industry. These concerns have made investigation into fault diagnosis of WT systems an extremely important and active area of research. In this paper, an immune system (IS) inspired methodology for performing fault detection and isolation (FDI) of a WT system is proposed and developed. The proposed scheme is based on a self nonself discrimination paradigm of a biological IS. Specifically, the negative selection mechanism [negative selection algorithm (NSA)] of the human body is utilized. In this paper, a hierarchical bank of NSAs are designed to detect and isolate both individual as well as simultaneously occurring faults common to the WTs. A smoothing moving window filter is then utilized to further improve the reliability and performance of the FDI scheme. Moreover, the performance of our proposed scheme is compared with another state-of-the-art data-driven technique, namely the support vector machines (SVMs) to demonstrate and illustrate the superiority and advantages of our proposed NSA-based FDI scheme. Finally, a nonparametric statistical comparison test is implemented to evaluate our proposed methodology with that of the SVM under various fault severities.

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,

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

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Fault recovery for real-time, multi-tasking computer system

NASA Technical Reports Server (NTRS)

Hess, Richard (Inventor); Kelly, Gerald B. (Inventor); Rogers, Randy (Inventor); Stange, Kent A. (Inventor)

2011-01-01

System and methods for providing a recoverable real time multi-tasking computer system are disclosed. In one embodiment, a system comprises a real time computing environment, wherein the real time computing environment is adapted to execute one or more applications and wherein each application is time and space partitioned. The system further comprises a fault detection system adapted to detect one or more faults affecting the real time computing environment and a fault recovery system, wherein upon the detection of a fault the fault recovery system is adapted to restore a backup set of state variables.

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

NASA Technical Reports Server (NTRS)

2004-01-01

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

A Thermal Expert System (TEXSYS) development overview - AI-based control of a Space Station prototype thermal bus

NASA Technical Reports Server (NTRS)

Glass, B. J.; Hack, E. C.

1990-01-01

A knowledge-based control system for real-time control and fault detection, isolation and recovery (FDIR) of a prototype two-phase Space Station Freedom external thermal control system (TCS) is discussed in this paper. The Thermal Expert System (TEXSYS) has been demonstrated in recent tests to be capable of both fault anticipation and detection and real-time control of the thermal bus. Performance requirements were achieved by using a symbolic control approach, layering model-based expert system software on a conventional numerical data acquisition and control system. The model-based capabilities of TEXSYS were shown to be advantageous during software development and testing. One representative example is given from on-line TCS tests of TEXSYS. The integration and testing of TEXSYS with a live TCS testbed provides some insight on the use of formal software design, development and documentation methodologies to qualify knowledge-based systems for on-line or flight applications.

Effectiveness of back-to-back testing

NASA Technical Reports Server (NTRS)

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

1987-01-01

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

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.

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.

A distributed programming environment for Ada

NASA Technical Reports Server (NTRS)

Brennan, Peter; Mcdonnell, Tom; Mcfarland, Gregory; Timmins, Lawrence J.; Litke, John D.

1986-01-01

Despite considerable commercial exploitation of fault tolerance systems, significant and difficult research problems remain in such areas as fault detection and correction. A research project is described which constructs a distributed computing test bed for loosely coupled computers. The project is constructing a tool kit to support research into distributed control algorithms, including a distributed Ada compiler, distributed debugger, test harnesses, and environment monitors. The Ada compiler is being written in Ada and will implement distributed computing at the subsystem level. The design goal is to provide a variety of control mechanics for distributed programming while retaining total transparency at the code level.

Automated Power Systems Management (APSM)

NASA Technical Reports Server (NTRS)

Bridgeforth, A. O.

1981-01-01

A breadboard power system incorporating autonomous functions of monitoring, fault detection and recovery, command and control was developed, tested and evaluated to demonstrate technology feasibility. Autonomous functions including switching of redundant power processing elements, individual load fault removal, and battery charge/discharge control were implemented by means of a distributed microcomputer system within the power subsystem. Three local microcomputers provide the monitoring, control and command function interfaces between the central power subsystem microcomputer and the power sources, power processing and power distribution elements. The central microcomputer is the interface between the local microcomputers and the spacecraft central computer or ground test equipment.

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

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

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

NASA Technical Reports Server (NTRS)

Walker, M.; Figueroa, F.

2015-01-01

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

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.

Fault detection and isolation

NASA Technical Reports Server (NTRS)

Bernath, Greg

1994-01-01

In order for a current satellite-based navigation system (such as the Global Positioning System, GPS) to meet integrity requirements, there must be a way of detecting erroneous measurements, without help from outside the system. This process is called Fault Detection and Isolation (FDI). Fault detection requires at least one redundant measurement, and can be done with a parity space algorithm. The best way around the fault isolation problem is not necessarily isolating the bad measurement, but finding a new combination of measurements which excludes it.

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

Katti, Amogh; Di Fatta, Giuseppe; Naughton III, Thomas J

Future extreme-scale high-performance computing systems will be required to work under frequent component failures. The MPI Forum's User Level Failure Mitigation proposal has introduced an operation, MPI_Comm_shrink, to synchronize the alive processes on the list of failed processes, so that applications can continue to execute even in the presence of failures by adopting algorithm-based fault tolerance techniques. This MPI_Comm_shrink operation requires a fault tolerant failure detection and consensus algorithm. This paper presents and compares two novel failure detection and consensus algorithms. The proposed algorithms are based on Gossip protocols and are inherently fault-tolerant and scalable. The proposed algorithms were implementedmore » and tested using the Extreme-scale Simulator. The results show that in both algorithms the number of Gossip cycles to achieve global consensus scales logarithmically with system size. The second algorithm also shows better scalability in terms of memory and network bandwidth usage and a perfect synchronization in achieving global consensus.« less

Event-Triggered Fault Detection of Nonlinear Networked Systems.

PubMed

Li, Hongyi; Chen, Ziran; Wu, Ligang; Lam, Hak-Keung; Du, Haiping

2017-04-01

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices.

PubMed

Wu, Yunkai; Jiang, Bin; Lu, Ningyun; Yang, Hao; Zhou, Yang

2017-03-01

This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Robust Fault Diagnosis in Electric Drives Using Machine Learning

DTIC Science & Technology

2004-09-08

detection of fault conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points...were used to generate various fault condition data for machine learning . The technique is viable for accurate, reliable and fast fault detection in electric drives.

Solar system fault detection

DOEpatents

Farrington, R.B.; Pruett, J.C. Jr.

1984-05-14

A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

Solar system fault detection

DOEpatents

Farrington, Robert B.; Pruett, Jr., James C.

1986-01-01

A fault detecting apparatus and method are provided for use with an active solar system. The apparatus provides an indication as to whether one or more predetermined faults have occurred in the solar system. The apparatus includes a plurality of sensors, each sensor being used in determining whether a predetermined condition is present. The outputs of the sensors are combined in a pre-established manner in accordance with the kind of predetermined faults to be detected. Indicators communicate with the outputs generated by combining the sensor outputs to give the user of the solar system and the apparatus an indication as to whether a predetermined fault has occurred. Upon detection and indication of any predetermined fault, the user can take appropriate corrective action so that the overall reliability and efficiency of the active solar system are increased.

Effects on Diagnostic Parameters After Removing Additional Synchronous Gear Meshes

NASA Technical Reports Server (NTRS)

Decker, Harry J.

2003-01-01

Gear cracks are typically difficult to diagnose with sufficient time before catastrophic damage occurs. Significant damage must be present before algorithms appear to be able to detect the damage. Frequently there are multiple gear meshes on a single shaft. Since they are all synchronous with the shaft frequency, the commonly used synchronous averaging technique is ineffective in removing other gear mesh effects. Carefully applying a filter to these extraneous gear mesh frequencies can reduce the overall vibration signal and increase the accuracy of commonly used vibration metrics. The vibration signals from three seeded fault tests were analyzed using this filtering procedure. Both the filtered and unfiltered vibration signals were then analyzed using commonly used fault detection metrics and compared. The tests were conducted on aerospace quality spur gears in a test rig. The tests were conducted at speeds ranging from 2500 to 5000 revolutions per minute and torques from 184 to 228 percent of design load. The inability to detect these cracks with high confidence results from the high loading which is causing fast fracture as opposed to stable crack growth. The results indicate that these techniques do not currently produce an indication of damage that significantly exceeds experimental scatter.

From experiment to design -- Fault characterization and detection in parallel computer systems using computational accelerators

NASA Astrophysics Data System (ADS)

Yim, Keun Soo

This dissertation summarizes experimental validation and co-design studies conducted to optimize the fault detection capabilities and overheads in hybrid computer systems (e.g., using CPUs and Graphics Processing Units, or GPUs), and consequently to improve the scalability of parallel computer systems using computational accelerators. The experimental validation studies were conducted to help us understand the failure characteristics of CPU-GPU hybrid computer systems under various types of hardware faults. The main characterization targets were faults that are difficult to detect and/or recover from, e.g., faults that cause long latency failures (Ch. 3), faults in dynamically allocated resources (Ch. 4), faults in GPUs (Ch. 5), faults in MPI programs (Ch. 6), and microarchitecture-level faults with specific timing features (Ch. 7). The co-design studies were based on the characterization results. One of the co-designed systems has a set of source-to-source translators that customize and strategically place error detectors in the source code of target GPU programs (Ch. 5). Another co-designed system uses an extension card to learn the normal behavioral and semantic execution patterns of message-passing processes executing on CPUs, and to detect abnormal behaviors of those parallel processes (Ch. 6). The third co-designed system is a co-processor that has a set of new instructions in order to support software-implemented fault detection techniques (Ch. 7). The work described in this dissertation gains more importance because heterogeneous processors have become an essential component of state-of-the-art supercomputers. GPUs were used in three of the five fastest supercomputers that were operating in 2011. Our work included comprehensive fault characterization studies in CPU-GPU hybrid computers. In CPUs, we monitored the target systems for a long period of time after injecting faults (a temporally comprehensive experiment), and injected faults into various types of program states that included dynamically allocated memory (to be spatially comprehensive). In GPUs, we used fault injection studies to demonstrate the importance of detecting silent data corruption (SDC) errors that are mainly due to the lack of fine-grained protections and the massive use of fault-insensitive data. This dissertation also presents transparent fault tolerance frameworks and techniques that are directly applicable to hybrid computers built using only commercial off-the-shelf hardware components. This dissertation shows that by developing understanding of the failure characteristics and error propagation paths of target programs, we were able to create fault tolerance frameworks and techniques that can quickly detect and recover from hardware faults with low performance and hardware overheads.

Model-Based Diagnosis and Prognosis of a Water Recycling System

NASA Technical Reports Server (NTRS)

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

2013-01-01

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

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.

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.

Low-Frequency Earthquakes Associated with the Late-Interseismic Central Alpine Fault, Southern Alps, New Zealand

NASA Astrophysics Data System (ADS)

Baratin, L. M.; Chamberlain, C. J.; Townend, J.; Savage, M. K.

2016-12-01

Characterising the seismicity associated with slow deformation in the vicinity of the Alpine Fault may provide constraints on the state of stress of this major transpressive margin prior to a large (≥M8) earthquake. Here, we use recently detected tremor and low-frequency earthquakes (LFEs) to examine how slow tectonic deformation is loading the Alpine Fault toward an anticipated large rupture. We initially work with a continous seismic dataset collected between 2009 and 2012 from an array of short-period seismometers, the Southern Alps Microearthquake Borehole Array. Fourteen primary LFE templates are used in an iterative matched-filter and stacking routine. This method allows the detection of similar signals and establishes LFE families with common locations. We thus generate a 36 month catalogue of 10718 LFEs. The detections are then combined for each LFE family using phase-weighted stacking to yield a signal with the highest possible signal to noise ratio. We found phase-weighted stacking to be successful in increasing the number of LFE detections by roughly 20%. Phase-weighted stacking also provides cleaner phase arrivals of apparently impulsive nature allowing more precise phase and polarity picks. We then compute improved non-linear earthquake locations using a 3D velocity model. We find LFEs to occur below the seismogenic zone at depths of 18-34 km, locating on or near the proposed deep extent of the Alpine Fault. Our next step is to estimate seismic source parameters by implementing a moment tensor inversion technique. Our focus is currently on generating a more extensive catalogue (spanning the years 2009 to 2016) using synthetic waveforms as primary templates, with which to detect LFEs. Initial testing shows that this technique paired up with phase-weighted stacking increases the number of LFE families and overall detected events roughly sevenfold. This catalogue should provide new insight into the geometry of the Alpine Fault and the prevailing stress field in the central Southern Alps.

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

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

NASA Technical Reports Server (NTRS)

Russell, B. Don

1989-01-01

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

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

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

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

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

The use of spore strips for monitoring the sterilization of bottled fluids.

PubMed Central

Selkon, J. B.; Sisson, P. R.; Ingham, H. R.

1979-01-01

A bacterial spore test has been developed which enables the efficacy of the sterilizing cycle recommended by the British Pharmaceutical Codex (1973) for bottled fluids to be accurately monitored. During a 14-month period this test detected faults in 3.3% of the sterilizing cycles, representing five distinct episodes of sterilization failure that passed unnoticed by the conventional controls of physical measurements and sterility testing. There were no failures of sterilization as detected by conventional techniques which were not indicated by the spore test. PMID:458140

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

NASA Astrophysics Data System (ADS)

Yi, Zhehan

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

Fault Detection for Automotive Shock Absorber

NASA Astrophysics Data System (ADS)

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

2015-11-01

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

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.

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

NASA Technical Reports Server (NTRS)

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

2013-01-01

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

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.

A Diagnostic Approach for Electro-Mechanical Actuators in Aerospace Systems

NASA Technical Reports Server (NTRS)

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

2009-01-01

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

Switch failure diagnosis based on inductor current observation for boost converters

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

A robust data fusion scheme for integrated navigation systems employing fault detection methodology augmented with fuzzy adaptive filtering

NASA Astrophysics Data System (ADS)

Ushaq, Muhammad; Fang, Jiancheng

2013-10-01

Integrated navigation systems for various applications, generally employs the centralized Kalman filter (CKF) wherein all measured sensor data are communicated to a single central Kalman filter. The advantage of CKF is that there is a minimal loss of information and high precision under benign conditions. But CKF may suffer computational overloading, and poor fault tolerance. The alternative is the federated Kalman filter (FKF) wherein the local estimates can deliver optimal or suboptimal state estimate as per certain information fusion criterion. FKF has enhanced throughput and multiple level fault detection capability. The Standard CKF or FKF require that the system noise and the measurement noise are zero-mean and Gaussian. Moreover it is assumed that covariance of system and measurement noises remain constant. But if the theoretical and actual statistical features employed in Kalman filter are not compatible, the Kalman filter does not render satisfactory solutions and divergence problems also occur. To resolve such problems, in this paper, an adaptive Kalman filter scheme strengthened with fuzzy inference system (FIS) is employed to adapt the statistical features of contributing sensors, online, in the light of real system dynamics and varying measurement noises. The excessive faults are detected and isolated by employing Chi Square test method. As a case study, the presented scheme has been implemented on Strapdown Inertial Navigation System (SINS) integrated with the Celestial Navigation System (CNS), GPS and Doppler radar using FKF. Collectively the overall system can be termed as SINS/CNS/GPS/Doppler integrated navigation system. The simulation results have validated the effectiveness of the presented scheme with significantly enhanced precision, reliability and fault tolerance. Effectiveness of the scheme has been tested against simulated abnormal errors/noises during different time segments of flight. It is believed that the presented scheme can be applied to the navigation system of aircraft or unmanned aerial vehicle (UAV).

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.

Real-Time Condition Monitoring and Fault Diagnosis of Gear Train Systems Using Instantaneous Angular Speed (IAS) Analysis

NASA Astrophysics Data System (ADS)

Sait, Abdulrahman S.

This dissertation presents a reliable technique for monitoring the condition of rotating machinery by applying instantaneous angular speed (IAS) analysis. A new analysis of the effects of changes in the orientation of the line of action and the pressure angle of the resultant force acting on gear tooth profile of spur gear under different levels of tooth damage is utilized. The analysis and experimental work discussed in this dissertation provide a clear understating of the effects of damage on the IAS by analyzing the digital signals output of rotary incremental optical encoder. A comprehensive literature review of state of the knowledge in condition monitoring and fault diagnostics of rotating machinery, including gearbox system is presented. Progress and new developments over the past 30 years in failure detection techniques of rotating machinery including engines, bearings and gearboxes are thoroughly reviewed. This work is limited to the analysis of a gear train system with gear tooth surface faults utilizing angular motion analysis technique. Angular motion data were acquired using an incremental optical encoder. Results are compared to a vibration-based technique. The vibration data were acquired using an accelerometer. The signals were obtained and analyzed in the phase domains using signal averaging to determine the existence and position of faults on the gear train system. Forces between the mating teeth surfaces are analyzed and simulated to validate the influence of the presence of damage on the pressure angle and the IAS. National Instruments hardware is used and NI LabVIEW software code is developed for real-time, online condition monitoring systems and fault detection techniques. The sensitivity of optical encoders to gear fault detection techniques is experimentally investigated by applying IAS analysis under different gear damage levels and different operating conditions. A reliable methodology is developed for selecting appropriate testing/operating conditions of a rotating system to generate an alarm system for damage detection.

Optimization of Second Fault Detection Thresholds to Maximize Mission POS

NASA Technical Reports Server (NTRS)

Anzalone, Evan

2018-01-01

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

Study of New Method Combined Ultra-High Frequency (UHF) Method and Ultrasonic Method on PD Detection for GIS

NASA Astrophysics Data System (ADS)

Li, Yanran; Chen, Duo; Zhang, Jiwei; Chen, Ning; Li, Xiaoqi; Gong, Xiaojing

2017-09-01

GIS (gas insulated switchgear), is an important equipment in power system. Partial discharge plays an important role in detecting the insulation performance of GIS. UHF method and ultrasonic method frequently used in partial discharge (PD) detection for GIS. It is necessary to investigate UHF method and ultrasonic method for partial discharge in GIS. However, very few studies have been conducted on the method combined this two methods. From the view point of safety, a new method based on UHF method and ultrasonic method of PD detection for GIS is proposed in order to greatly enhance the ability of anti-interference of signal detection and the accuracy of fault localization. This paper presents study aimed at clarifying the effect of the new method combined UHF method and ultrasonic method. Partial discharge tests were performed in laboratory simulated environment. Obtained results show the ability of anti-interference of signal detection and the accuracy of fault localization for this new method combined UHF method and ultrasonic method.

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.

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

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

Frank, Stephen; Heaney, Michael; Jin, Xin

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

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

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

Frank, Stephen; Heaney, Michael; Jin, Xin

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

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

DTIC Science & Technology

2014-10-02

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

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

PubMed

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

2016-05-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Automatic Fault Characterization via Abnormality-Enhanced Classification

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

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

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

Providing an empirical basis for optimizing the verification and testing phases of software development

NASA Technical Reports Server (NTRS)

Briand, Lionel C.; Basili, Victor R.; Hetmanski, Christopher J.

1992-01-01

Applying equal testing and verification effort to all parts of a software system is not very efficient, especially when resources are limited and scheduling is tight. Therefore, one needs to be able to differentiate low/high fault density components so that the testing/verification effort can be concentrated where needed. Such a strategy is expected to detect more faults and thus improve the resulting reliability of the overall system. This paper presents an alternative approach for constructing such models that is intended to fulfill specific software engineering needs (i.e. dealing with partial/incomplete information and creating models that are easy to interpret). Our approach to classification is as follows: (1) to measure the software system to be considered; and (2) to build multivariate stochastic models for prediction. We present experimental results obtained by classifying FORTRAN components developed at the NASA/GSFC into two fault density classes: low and high. Also we evaluate the accuracy of the model and the insights it provides into the software process.

Analysis of variation in oil pressure in lubricating system

NASA Astrophysics Data System (ADS)

Sharma, Sumit; Upreti, Mritunjay; Sharma, Bharat; Poddar, Keshav

2018-05-01

Automotive Maintenance for an engine contributes to its reliability, energy efficiency and repair cost reduction. Modeling of engine performance and fault detection require large amount of data, which are usually obtained on test benches. This report presents a methodical study on analysis of variation in lubrication system of various medium speed engines. Further this study is limited to the influence of Engine Oil Pressure on frictional losses, Torque analysis for various Oil Pressures and an analytical analysis of engine Lubrication System. The data collected from various Engines under diagnostics is represented graphically. Finally the illustrated results were used as a viable source for detection and troubleshooting of faults in Lubrication System of regular passenger vehicle.

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 chances of recovery from any hardware or software fault is detailed. A generic fault detection and correction software structure is used, allowing additions, deletions, and adjustments to fault detection and correction rules. This software structure is fed by in-line fault tests that are also able to take appropriate actions to avoid corruption of the data stream.

A case study on pseudo 3-D Chirp sub-bottom profiler (SBP) survey for the detection of a fault trace in shallow sedimentary layers at gas hydrate site in the Ulleung Basin, East Sea

NASA Astrophysics Data System (ADS)

Kim, Young-Jun; Koo, Nam-Hyung; Cheong, Snons; Kim, Jung-Ki; Chun, Jong-Hwa; Shin, Sung-Ryul; Riedel, Michael; Lee, Ho-Young

2016-10-01

A pseudo 3-D Chirp sub-bottom profiler (SBP) survey was conducted to define the extension of a fault that was previously identified on low-resolution 2-D seismic data with an emphasis on the shallow sedimentary layers and to determine if the fault extends to the seafloor. The geophysical survey was conducted as part of an environmental impact assessment for a proposed gas hydrate production test in the Ulleung Basin, East Sea. The Chirp SBP raw data were acquired over an area of 1 km × 1 km with an average line spacing of 20 m. To produce a 3-D Chirp SBP volume, we developed an optimal processing sequence that was divided into two steps. The first phase of 2-D data processing included a sweep signature estimation, correlation, deconvolution, swell effect correction, and migration. The second phase of 3-D data processing was composed of a bin design, bin gathering of the final processed 2-D data set, amplitude normalization, and residual statics correction. The 3-D Chirp SBP volume provides enhanced imaging especially due to the residual static processing using a moving average method and shows better continuity of the sedimentary layers and consistency of the reflection events than the individual 2-D lines. Deformation of the seafloor as a result of the fault was detected, and the fault offset increases in the deeper sedimentary layers. We also determined that the fault strikes northwest-southeast. However, the shallow sub-seafloor sediments have high porosities and therefore do not exhibit brittle fault-behavior but rather deform continuously due to fault movement.

The development of an interim generalized gate logic software simulator

NASA Technical Reports Server (NTRS)

Mcgough, J. G.; Nemeroff, S.

1985-01-01

A proof-of-concept computer program called IGGLOSS (Interim Generalized Gate Logic Software Simulator) was developed and is discussed. The simulator engine was designed to perform stochastic estimation of self test coverage (fault-detection latency times) of digital computers or systems. A major attribute of the IGGLOSS is its high-speed simulation: 9.5 x 1,000,000 gates/cpu sec for nonfaulted circuits and 4.4 x 1,000,000 gates/cpu sec for faulted circuits on a VAX 11/780 host computer.

Evaluate the application of modal test and analysis processes to structural fault detection in MSFC - STS project elements

NASA Technical Reports Server (NTRS)

Springer, William T.

1987-01-01

The Space Transportation System (STS) is a complex and expensive flight system intended to carry unique payloads into low Earth orbit and return. A catastrophic failure, such as STS 51-L, resulted in the loss of both human life as well as expensive and unique hardware. The impact of this incident reaffirms the need to do everything possible to ensure the integrity and reliability of STS. One means of achieving this goal is to expand the number of inspection technologies available. Reported here is the evaluation of the use of modal analysis and test techniques for the purpose of assessing the structural integrity of STS components for which Marshall Space Flight Center has responsibility. This entailed reviewing existing literature and developing a low-level experimental program determine the feasibility of using this technology for structural fault detection.

Performance monitor system functional simulator, environmental data, orbiter 101(HFT)

NASA Technical Reports Server (NTRS)

Parker, F. W.

1974-01-01

Information concerning the environment component of the space shuttle performance monitor system simulator (PMSS) and those subsystems operational on the shuttle orbiter 101 used for horizontal flight test (HFT) is provided, along with detailed data for the shuttle performance monitor system (PMS) whose software requirements evolve from three basic PMS functions: (1) fault detection and annunciation; (2) subsystem measurement management; and (3) subsystem configuration management. Information relative to the design and operation of Orbiter systems for HFT is also presented, and the functional paths are identified to the lowest level at which the crew can control the system functions. Measurement requirements are given which are necessary to adequately monitor the health status of the system. PMS process requirements, relative to the measurements which are necessary for fault detection and annunciation of a failed functional path, consist of measurement characteristics, tolerance limits, precondition tests, and correlation measurements.

A second generation experiment in fault-tolerant software

NASA Technical Reports Server (NTRS)

Knight, J. C.

1986-01-01

Information was collected on the efficacy of fault-tolerant software by conducting two large-scale controlled experiments. In the first, an empirical study of multi-version software (MVS) was conducted. The second experiment is an empirical evaluation of self testing as a method of error detection (STED). The purpose ot the MVS experiment was to obtain empirical measurement of the performance of multi-version systems. Twenty versions of a program were prepared at four different sites under reasonably realistic development conditions from the same specifications. The purpose of the STED experiment was to obtain empirical measurements of the performance of assertions in error detection. Eight versions of a program were modified to include assertions at two different sites under controlled conditions. The overall structure of the testing environment for the MVS experiment and its status are described. Work to date in the STED experiment is also presented.

The effect of the Baton Rouge fault on flow in the Abita aquifer of southeastern Louisiana

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

Rapp, T.R.

1993-03-01

The ground-water resources of southern Tangipahoa Parish and adjacent areas were studied to determine their potential for development as an alternative to the Mississippi River as a water supply source for Jefferson Parish, Louisiana. The study area, in southeastern Louisiana, is underlain by eight major aquifers and is crossed by a fault zone, referred to as the Baton Rouge fault. The fault restricts the flow of water in the aquifers of intermediate depth. Data from a test well drilling program and geophysical logs of a nearby oil well indicated that a significant freshwater aquifer that provides water to a nearbymore » municipality was actually the Abita aquifer and not the Covington aquifer, as was previously thought. The Abita aquifer, a shallower aquifer with a lower hydraulic conductivity, had been displaced to a position equivalent to that of the Covington aquifer by the Baton Rouge fault. An additional final test well drilled south of the fault penetrated the leading edge of a wedge-shaped saltwater interface. Analysis of lithologic and geophysical logs indicated that the Abita aquifer has a well-sorted, clean sand at the base of the aquifer and substantial amounts of clay in the top two-thirds of the aquifer. Geophysical logs of oil test wells south of the fault zone indicated that the sand thickens substantially to the south. The thicker sand south of a public supply well that pumps water from the Abita aquifer and the higher hydraulic conductivity of the lower part of the aquifer where the saline water was detected indicate that a much larger percentage of recharge to the public supply well may come from the south than was originally thought.« less

Intelligent classifier for dynamic fault patterns based on hidden Markov model

NASA Astrophysics Data System (ADS)

Xu, Bo; Feng, Yuguang; Yu, Jinsong

2006-11-01

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

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

NASA Astrophysics Data System (ADS)

Ambur, Ramakrishnan; Rinderknecht, Stephan

2018-03-01

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

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.

Analysis of pseudocolor transformations of ERTS-1 images of Southern California area. [geological faults and lineaments

NASA Technical Reports Server (NTRS)

Merifield, P. M. (Principal Investigator); Lamar, D. L.; Stratton, R. H.; Lamar, J. V.; Gazley, C., Jr.

1974-01-01

The author has identified the following significant results. Representative faults and lineaments, natural features on the Mojave Desert, and cultural features of the southern California area were studied on ERTS-1 images. The relative appearances of the features were compared on a band 4 and 5 subtraction image, its pseudocolor transformation, and pseudocolor images of bands 4, 5, and 7. Selected features were also evaluated in a test given students at the University of California, Los Angeles. Observations and the test revealed no significant improvement in the ability to detect and locate faults and lineaments on the pseudocolor transformations. With the exception of dry lake surfaces, no enhancement of the features studied was observed on the bands 4 and 5 subtraction images. Geologic and geographic features characterized by minor tonal differences on relatively flat surfaces were enhanced on some of the pseudocolor images.

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

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

Benwell, Andrew; Kemp, Mark; Burkhart, Craig

2010-06-11

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

Non-Intrusive Cable Tester

NASA Technical Reports Server (NTRS)

Medelius, Pedro J. (Inventor); Simpson, Howard J. (Inventor)

1999-01-01

A cable tester is described for low frequency testing of a cable for faults. The tester allows for testing a cable beyond a point where a signal conditioner is installed, minimizing the number of connections which have to be disconnected. A magnetic pickup coil is described for detecting a test signal injected into the cable. A narrow bandpass filter is described for increasing detection of the test signal. The bandpass filter reduces noise so that a high gain amplifier provided for detecting a test signal is not completely saturate by noise. To further increase the accuracy of the cable tester, processing gain is achieved by comparing the signal from the amplifier with at least one reference signal emulating the low frequency input signal injected into the cable. Different processing techniques are described evaluating a detected signal.

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

PubMed

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

2014-01-01

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

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

PubMed Central

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

2014-01-01

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

Integral Sensor Fault Detection and Isolation for Railway Traction Drive.

PubMed

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

2018-05-13

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

Integral Sensor Fault Detection and Isolation for Railway Traction Drive

PubMed Central

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

2018-01-01

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

Aircraft Fault Detection Using Real-Time Frequency Response Estimation

NASA Technical Reports Server (NTRS)

Grauer, Jared A.

2016-01-01

A real-time method for estimating time-varying aircraft frequency responses from input and output measurements was demonstrated. The Bat-4 subscale airplane was used with NASA Langley Research Center's AirSTAR unmanned aerial flight test facility to conduct flight tests and collect data for dynamic modeling. Orthogonal phase-optimized multisine inputs, summed with pilot stick and pedal inputs, were used to excite the responses. The aircraft was tested in its normal configuration and with emulated failures, which included a stuck left ruddervator and an increased command path latency. No prior knowledge of a dynamic model was used or available for the estimation. The longitudinal short period dynamics were investigated in this work. Time-varying frequency responses and stability margins were tracked well using a 20 second sliding window of data, as compared to a post-flight analysis using output error parameter estimation and a low-order equivalent system model. This method could be used in a real-time fault detection system, or for other applications of dynamic modeling such as real-time verification of stability margins during envelope expansion tests.

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

PubMed

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

2007-07-01

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

An improved PCA method with application to boiler leak detection.

PubMed

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

2005-07-01

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

Early Oscillation Detection for DC/DC Converter Fault Diagnosis

NASA Technical Reports Server (NTRS)

Wang, Bright L.

2011-01-01

The electrical power system of a spacecraft plays a very critical role for space mission success. Such a modern power system may contain numerous hybrid DC/DC converters both inside the power system electronics (PSE) units and onboard most of the flight electronics modules. One of the faulty conditions for DC/DC converter that poses serious threats to mission safety is the random occurrence of oscillation related to inherent instability characteristics of the DC/DC converters and design deficiency of the power systems. To ensure the highest reliability of the power system, oscillations in any form shall be promptly detected during part level testing, system integration tests, flight health monitoring, and on-board fault diagnosis. The popular gain/phase margin analysis method is capable of predicting stability levels of DC/DC converters, but it is limited only to verification of designs and to part-level testing on some of the models. This method has to inject noise signals into the control loop circuitry as required, thus, interrupts the DC/DC converter's normal operation and increases risks of degrading and damaging the flight unit. A novel technique to detect oscillations at early stage for flight hybrid DC/DC converters was developed.

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

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

Zhao Jinsong; Huang Jianchao; Sun Wei

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

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.

Detecting Blind Fault with Fractal and Roughness Factors from High Resolution LiDAR DEM at Taiwan

NASA Astrophysics Data System (ADS)

Cheng, Y. S.; Yu, T. T.

2014-12-01

There is no obvious fault scarp associated with blind fault. The traditional method of mapping this unrevealed geological structure is the cluster of seismicity. Neither the seismic event nor the completeness of cluster could be captured by network to chart the location of the entire possible active blind fault within short period of time. High resolution DEM gathered by LiDAR could denote actual terrain information despite the existence of plantation. 1-meter interval DEM of mountain region at Taiwan is utilized by fractal, entropy and roughness calculating with MATLAB code. By jointing these handing, the regions of non-sediment deposit are charted automatically. Possible blind fault associated with Chia-Sen earthquake at southern Taiwan is served as testing ground. GIS layer help in removing the difference from various geological formation, then multi-resolution fractal index is computed around the target region. The type of fault movement controls distribution of fractal index number. The scale of blind fault governs degree of change in fractal index. Landslide induced by rainfall and/or earthquake possesses larger degree of geomorphology alteration than blind fault; special treatment in removing these phenomena is required. Highly weathered condition at Taiwan should erase the possible trace remained upon DEM from the ruptured of blind fault while reoccurrence interval is higher than hundreds of years. This is one of the obstacle in finding possible blind fault at Taiwan.

Detecting the crankshaft torsional vibration of diesel engines for combustion related diagnosis

NASA Astrophysics Data System (ADS)

Charles, P.; Sinha, Jyoti K.; Gu, F.; Lidstone, L.; Ball, A. D.

2009-04-01

Early fault detection and diagnosis for medium-speed diesel engines is important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion related fault detection capability of crankshaft torsional vibration. The encoder signal, often used for shaft speed measurement, has been used to construct the instantaneous angular speed (IAS) waveform, which actually represents the signature of the torsional vibration. Earlier studies have shown that the IAS signal and its fast Fourier transform (FFT) analysis are effective for monitoring engines with less than eight cylinders. The applicability to medium-speed engines, however, is strongly contested due to the high number of cylinders and large moment of inertia. Therefore the effectiveness of the FFT-based approach has further been enhanced by improving the signal processing to determine the IAS signal and subsequently tested on a 16-cylinder engine. In addition, a novel method of presentation, based on the polar coordinate system of the IAS signal, has also been introduced; to improve the discrimination features of the faults compared to the FFT-based approach of the IAS signal. The paper discusses two typical experimental studies on 16- and 20-cylinder engines, with and without faults, and the diagnosis results by the proposed polar presentation method. The results were also compared with the earlier FFT-based method of the IAS signal.

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.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

PubMed

Duong, Bach Phi; Kim, Jong-Myon

2018-04-07

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance.

An improved low-frequency earthquakes catalogue in the vicinity of the late-interseismic central Alpine Fault, Southern Alps, New Zealand

NASA Astrophysics Data System (ADS)

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

2017-04-01

Characterising the seismicity associated with slow deformation in the vicinity of the Alpine Fault may provide constraints on the state of stress of this major transpressive margin prior to a large (≥M8) earthquake. Here, we use recently detected tectonic tremor and low-frequency earthquakes (LFEs) to examine how slow tectonic deformation is loading the Alpine Fault toward an anticipated large rupture. We initially work with a continous seismic dataset collected between 2009 and 2012 from an array of short-period seismometers, the Southern Alps Microearthquake Borehole Array. Fourteen primary LFE templates, found through visual inspection within previously identified tectonic tremor, are used in an iterative matched-filter and stacking routine. This method allows the detection of similar signals and establishes LFE families with common locations. We thus generate a 36 month catalogue of 10718 LFEs. The detections are then combined for each LFE family using phase-weighted stacking to yield a signal with the highest possible signal to noise ratio. We found phase-weighted stacking to be successful in increasing the number of LFE detections by roughly 20%. Phase-weighted stacking also provides cleaner phase arrivals of apparently impulsive nature allowing more precise phase picks. We then compute non-linear earthquake locations using a 3D velocity model and find LFEs to occur below the seismogenic zone at depths of 18-34 km, locating on or near the proposed deep extent of the Alpine Fault. To gain insight into deep fault slip behaviour, a detailed study of the spatial-temporal evolution of LFEs is required. We thus generate a more extensive catalogue of LFEs spanning the years 2009 to 2016 using a different technique to detect LFEs more efficiently. This time 638 synthetic waveforms are used as primary templates in the match-filter routine. Of those, 38 templates yield no detections over our 7-yr study period. The remaining 600 templates end up detecting between 370 and 730 events each totalling ˜310 000 detections. We then focus on only keeping the detections that robustly stack (i.e. representing real LFEs) for each synthetic template hence creating new LFE templates. From there, we rerun the match-filter routine with our new LFE templates. Finally, each LFE template and its subsequent detections form a LFE family, itself associated with a single source. Initial testing shows that this technique paired up with phase-weighted stacking increases the number of LFE families and overall detected events roughly thirtyfold. Our next step is to study in detail the spatial and temporal activity of our LFEs. This new catalogue should provide new insight into the deep central Alpine Fault structure and its slip behaviour.

Automatic Detection of Previously-Unseen Application States for Deployment Environment Testing and Analysis

PubMed Central

Murphy, Christian; Vaughan, Moses; Ilahi, Waseem; Kaiser, Gail

2010-01-01

For large, complex software systems, it is typically impossible in terms of time and cost to reliably test the application in all possible execution states and configurations before releasing it into production. One proposed way of addressing this problem has been to continue testing and analysis of the application in the field, after it has been deployed. A practical limitation of many such automated approaches is the potentially high performance overhead incurred by the necessary instrumentation. However, it may be possible to reduce this overhead by selecting test cases and performing analysis only in previously-unseen application states, thus reducing the number of redundant tests and analyses that are run. Solutions for fault detection, model checking, security testing, and fault localization in deployed software may all benefit from a technique that ignores application states that have already been tested or explored. In this paper, we present a solution that ensures that deployment environment tests are only executed in states that the application has not previously encountered. In addition to discussing our implementation, we present the results of an empirical study that demonstrates its effectiveness, and explain how the new approach can be generalized to assist other automated testing and analysis techniques intended for the deployment environment. PMID:21197140

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.

River gradient anomalies reveal recent tectonic movements when assuming an exponential gradient decrease along a river course

NASA Astrophysics Data System (ADS)

Žibret, Gorazd; Žibret, Lea

2017-03-01

High resolution digital models, combined with GIS or other terrain modelling software, allow many new possibilities in geoscience. In this paper we develop, describe and test a novel method, the GLA method, to detect active tectonic uplift or subsidence along river courses. It is a modification of Hack's SL-index method in order to overcome the disadvantages of the latter. The core assumption of the GLA method is that over geological time river profiles quickly adjust to follow an exponential decrease in elevation along the river course. Any large deviation can be attributed to active tectonic movement, or to disturbances in erosion/sedimentation processes caused by an anthropogenic structure (e.g. artificial dam). During the testing phase, the locations of identified deviations were compared to the locations of faults, identified on a 1:100,000 geological map. Results show that higher magnitude deviations are found within a maximum radius of 200 m from the fault, and the majority of detected deviations within a maximum radius of 600 m from faults or thrusts. However, these results are not the best that could be obtained because the geological map that was used (and the only one available for the area) is not of the appropriate scale, and was therefore not precise enough. Comparison of deviation magnitudes against PSInSAR measurements of vertical displacements in the vicinity revealed that in spite of the very few suitable points available, a good correlation between both independent methods was obtained (R2 = 0.68 for the E research area and R2 = 0.69 for the W research area). The GLA method was applied to the three test sites where previous studies have shown active tectonic movements. It shows that deviations occur at the intersections between active faults and river courses, as well as also correctly detected active uplift, attributed to the increased sedimentation rate above an artificial hydropower dam, and an increased erosion rate below. The method gives promising results, and it is acknowledged that the GLA method needs to be tested in other locations around the world.

Fault detection and fault tolerance in robotics

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

Improvement in operational characteristics of KEPCO’s line-commutation-type superconducting hybrid fault current limiter

NASA Astrophysics Data System (ADS)

Yim, S.-W.; Park, B.-C.; Jeong, Y.-T.; Kim, Y.-J.; Yang, S.-E.; Kim, W.-S.; Kim, H.-R.; Du, H.-I.

2013-01-01

A 22.9 kV class hybrid fault current limiter (FCL) developed by Korea Electric Power Corporation and LS Industrial Systems in 2006 operates using the line commutation mechanism and begins to limit the fault current after the first half-cycle. The first peak of the fault current is available for protective coordination in the power system. However, it also produces a large electromagnetic force and imposes a huge stress on power facilities such as the main transformer and gas-insulated switchgear. In this study, we improved the operational characteristics of the hybrid FCL in order to reduce the first peak of the fault current. While maintaining the structure of the hybrid FCL system, we developed a superconducting module that detects and limits the fault current during the first half-cycle. To maintain the protective coordination capacity, the hybrid FCL was designed to reduce the first peak value of the fault current by up to approximately 30%. The superconducting module was also designed to produce a minimum AC loss, generating a small, uniform magnetic field distribution during normal operation. Performance tests confirmed that when applied to the hybrid FCL, the superconducting module showed successful current limiting operation without any damage.

Arc Fault Detection & Localization by Electromagnetic-Acoustic Remote Sensing

NASA Astrophysics Data System (ADS)

Vasile, C.; Ioana, C.

2017-05-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

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

PubMed Central

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

2015-01-01

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

Adaptive noise cancelling and time-frequency techniques for rail surface defect detection

NASA Astrophysics Data System (ADS)

Liang, B.; Iwnicki, S.; Ball, A.; Young, A. E.

2015-03-01

Adaptive noise cancelling (ANC) is a technique which is very effective to remove additive noises from the contaminated signals. It has been widely used in the fields of telecommunication, radar and sonar signal processing. However it was seldom used for the surveillance and diagnosis of mechanical systems before late of 1990s. As a promising technique it has gradually been exploited for the purpose of condition monitoring and fault diagnosis. Time-frequency analysis is another useful tool for condition monitoring and fault diagnosis purpose as time-frequency analysis can keep both time and frequency information simultaneously. This paper presents an ANC and time-frequency application for railway wheel flat and rail surface defect detection. The experimental results from a scaled roller test rig show that this approach can significantly reduce unwanted interferences and extract the weak signals from strong background noises. The combination of ANC and time-frequency analysis may provide us one of useful tools for condition monitoring and fault diagnosis of railway vehicles.

The use of automatic programming techniques for fault tolerant computing systems

NASA Technical Reports Server (NTRS)

Wild, C.

1985-01-01

It is conjectured that the production of software for ultra-reliable computing systems such as required by Space Station, aircraft, nuclear power plants and the like will require a high degree of automation as well as fault tolerance. In this paper, the relationship between automatic programming techniques and fault tolerant computing systems is explored. Initial efforts in the automatic synthesis of code from assertions to be used for error detection as well as the automatic generation of assertions and test cases from abstract data type specifications is outlined. Speculation on the ability to generate truly diverse designs capable of recovery from errors by exploring alternate paths in the program synthesis tree is discussed. Some initial thoughts on the use of knowledge based systems for the global detection of abnormal behavior using expectations and the goal-directed reconfiguration of resources to meet critical mission objectives are given. One of the sources of information for these systems would be the knowledge captured during the automatic programming process.

Determination of accuracy of winding deformation method using kNN based classifier used for 3 MVA transformer

NASA Astrophysics Data System (ADS)

Ahmed, Mustafa Wasir; Baishya, Manash Jyoti; Sharma, Sasanka Sekhor; Hazarika, Manash

2018-04-01

This paper presents a detecting system on power transformer in transformer winding, core and on load tap changer (OLTC). Accuracy of winding deformation is determined using kNN based classifier. Winding deformation in power transformer can be measured using sweep frequency response analysis (SFRA), which can enhance the diagnosis accuracy to a large degree. It is suggested that in the results minor deformation faults can be detected at frequency range of 1 mHz to 2 MHz. The values of RCL parameters are changed when faults occur and hence frequency response of the winding will change accordingly. The SFRA data of tested transformer is compared with reference trace. The difference between two graphs indicate faults in the transformer. The deformation between 1 mHz to 1kHz gives winding deformation, 1 kHz to 100 kHz gives core deformation and 100 kHz to 2 MHz gives OLTC deformation.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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

NASA Astrophysics Data System (ADS)

Zeng, Hongqing; Vukovic, Alex; Huang, Changcheng

2005-09-01

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

SU-E-T-660: Quantitative Fault Testing for Commissioning of Proton Therapy Machines

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

Reilly, M; Rankine, L; Grantham, K

2015-06-15

Purpose: To ensure proper fault testing for the first single room proton therapy machine by establishing a common set of acceptance testing and commissioning parameters with the manufacturer. The following work details the parameters tested and associated results. Methods: Dose rates in service mode were varied to ensure that when the threshold for maximum or minimum MU/min was met, the beam promptly shut off. The flatness parameter was tested by purposely assigning an incorrect secondary scatter, to ensure the beam shut off when detecting a heterogeneous profile. The beam symmetry parameter was tested by altering the steering coil up tomore » 3.0A, thereby forcing the beam to be asymmetric and shut off. Lastly, the quench system was tested by ramping down the magnet to 5% capacity, whereby the quench button was engaged to bring down the magnet current to a safe level. Results: A dose rate increase or decrease in excess of 10% shut the beam off within 5 seconds as observed by the current on a Matrixx ionization chamber array (IBA Dosimetry, Bartlett, TN) A 3.0A change in the beam steering coil introduced a 2% change in the flatness and symmetry profiles with respect to baseline measurements resulting in the beam shutting off within 5 seconds. An incorrect 2nd scatterer introduced a flatness of 4.1% and symmetry of 6.4% which immediately triggered a beam shut off. Finally, the quench system worked as expected during the ramp down procedure. Conclusion: A fault testing plan to check dosimetric faults and the quench system was performed for the first single room proton therapy system. All dosimetric parameters and machine conditions were met to our satisfaction. We propose that the same type of fault testing should be applied to any proton system during commissioning, including scanning beam systems.« less

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

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

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

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

Tunable architecture for aircraft fault detection

NASA Technical Reports Server (NTRS)

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

2012-01-01

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

Detailed Vibration Analysis of Pinion Gear with Time-Frequency Methods

NASA Technical Reports Server (NTRS)

Mosher, Marianne; Pryor, Anna H.; Lewicki, David G.

2003-01-01

In this paper, the authors show a detailed analysis of the vibration signal from the destructive testing of a spiral bevel gear and pinion pair containing seeded faults. The vibration signal is analyzed in the time domain, frequency domain and with four time-frequency transforms: the Short Time Frequency Transform (STFT), the Wigner-Ville Distribution with the Choi-Williams kernel (WV-CW), the Continuous Wavelet' Transform (CWT) and the Discrete Wavelet Transform (DWT). Vibration data of bevel gear tooth fatigue cracks, under a variety of operating load levels and damage conditions, are analyzed using these methods. A new metric for automatic anomaly detection is developed and can be produced from any systematic numerical representation of the vibration signals. This new metric reveals indications of gear damage with all of the time-frequency transforms, as well as time and frequency representations, on this data set. Analysis with the CWT detects changes in the signal at low torque levels not found with the other transforms. The WV-CW and CWT use considerably more resources than the STFT and the DWT. More testing of the new metric is needed to determine its value for automatic anomaly detection and to develop fault detection methods for the metric.

A Review of Transmission Diagnostics Research at NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Zakajsek, James J.

1994-01-01

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

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

PubMed Central

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

2017-01-01

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

Evolution of solid rocket booster component testing

NASA Technical Reports Server (NTRS)

Lessey, Joseph A.

1989-01-01

The evolution of one of the new generation of test sets developed for the Solid Rocket Booster of the U.S. Space Transportation System. Requirements leading to factory checkout of the test set are explained, including the evolution from manual to semiautomated toward fully automated status. Individual improvements in the built-in test equipment, self-calibration, and software flexibility are addressed, and the insertion of fault detection to improve reliability is discussed.

Automatic characteristic frequency association and all-sideband demodulation for the detection of a bearing fault

NASA Astrophysics Data System (ADS)

Firla, Marcin; Li, Zhong-Yang; Martin, Nadine; Pachaud, Christian; Barszcz, Tomasz

2016-12-01

This paper proposes advanced signal-processing techniques to improve condition monitoring of operating machines. The proposed methods use the results of a blind spectrum interpretation that includes harmonic and sideband series detection. The first contribution of this study is an algorithm for automatic association of harmonic and sideband series to characteristic fault frequencies according to a kinematic configuration. The approach proposed has the advantage of taking into account a possible slip of the rolling-element bearings. In the second part, we propose a full-band demodulation process from all sidebands that are relevant to the spectral estimation. To do so, a multi-rate filtering process in an iterative schema provides satisfying precision and stability over the targeted demodulation band, even for unsymmetrical and extremely narrow bands. After synchronous averaging, the filtered signal is demodulated for calculation of the amplitude and frequency modulation functions, and then any features that indicate faults. Finally, the proposed algorithms are validated on vibration signals measured on a test rig that was designed as part of the European Innovation Project 'KAStrion'. This rig simulates a wind turbine drive train at a smaller scale. The data show the robustness of the method for localizing and extracting a fault on the main bearing. The evolution of the proposed features is a good indicator of the fault severity.

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

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

Zhang Yumin; Lum, Kai-Yew; Wang Qingguo

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

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

NASA Astrophysics Data System (ADS)

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

2009-03-01

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

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

PubMed

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

2016-12-08

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

Gaining Insight Into Femtosecond-scale CMOS Effects using FPGAs

DTIC Science & Technology

2015-03-24

paths or detecting gross path delay faults , but for characterizing subtle aging effects, there is a need to isolate very short paths and detect very...data using COTS FPGAs and novel self-test. Hardware experiments using a 28 nm FPGA demonstrate isolation of small sets of transistors, detection of...hold the static configuration data specifying the LUT function. A set of inverters drive the SRAM contents into a pass-gate multiplexor tree; we

Test plan. GCPS task 7, subtask 7.1: IHM development

NASA Technical Reports Server (NTRS)

Greenberg, H. S.

1994-01-01

The overall objective of Task 7 is to identify cost-effective life cycle integrated health management (IHM) approaches for a reusable launch vehicle's primary structure. Acceptable IHM approaches must: eliminate and accommodate faults through robust designs, identify optimum inspection/maintenance periods, automate ground and on-board test and check-out, and accommodate and detect structural faults by providing wide and localized area sensor and test coverage as required. These requirements are elements of our targeted primary structure low cost operations approach using airline-like maintenance by exception philosophies. This development plan will follow an evolutionary path paving the way to the ultimate development of flight-quality production, operations, and vehicle systems. This effort will be focused on maturing the recommended sensor technologies required for localized and wide area health monitoring to a technology readiness level (TRL) of 6 and to establish flight ready system design requirements. The following is a brief list of IHM program objectives: design out faults by analyzing material properties, structural geometry, and load and environment variables and identify failure modes and damage tolerance requirements; design in system robustness while meeting performance objectives (weight limitations) of the reusable launch vehicle primary structure; establish structural integrity margins to preclude the need for test and checkout and predict optimum inspection/maintenance periods through life prediction analysis; identify optimum fault protection system concept definitions combining system robustness and integrity margins established above with cost effective health monitoring technologies; and use coupons, panels, and integrated full scale primary structure test articles to identify, evaluate, and characterize the preferred NDE/NDI/IHM sensor technologies that will be a part of the fault protection system.

Semi-automatic mapping of fault rocks on a Digital Outcrop Model, Gole Larghe Fault Zone (Southern Alps, Italy)

NASA Astrophysics Data System (ADS)

Mittempergher, Silvia; Vho, Alice; Bistacchi, Andrea

2016-04-01

A quantitative analysis of fault-rock distribution in outcrops of exhumed fault zones is of fundamental importance for studies of fault zone architecture, fault and earthquake mechanics, and fluid circulation. We present a semi-automatic workflow for fault-rock mapping on a Digital Outcrop Model (DOM), developed on the Gole Larghe Fault Zone (GLFZ), a well exposed strike-slip fault in the Adamello batholith (Italian Southern Alps). The GLFZ has been exhumed from ca. 8-10 km depth, and consists of hundreds of individual seismogenic slip surfaces lined by green cataclasites (crushed wall rocks cemented by the hydrothermal epidote and K-feldspar) and black pseudotachylytes (solidified frictional melts, considered as a marker for seismic slip). A digital model of selected outcrop exposures was reconstructed with photogrammetric techniques, using a large number of high resolution digital photographs processed with VisualSFM software. The resulting DOM has a resolution up to 0.2 mm/pixel. Most of the outcrop was imaged using images each one covering a 1 x 1 m2 area, while selected structural features, such as sidewall ripouts or stepovers, were covered with higher-resolution images covering 30 x 40 cm2 areas.Image processing algorithms were preliminarily tested using the ImageJ-Fiji package, then a workflow in Matlab was developed to process a large collection of images sequentially. Particularly in detailed 30 x 40 cm images, cataclasites and hydrothermal veins were successfully identified using spectral analysis in RGB and HSV color spaces. This allows mapping the network of cataclasites and veins which provided the pathway for hydrothermal fluid circulation, and also the volume of mineralization, since we are able to measure the thickness of cataclasites and veins on the outcrop surface. The spectral signature of pseudotachylyte veins is indistinguishable from that of biotite grains in the wall rock (tonalite), so we tested morphological analysis tools to discriminate them with respect to biotite. In higher resolution images this could be performed using circularity and size thresholds, however this could not be easily implemented in an automated procedure since the thresholds must be varied by the interpreter almost for each image. In 1 x 1 m images the resolution is generally too low to distinguish cataclasite and pseudotachylyte, so most of the time fault rocks were treated together. For this analysis we developed a fully automated workflow that, after applying noise correction, classification and skeletonization algorithms, returns labeled edge images of fault segments together with vector polylines associated to edge properties. Vector and edge properties represent a useful format to perform further quantitative analysis, for instance for classifying fault segments based on structural criteria, detect continuous fault traces, and detect the kind of termination of faults/fractures. This approach allows to collect statistically relevant datasets useful for further quantitative structural analysis.

A Three-Dimensional Receiver Operator Characteristic Surface Diagnostic Metric

NASA Technical Reports Server (NTRS)

Simon, Donald L.

2011-01-01

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

Generic, scalable and decentralized fault detection for robot swarms.

PubMed

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

2017-01-01

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

Generic, scalable and decentralized fault detection for robot swarms

PubMed Central

Christensen, Anders Lyhne; Timmis, Jon

2017-01-01

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

Along-strike variations of the partitioning of convergence across the Haiyuan fault system detected by InSAR

NASA Astrophysics Data System (ADS)

Daout, S.; Jolivet, R.; Lasserre, C.; Doin, M.-P.; Barbot, S.; Tapponnier, P.; Peltzer, G.; Socquet, A.; Sun, J.

2016-04-01

Oblique convergence across Tibet leads to slip partitioning with the coexistence of strike-slip, normal and thrust motion on major fault systems. A key point is to understand and model how faults interact and accumulate strain at depth. Here, we extract ground deformation across the Haiyuan Fault restraining bend, at the northeastern boundary of the Tibetan plateau, from Envisat radar data spanning the 2001-2011 period. We show that the complexity of the surface displacement field can be explained by the partitioning of a uniform deep-seated convergence. Mountains and sand dunes in the study area make the radar data processing challenging and require the latest developments in processing procedures for Synthetic Aperture Radar interferometry. The processing strategy is based on a small baseline approach. Before unwrapping, we correct for atmospheric phase delays from global atmospheric models and digital elevation model errors. A series of filtering steps is applied to improve the signal-to-noise ratio across high ranges of the Tibetan plateau and the phase unwrapping capability across the fault, required for reliable estimate of fault movement. We then jointly invert our InSAR time-series together with published GPS displacements to test a proposed long-term slip-partitioning model between the Haiyuan and Gulang left-lateral Faults and the Qilian Shan thrusts. We explore the geometry of the fault system at depth and associated slip rates using a Bayesian approach and test the consistency of present-day geodetic surface displacements with a long-term tectonic model. We determine a uniform convergence rate of 10 [8.6-11.5] mm yr-1 with an N89 [81-97]°E across the whole fault system, with a variable partitioning west and east of a major extensional fault-jog (the Tianzhu pull-apart basin). Our 2-D model of two profiles perpendicular to the fault system gives a quantitative understanding of how crustal deformation is accommodated by the various branches of this thrust/strike-slip fault system and demonstrates how the geometry of the Haiyuan fault system controls the partitioning of the deep secular motion.

Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis

NASA Astrophysics Data System (ADS)

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

2013-07-01

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

Simulative and experimental investigation on stator winding turn and unbalanced supply voltage fault diagnosis in induction motors using Artificial Neural Networks.

PubMed

Lashkari, Negin; Poshtan, Javad; Azgomi, Hamid Fekri

2015-11-01

The three-phase shift between line current and phase voltage of induction motors can be used as an efficient fault indicator to detect and locate inter-turn stator short-circuit (ITSC) fault. However, unbalanced supply voltage is one of the contributing factors that inevitably affect stator currents and therefore the three-phase shift. Thus, it is necessary to propose a method that is able to identify whether the unbalance of three currents is caused by ITSC or supply voltage fault. This paper presents a feedforward multilayer-perceptron Neural Network (NN) trained by back propagation, based on monitoring negative sequence voltage and the three-phase shift. The data which are required for training and test NN are generated using simulated model of stator. The experimental results are presented to verify the superior accuracy of the proposed method. Copyright © 2015. Published by Elsevier Ltd.

An experiment in software reliability

NASA Technical Reports Server (NTRS)

Dunham, J. R.; Pierce, J. L.

1986-01-01

The results of a software reliability experiment conducted in a controlled laboratory setting are reported. The experiment was undertaken to gather data on software failures and is one in a series of experiments being pursued by the Fault Tolerant Systems Branch of NASA Langley Research Center to find a means of credibly performing reliability evaluations of flight control software. The experiment tests a small sample of implementations of radar tracking software having ultra-reliability requirements and uses n-version programming for error detection, and repetitive run modeling for failure and fault rate estimation. The experiment results agree with those of Nagel and Skrivan in that the program error rates suggest an approximate log-linear pattern and the individual faults occurred with significantly different error rates. Additional analysis of the experimental data raises new questions concerning the phenomenon of interacting faults. This phenomenon may provide one explanation for software reliability decay.

Modeling and Performance Considerations for Automated Fault Isolation in Complex Systems

NASA Technical Reports Server (NTRS)

Ferrell, Bob; Oostdyk, Rebecca

2010-01-01

The purpose of this paper is to document the modeling considerations and performance metrics that were examined in the development of a large-scale Fault Detection, Isolation and Recovery (FDIR) system. The FDIR system is envisioned to perform health management functions for both a launch vehicle and the ground systems that support the vehicle during checkout and launch countdown by using suite of complimentary software tools that alert operators to anomalies and failures in real-time. The FDIR team members developed a set of operational requirements for the models that would be used for fault isolation and worked closely with the vendor of the software tools selected for fault isolation to ensure that the software was able to meet the requirements. Once the requirements were established, example models of sufficient complexity were used to test the performance of the software. The results of the performance testing demonstrated the need for enhancements to the software in order to meet the demands of the full-scale ground and vehicle FDIR system. The paper highlights the importance of the development of operational requirements and preliminary performance testing as a strategy for identifying deficiencies in highly scalable systems and rectifying those deficiencies before they imperil the success of the project

On Restructurable Control System Theory

NASA Technical Reports Server (NTRS)

Athans, M.

1983-01-01

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

A simple uniformity test for ultrasound phased arrays.

PubMed

Dudley, Nicholas J; Woolley, Darren J

2016-09-01

It is difficult to test phased array ultrasound transducers for non functioning elements. We aimed to modify a widely performed test to improve its ease and effectiveness for these arrays. A paperclip was slowly moved along the transducer array, with the scanner operating in M-mode, imaging at a fundamental frequency with automatic gain and grey scale adjustment disabled. Non-functioning elements are identified by a dark vertical line in the image. The test was repeated several times for each transducer, looking for consistency of results. 2 transducers, with faults already shown by electronic transducer testing, were used to validate the method. 23 transducers in clinical use were tested. The results of the modified test on the 2 faulty transducers agreed closely with electronic transducer testing results. The test indicated faults in 5 of the 23 transducers in clinical use: 3 with a single failed element and 2 with non-uniform sensitivity. 1 transducer with non-uniform sensitivity had undergone lens repair; the new lens was visibly non-uniform in thickness and further testing showed a reduction in depth of penetration and a loss of elevational focus in comparison with a new transducer. The modified test is capable of detecting non-functioning elements. Further work is required to provide a better understanding of more subtle faults. Copyright © 2016 Associazione Italiana di Fisica Medica. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

ASCS online fault detection and isolation based on an improved MPCA

NASA Astrophysics Data System (ADS)

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

2014-09-01

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

Joint geophysical investigation of a small scale magnetic anomaly near Gotha, Germany

NASA Astrophysics Data System (ADS)

Queitsch, Matthias; Schiffler, Markus; Goepel, Andreas; Stolz, Ronny; Guenther, Thomas; Malz, Alexander; Meyer, Matthias; Meyer, Hans-Georg; Kukowski, Nina

2014-05-01

In the framework of the multidisciplinary project INFLUINS (INtegrated FLUid Dynamics IN Sedimentary Basins) several airborne surveys using a full tensor magnetic gradiometer (FTMG) system were conducted in and around the Thuringian basin (central Germany). These sensors are based on highly sensitive superconducting quantum interference devices (SQUIDs) with a planar-type gradiometer setup. One of the main goals was to map magnetic anomalies along major fault zones in this sedimentary basin. In most survey areas low signal amplitudes were observed caused by very low magnetization of subsurface rocks. Due to the high lateral resolution of a magnetic gradiometer system and a flight line spacing of only 50m, however, we were able to detect even small magnetic lineaments. Especially close to Gotha a NW-SE striking strong magnetic anomaly with a length of 1.5 km was detected, which cannot be explained by the structure of the Eichenberg-Gotha-Saalfeld (EGS) fault zone and the rock-physical properties (low susceptibilities). Therefore, we hypothesize that the source of the anomaly must be related to an anomalous magnetization in the fault plane. To test this hypothesis, here we focus on the results of the 3D inversion of the airborne magnetic data set and compare them with existing structural geological models. In addition, we conducted several ground based measurements such as electrical resistivity tomography (ERT) and frequency domain electromagnetics (FDEM) to locate the fault. Especially, the geoelectrical measurements were able to image the fault zone. The result of the 2D electrical resistivity tomography shows a lower resistivity in the fault zone. Joint interpretation of airborne magnetics, geoelectrical and geological information let us propose that the source of the magnetization may be a fluid-flow induced impregnation with iron-oxide bearing minerals in the vicinity of the EGS fault plane.

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.

New procedure for gear fault detection and diagnosis using instantaneous angular speed

NASA Astrophysics Data System (ADS)

Li, Bing; Zhang, Xining; Wu, Jili

2017-02-01

Besides the extreme complexity of gear dynamics, the fault diagnosis results in terms of vibration signal are sometimes easily misled and even distorted by the interference of transmission channel or other components like bearings, bars. Recently, the research field of Instantaneous Angular Speed (IAS) has attracted significant attentions due to its own advantages over conventional vibration analysis. On the basis of IAS signal's advantages, this paper presents a new feature extraction method by combining the Empirical Mode Decomposition (EMD) and Autocorrelation Local Cepstrum (ALC) for fault diagnosis of sophisticated multistage gearbox. Firstly, as a pre-processing step, signal reconstruction is employed to address the oversampled issue caused by the high resolution of the angular sensor and the test speed. Then the adaptive EMD is used to acquire a number of Intrinsic Mode Functions (IMFs). Nevertheless, not all the IMFs are needed for the further analysis since different IMFs have different sensitivities to fault. Hence, the cosine similarity metric is introduced to select the most sensitive IMF. Even though, the sensitive IMF is still insufficient for the gear fault diagnosis due to the weakness of the fault component related to the gear fault. Therefore, as the final step, ALC is used for the purpose of signal de-noising and feature extraction. The effectiveness and robustness of the new approach has been validated experimentally on the basis of two gear test rigs with gears under different working conditions. Diagnosis results show that the new approach is capable of effectively handling the gear fault diagnosis i.e., the highlighted quefrency and its rahmonics corresponding to the rotary period and its multiple are displayed clearly in the cepstrum record of the proposed method.

Drive and protection circuit for converter module of cascaded H-bridge STATCOM

NASA Astrophysics Data System (ADS)

Wang, Xuan; Yuan, Hongliang; Wang, Xiaoxing; Wang, Shuai; Fu, Yongsheng

2018-04-01

Drive and protection circuit is an important part of power electronics, which is related to safe and stable operation issues in the power electronics. The drive and protection circuit is designed for the cascaded H-bridge STATCOM. This circuit can realize flexible dead-time setting, operation status self-detection, fault priority protection and detailed fault status uploading. It can help to improve the reliability of STATCOM's operation. Finally, the proposed circuit is tested and analyzed by power electronic simulation software PSPICE (Simulation Program with IC Emphasis) and a series of experiments. Further studies showed that the proposed circuit can realize drive and control of H-bridge circuit, meanwhile it also can realize fast processing faults and have advantage of high reliability.

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 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

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.

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.

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

PubMed Central

Li, Ke; Chen, Peng

2011-01-01

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

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

PubMed

Li, Ke; Chen, Peng

2011-01-01

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

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

PubMed

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

2018-06-14

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

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Soft Computing Application in Fault Detection of Induction Motor

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

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

2010-10-26

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

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

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2004-01-01

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

Using the Bongwana natural CO2 release to understand leakage processes and develop monitoring

NASA Astrophysics Data System (ADS)

Jones, David; Johnson, Gareth; Hicks, Nigel; Bond, Clare; Gilfillan, Stuart; Kremer, Yannick; Lister, Bob; Nkwane, Mzikayise; Maupa, Thulani; Munyangane, Portia; Robey, Kate; Saunders, Ian; Shipton, Zoe; Pearce, Jonathan; Haszeldine, Stuart

2016-04-01

Natural CO2 leakage along the Bongwana Fault in South Africa is being studied to help understand processes of CO2 leakage and develop monitoring protocols. The Bongwana Fault crops out over approximately 80 km in KwaZulu-Natal province, South Africa. In outcrop the fault is expressed as a broad fracture corridor in Dwyka Tillite, with fractures oriented approximately N-S. Natural emissions of CO2 occur at various points along the fault, manifest as travertine cones and terraces, bubbling in the rivers and as gas fluxes through soil. Exposed rock outcrop shows evidence for Fe-staining around fractures and is locally extensively kaolinitised. The gas has also been released through a shallow water well, and was exploited commercially in the past. Preliminary studies have been carried out to better document the surface emissions using near surface gas monitoring, understand the origin of the gas through major gas composition and stable and noble gas isotopes and improve understanding of the structural controls on gas leakage through mapping. In addition the impact of the leaking CO2 on local water sources (surface and ground) is being investigated, along with the seismic activity of the fault. The investigation will help to build technical capacity in South Africa and to develop monitoring techniques and plans for a future CO2 storage pilot there. Early results suggest that CO2 leakage is confined to a relatively small number of spatially-restricted locations along the weakly seismically active fault. Fracture permeability appears to be the main method by which the CO2 migrates to the surface. The bulk of the CO2 is of deep origin with a minor contribution from near surface biogenic processes as determined by major gas composition. Water chemistry, including pH, DO and TDS is notably different between CO2-rich and CO2-poor sites. Soil gas content and flux effectively delineates the fault trace in active leakage sites. The fault provides an effective testing ground for field-based monitoring with results to date indicating the methods and technologies tested successfully detect leaking CO2. Further work will investigate the source of the CO2 and attempt to quantify CO2 flux rates and detection thresholds.

Remote Structural Health Monitoring and Advanced Prognostics of Wind Turbines

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

Douglas Brown; Bernard Laskowski

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

Power plant fault detection using artificial neural network

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

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

PubMed

Wang, Tianyang; Chu, Fulei; Han, Qinkai

2017-03-01

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

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

DOEpatents

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

2010-12-07

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

Engine rotor health monitoring: an experimental approach to fault detection and durability assessment

NASA Astrophysics Data System (ADS)

Abdul-Aziz, Ali; Woike, Mark R.; Clem, Michelle; Baaklini, George

2015-03-01

Efforts to update and improve turbine engine components in meeting flights safety and durability requirements are commitments that engine manufacturers try to continuously fulfill. Most of their concerns and developments energies focus on the rotating components as rotor disks. These components typically undergo rigorous operating conditions and are subject to high centrifugal loadings which subject them to various failure mechanisms. Thus, developing highly advanced health monitoring technology to screen their efficacy and performance is very essential to their prolonged service life and operational success. Nondestructive evaluation techniques are among the many screening methods that presently are being used to pre-detect hidden flaws and mini cracks prior to any appalling events occurrence. Most of these methods or procedures are confined to evaluating material's discontinuities and other defects that have mature to a point where failure is eminent. Hence, development of more robust techniques to pre-predict faults prior to any catastrophic events in these components is highly vital. This paper is focused on presenting research activities covering the ongoing research efforts at NASA Glenn Research Center (GRC) rotor dynamics laboratory in support of developing a fault detection system for key critical turbine engine components. Data obtained from spin test experiments of a rotor disk that relates to investigating behavior of blade tip clearance, tip timing and shaft displacement based on measured data acquired from sensor devices such as eddy current, capacitive and microwave are presented. Additional results linking test data with finite element modeling to characterize the structural durability of a cracked rotor as it relays to the experimental tests and findings is also presented. An obvious difference in the vibration response is shown between the notched and the baseline no notch rotor disk indicating the presence of some type of irregularity.

Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

NASA Technical Reports Server (NTRS)

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

2001-01-01

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

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

NASA Astrophysics Data System (ADS)

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

1997-09-01

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

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

NASA Astrophysics Data System (ADS)

Singh, Dheeraj Sharan; Zhao, Qing

2016-12-01

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

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

PubMed

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

2012-11-01

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

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

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2008-01-01

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

Detecting of transient vibration signatures using an improved fast spatial-spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery

NASA Astrophysics Data System (ADS)

Chen, BinQiang; Zhang, ZhouSuo; Zi, YanYang; He, ZhengJia; Sun, Chuang

2013-10-01

Detecting transient vibration signatures is of vital importance for vibration-based condition monitoring and fault detection of the rotating machinery. However, raw mechanical signals collected by vibration sensors are generally mixtures of physical vibrations of the multiple mechanical components installed in the examined machinery. Fault-generated incipient vibration signatures masked by interfering contents are difficult to be identified. The fast kurtogram (FK) is a concise and smart gadget for characterizing these vibration features. The multi-rate filter-bank (MRFB) and the spectral kurtosis (SK) indicator of the FK are less powerful when strong interfering vibration contents exist, especially when the FK are applied to vibration signals of short duration. It is encountered that the impulsive interfering contents not authentically induced by mechanical faults complicate the optimal analyzing process and lead to incorrect choosing of the optimal analysis subband, therefore the original FK may leave out the essential fault signatures. To enhance the analyzing performance of FK for industrial applications, an improved version of fast kurtogram, named as "fast spatial-spectral ensemble kurtosis kurtogram", is presented. In the proposed technique, discrete quasi-analytic wavelet tight frame (QAWTF) expansion methods are incorporated as the detection filters. The QAWTF, constructed based on dual tree complex wavelet transform, possesses better vibration transient signature extracting ability and enhanced time-frequency localizability compared with conventional wavelet packet transforms (WPTs). Moreover, in the constructed QAWTF, a non-dyadic ensemble wavelet subband generating strategy is put forward to produce extra wavelet subbands that are capable of identifying fault features located in transition-band of WPT. On the other hand, an enhanced signal impulsiveness evaluating indicator, named "spatial-spectral ensemble kurtosis" (SSEK), is put forward and utilized as the quantitative measure to select optimal analyzing parameters. The SSEK indicator is robuster in evaluating the impulsiveness intensity of vibration signals due to its better suppressing ability of Gaussian noise, harmonics and sporadic impulsive shocks. Numerical validations, an experimental test and two engineering applications were used to verify the effectiveness of the proposed technique. The analyzing results of the numerical validations, experimental tests and engineering applications demonstrate that the proposed technique possesses robuster transient vibration content detecting performance in comparison with the original FK and the WPT-based FK method, especially when they are applied to the processing of vibration signals of relative limited duration.

Influence of mineralogy and microstructures on strain localization and fault zone architecture of the Alpine Fault, New Zealand

NASA Astrophysics Data System (ADS)

Ichiba, T.; Kaneki, S.; Hirono, T.; Oohashi, K.; Schuck, B.; Janssen, C.; Schleicher, A.; Toy, V.; Dresen, G.

2017-12-01

The Alpine Fault on New Zealand's South Island is an oblique, dextral strike-slip fault that accommodated the majority of displacement between the Pacific and the Australian Plates and presents the biggest seismic hazard in the region. Along its central segment, the hanging wall comprises greenschist and amphibolite facies Alpine Schists. Exhumation from 35 km depth, along a SE-dipping detachment, lead to mylonitization which was subsequently overprinted by brittle deformation and finally resulted in the fault's 1 km wide damage zone. The geomechanical behavior of a fault is affected by the internal structure of its fault zone. Consequently, studying processes controlling fault zone architecture allows assessing the seismic hazard of a fault. Here we present the results of a combined microstructural (SEM and TEM), mineralogical (XRD) and geochemical (XRF) investigation of outcrop samples originating from several locations along the Alpine Fault, the aim of which is to evaluate the influence of mineralogical composition, alteration and pre-existing fabric on strain localization and to identify the controls on the fault zone architecture, particularly the locus of brittle deformation in P, T and t space. Field observations reveal that the fault's principal slip zone (PSZ) is either a thin (< 1 cm to < 7 cm) layered structure or a relatively thick (10s cm) package lacking a detectable macroscopic fabric. Lithological and related rheological contrasts are widely assumed to govern strain localization. However, our preliminary results suggest that qualitative mineralogical composition has only minor impact on fault zone architecture. Quantities of individual mineral phases differ markedly between fault damage zone and fault core at specific sites, but the quantitative composition of identical structural units such as the fault core, is similar in all samples. This indicates that the degree of strain localization at the Alpine Fault might be controlled by small initial heterogeneities in texture and fabric or a combination of these, rather than in mineralogy. Further microstructural investigations are needed to test this hypothesis.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis

PubMed Central

Kim, Jong-Myon

2018-01-01

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects. The DNN structure is based on the stacked denoising autoencoder non-mutually exclusive classifier (NMEC) method for combined modes. The NMEC-DNN is trained using data for a single fault and it classifies both single faults and multiple combined faults. The results of experiments conducted on AE data collected through an experimental test-bed demonstrate that the DNN achieves good classification performance with a maximum accuracy of 95%. The proposed method is compared with a multi-class classifier based on support vector machines (SVMs). The NMEC-DNN yields better diagnostic performance in comparison to the multi-class classifier based on SVM. The NMEC-DNN reduces the number of necessary data collections and improves the bearing fault diagnosis performance. PMID:29642466

An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only.

PubMed

Koley, Ebha; Verma, Khushaboo; Ghosh, Subhojit

2015-01-01

Restrictions on right of way and increasing power demand has boosted development of six phase transmission. It offers a viable alternative for transmitting more power, without major modification in existing structure of three phase double circuit transmission system. Inspite of the advantages, low acceptance of six phase system is attributed to the unavailability of a proper protection scheme. The complexity arising from large number of possible faults in six phase lines makes the protection quite challenging. The proposed work presents a hybrid wavelet transform and modular artificial neural network based fault detector, classifier and locator for six phase lines using single end data only. The standard deviation of the approximate coefficients of voltage and current signals obtained using discrete wavelet transform are applied as input to the modular artificial neural network for fault classification and location. The proposed scheme has been tested for all 120 types of shunt faults with variation in location, fault resistance, fault inception angles. The variation in power system parameters viz. short circuit capacity of the source and its X/R ratio, voltage, frequency and CT saturation has also been investigated. The result confirms the effectiveness and reliability of the proposed protection scheme which makes it ideal for real time implementation.

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.

A methodology for testing fault-tolerant software

NASA Technical Reports Server (NTRS)

Andrews, D. M.; Mahmood, A.; Mccluskey, E. J.

1985-01-01

A methodology for testing fault tolerant software is presented. There are problems associated with testing fault tolerant software because many errors are masked or corrected by voters, limiter, or automatic channel synchronization. This methodology illustrates how the same strategies used for testing fault tolerant hardware can be applied to testing fault tolerant software. For example, one strategy used in testing fault tolerant hardware is to disable the redundancy during testing. A similar testing strategy is proposed for software, namely, to move the major emphasis on testing earlier in the development cycle (before the redundancy is in place) thus reducing the possibility that undetected errors will be masked when limiters and voters are added.

Simulation verification techniques study: Simulation self test hardware design and techniques report

NASA Technical Reports Server (NTRS)

1974-01-01

The final results are presented of the hardware verification task. The basic objectives of the various subtasks are reviewed along with the ground rules under which the overall task was conducted and which impacted the approach taken in deriving techniques for hardware self test. The results of the first subtask and the definition of simulation hardware are presented. The hardware definition is based primarily on a brief review of the simulator configurations anticipated for the shuttle training program. The results of the survey of current self test techniques are presented. The data sources that were considered in the search for current techniques are reviewed, and results of the survey are presented in terms of the specific types of tests that are of interest for training simulator applications. Specifically, these types of tests are readiness tests, fault isolation tests and incipient fault detection techniques. The most applicable techniques were structured into software flows that are then referenced in discussions of techniques for specific subsystems.

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

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Distributed fault detection over sensor networks with Markovian switching topologies

NASA Astrophysics Data System (ADS)

Ge, Xiaohua; Han, Qing-Long

2014-05-01

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

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

PubMed Central

2017-01-01

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

Error detection and correction unit with built-in self-test capability for spacecraft applications

NASA Technical Reports Server (NTRS)

Timoc, Constantin

1990-01-01

The objective of this project was to research and develop a 32-bit single chip Error Detection and Correction unit capable of correcting all single bit errors and detecting all double bit errors in the memory systems of a spacecraft. We designed the 32-bit EDAC (Error Detection and Correction unit) based on a modified Hamming code and according to the design specifications and performance requirements. We constructed a laboratory prototype (breadboard) which was converted into a fault simulator. The correctness of the design was verified on the breadboard using an exhaustive set of test cases. A logic diagram of the EDAC was delivered to JPL Section 514 on 4 Oct. 1988.

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.

Non-Pilot Protection of the HVDC Grid

NASA Astrophysics Data System (ADS)

Badrkhani Ajaei, Firouz

This thesis develops a non-pilot protection system for the next generation power transmission system, the High-Voltage Direct Current (HVDC) grid. The HVDC grid protection system is required to be (i) adequately fast to prevent damages and/or converter blocking and (ii) reliable to minimize the impacts of faults. This study is mainly focused on the Modular Multilevel Converter (MMC) -based HVDC grid since the MMC is considered as the building block of the future HVDC systems. The studies reported in this thesis include (i) developing an enhanced equivalent model of the MMC to enable accurate representation of its DC-side fault response, (ii) developing a realistic HVDC-AC test system that includes a five-terminal MMC-based HVDC grid embedded in a large interconnected AC network, (iii) investigating the transient response of the developed test system to AC-side and DC-side disturbances in order to determine the HVDC grid protection requirements, (iv) investigating the fault surge propagation in the HVDC grid to determine the impacts of the DC-side fault location on the measured signals at each relay location, (v) designing a protection algorithm that detects and locates DC-side faults reliably and sufficiently fast to prevent relay malfunction and unnecessary blocking of the converters, and (vi) performing hardware-in-the-loop tests on the designed relay to verify its potential to be implemented in hardware. The results of the off-line time domain transients studies in the PSCAD software platform and the real-time hardware-in-the-loop tests using an enhanced version of the RTDS platform indicate that the developed HVDC grid relay meets all technical requirements including speed, dependability, security, selectivity, and robustness. Moreover, the developed protection algorithm does not impose considerable computational burden on the hardware.

An intelligent control system for failure detection and controller reconfiguration

NASA Technical Reports Server (NTRS)

Biswas, Saroj K.

1994-01-01

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

Sensor fault detection and recovery in satellite attitude control

NASA Astrophysics Data System (ADS)

Nasrolahi, Seiied Saeed; Abdollahi, Farzaneh

2018-04-01

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

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

NASA Technical Reports Server (NTRS)

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

2007-01-01

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

Fault-tolerant Greenberger-Horne-Zeilinger paradox based on non-Abelian anyons.

PubMed

Deng, Dong-Ling; Wu, Chunfeng; Chen, Jing-Ling; Oh, C H

2010-08-06

We propose a scheme to test the Greenberger-Horne-Zeilinger paradox based on braidings of non-Abelian anyons, which are exotic quasiparticle excitations of topological states of matter. Because topological ordered states are robust against local perturbations, this scheme is in some sense "fault-tolerant" and might close the detection inefficiency loophole problem in previous experimental tests of the Greenberger-Horne-Zeilinger paradox. In turn, the construction of the Greenberger-Horne-Zeilinger paradox reveals the nonlocal property of non-Abelian anyons. Our results indicate that the non-Abelian fractional statistics is a pure quantum effect and cannot be described by local realistic theories. Finally, we present a possible experimental implementation of the scheme based on the anyonic interferometry technologies.

Investigation of advanced fault insertion and simulator methods

NASA Technical Reports Server (NTRS)

Dunn, W. R.; Cottrell, D.

1986-01-01

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

Studies on Automobile Clutch Release Bearing Characteristics with Acoustic Emission

NASA Astrophysics Data System (ADS)

Chen, Guoliang; Chen, Xiaoyang

Automobile clutch release bearings are important automotive driveline components. For the clutch release bearing, early fatigue failure diagnosis is significant, but the early fatigue failure response signal is not obvious, because failure signals are susceptible to noise on the transmission path and to working environment factors such as interference. With an improvement in vehicle design, clutch release bearing fatigue life indicators have increasingly become an important requirement. Contact fatigue is the main failure mode of release rolling bearing components. Acoustic emission techniques in contact fatigue failure detection have unique advantages, which include highly sensitive nondestructive testing methods. In the acoustic emission technique to detect a bearing, signals are collected from multiple sensors. Each signal contains partial fault information, and there is overlap between the signals' fault information. Therefore, the sensor signals receive simultaneous source information integration is complete fragment rolling bearing fault acoustic emission signal, which is the key issue of accurate fault diagnosis. Release bearing comprises the following components: the outer ring, inner ring, rolling ball, cage. When a failure occurs (such as cracking, pitting), the other components will impact damaged point to produce acoustic emission signal. Release bearings mainly emit an acoustic emission waveform with a Rayleigh wave propagation. Elastic waves emitted from the sound source, and it is through the part surface bearing scattering. Dynamic simulation of rolling bearing failure will contribute to a more in-depth understanding of the characteristics of rolling bearing failure, because monitoring and fault diagnosis of rolling bearings provide a theoretical basis and foundation.

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

PubMed Central

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

2011-01-01

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

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.

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

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

Elkhatib, Mohamed; Ellis, Abraham; Milan Biswal

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

Online Tester for a Symbol Generator

NASA Technical Reports Server (NTRS)

Juday, D.; Mcconaugy, K.

1985-01-01

About 95 percent of faults detected. Programable instrument periodically checks for failures in system that generates alphanumerical and other symbol voltages for cathode-ray-tube displays. Symbol-generator tester compares gated test-point voltages with predetermined voltage limits while circuit under test performs commanded operation. A go/no-go indication given, depending on whether test voltage is or is not within its specification. Tester in plug-in modular form, temporarily wired to generator test points, or permanently wired to these points.

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.

A Generalized Machine Fault Detection Method Using Unified Change Detection

DTIC Science & Technology

2014-10-02

SOCIETY 2014 11 of the extension shaft. It can be induced by a lack of tightening torque of the end-nut and consequently causes a load...Test Facility (HTTF). The objective of the study was to provide HUMS systems with the capability to detect the loss of tightening torque of the end...from pinion SSA (at Ring-Front sensor & cruise power) change signal with cross-over at 75th shaft order Ten end-nut tightening torques were used in

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.

Research on measurement of aviation magneto ignition strength and balance

NASA Astrophysics Data System (ADS)

Gao, Feng; He, Zhixiang; Zhang, Dingpeng

2017-12-01

Aviation magneto ignition system failure accounted for two-thirds of the total fault aviation piston engine and above. At present the method used for this failure diagnosis is often depended on the visual inspections in the civil aviation maintenance field. Due to human factors, the visual inspections cannot provide ignition intensity value and ignition equilibrium deviation value among the different spark plugs in the different cylinder of aviation piston engine. So air magneto ignition strength and balance testing has become an aviation piston engine maintenance technical problem needed to resolve. In this paper, the ultraviolet sensor with detection wavelength of 185~260nm and driving voltage of 320V DC is used as the core of ultraviolet detection to detect the ignition intensity of Aviation magneto ignition system and the balance deviation of the ignition intensity of each cylinder. The experimental results show that the rotational speed within the range 0 to 3500 RPM test error less than 0.34%, ignition strength analysis and calculation error is less than 0.13%, and measured the visual inspection is hard to distinguish between high voltage wire leakage failure of deviation value of 200 pulse ignition strength balance/Sec. The method to detect aviation piston engine maintenance of magneto ignition system fault has a certain reference value.

Geological and vegetational applications of Shuttle Imaging Radar-B, Mineral County, Nevada

NASA Technical Reports Server (NTRS)

Borengasser, M. X.; Kleiner, E. F.; Peterson, F. F.; Klieforth, H.; Vreeland, P.

1988-01-01

Multiple-incidence angle and multi-azimuth radar data were acquired from a Shuttle platform over test sites in Nevada in October 1984. An attempt was made to correlate these data with ground features for the purpose of evaluating the use of such data for geological and vegetational assessment. Standard ecological parameters with respect to the flora (community composition, dominance, and relative cover) were recorded in the field at the time of overflight. Although a total of 33 species representing 11 plant families were recognized, and plant cover ranged from 13 to 26 percent, radar data could not be used to separate plant communities. The signal return is more a function of abiotic conditions than vegetative characteristics. Illumination geometry plays an important role in the ability to detect strike-slip and dip-slip faults. Local incidence angle is the most important parameter, and SIR-B data takes with small incidence angles are superior for identifying certain styles of faulting. Look direction is critical for detecting faults with a dip-slip component. New structural features were not observed. Problems with radar antenna power and recording significantly affected data quality.

Detection of Rooftop Cooling Unit Faults Based on Electrical Measurements

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

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

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

Fault 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.

Parametric Testing of Launch Vehicle FDDR Models

NASA Technical Reports Server (NTRS)

Schumann, Johann; Bajwa, Anupa; Berg, Peter; Thirumalainambi, Rajkumar

2011-01-01

For the safe operation of a complex system like a (manned) launch vehicle, real-time information about the state of the system and potential faults is extremely important. The on-board FDDR (Failure Detection, Diagnostics, and Response) system is a software system to detect and identify failures, provide real-time diagnostics, and to initiate fault recovery and mitigation. The ERIS (Evaluation of Rocket Integrated Subsystems) failure simulation is a unified Matlab/Simulink model of the Ares I Launch Vehicle with modular, hierarchical subsystems and components. With this model, the nominal flight performance characteristics can be studied. Additionally, failures can be injected to see their effects on vehicle state and on vehicle behavior. A comprehensive test and analysis of such a complicated model is virtually impossible. In this paper, we will describe, how parametric testing (PT) can be used to support testing and analysis of the ERIS failure simulation. PT uses a combination of Monte Carlo techniques with n-factor combinatorial exploration to generate a small, yet comprehensive set of parameters for the test runs. For the analysis of the high-dimensional simulation data, we are using multivariate clustering to automatically find structure in this high-dimensional data space. Our tools can generate detailed HTML reports that facilitate the analysis.

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

NASA Astrophysics Data System (ADS)

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

2018-07-01

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

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

PubMed

Jeon, Namju; Lee, Hyeongcheol

2016-12-12

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

Integrated analysis of error detection and recovery

NASA Technical Reports Server (NTRS)

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

1985-01-01

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

Recent Improvements to the Finite-Fault Rupture Detector Algorithm: FinDer II

NASA Astrophysics Data System (ADS)

Smith, D.; Boese, M.; Heaton, T. H.

2015-12-01

Constraining the finite-fault rupture extent and azimuth is crucial for accurately estimating ground-motion in large earthquakes. Detecting and modeling finite-fault ruptures in real-time is thus essential to both earthquake early warning (EEW) and rapid emergency response. Following extensive real-time and offline testing, the finite-fault rupture detector algorithm, FinDer (Böse et al., 2012 & 2015), was successfully integrated into the California-wide ShakeAlert EEW demonstration system. Since April 2015, FinDer has been scanning real-time waveform data from approximately 420 strong-motion stations in California for peak ground acceleration (PGA) patterns indicative of earthquakes. FinDer analyzes strong-motion data by comparing spatial images of observed PGA with theoretical templates modeled from empirical ground-motion prediction equations (GMPEs). If the correlation between the observed and theoretical PGA is sufficiently high, a report is sent to ShakeAlert including the estimated centroid position, length, and strike, and their uncertainties, of an ongoing fault rupture. Rupture estimates are continuously updated as new data arrives. As part of a joint effort between USGS Menlo Park, ETH Zurich, and Caltech, we have rewritten FinDer in C++ to obtain a faster and more flexible implementation. One new feature of FinDer II is that multiple contour lines of high-frequency PGA are computed and correlated with templates, allowing the detection of both large earthquakes and much smaller (~ M3.5) events shortly after their nucleation. Unlike previous EEW algorithms, FinDer II thus provides a modeling approach for both small-magnitude point-source and larger-magnitude finite-fault ruptures with consistent error estimates for the entire event magnitude range.

Surveillance system and method having an adaptive sequential probability fault detection test

NASA Technical Reports Server (NTRS)

Herzog, James P. (Inventor); Bickford, Randall L. (Inventor)

2005-01-01

System and method providing surveillance of an asset such as a process and/or apparatus by providing training and surveillance procedures that numerically fit a probability density function to an observed residual error signal distribution that is correlative to normal asset operation and then utilizes the fitted probability density function in a dynamic statistical hypothesis test for providing improved asset surveillance.

Surveillance system and method having an adaptive sequential probability fault detection test

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor); Herzog, James P. (Inventor)

2006-01-01

System and method providing surveillance of an asset such as a process and/or apparatus by providing training and surveillance procedures that numerically fit a probability density function to an observed residual error signal distribution that is correlative to normal asset operation and then utilizes the fitted probability density function in a dynamic statistical hypothesis test for providing improved asset surveillance.

Surveillance System and Method having an Adaptive Sequential Probability Fault Detection Test

NASA Technical Reports Server (NTRS)

Bickford, Randall L. (Inventor); Herzog, James P. (Inventor)

2008-01-01

System and method providing surveillance of an asset such as a process and/or apparatus by providing training and surveillance procedures that numerically fit a probability density function to an observed residual error signal distribution that is correlative to normal asset operation and then utilizes the fitted probability density function in a dynamic statistical hypothesis test for providing improved asset surveillance.

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

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

NASA Technical Reports Server (NTRS)

Benazera, Emmanuel; Narasimhan, Sriram

2005-01-01

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

Sliding Mode Fault Tolerant Control with Adaptive Diagnosis for Aircraft Engines

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Distributed intrusion monitoring system with fiber link backup and on-line fault diagnosis functions

NASA Astrophysics Data System (ADS)

Xu, Jiwei; Wu, Huijuan; Xiao, Shunkun

2014-12-01

A novel multi-channel distributed optical fiber intrusion monitoring system with smart fiber link backup and on-line fault diagnosis functions was proposed. A 1× N optical switch was intelligently controlled by a peripheral interface controller (PIC) to expand the fiber link from one channel to several ones to lower the cost of the long or ultra-long distance intrusion monitoring system and also to strengthen the intelligent monitoring link backup function. At the same time, a sliding window auto-correlation method was presented to identify and locate the broken or fault point of the cable. The experimental results showed that the proposed multi-channel system performed well especially whenever any a broken cable was detected. It could locate the broken or fault point by itself accurately and switch to its backup sensing link immediately to ensure the security system to operate stably without a minute idling. And it was successfully applied in a field test for security monitoring of the 220-km-length national borderline in China.

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

NASA Astrophysics Data System (ADS)

Yang, H.; Moresi, L. N.

2017-12-01

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

Protection of Renewable-dominated Microgrids: Challenges and Potential Solutions

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

Elkhatib, Mohamed; Ellis, Abraham; Biswal, Milan

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

A probabilistic method to diagnose faults of air handling units

NASA Astrophysics Data System (ADS)

Dey, Debashis

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

Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1

NASA Technical Reports Server (NTRS)

Park, Thomas; Smith, Austin; Oliver, T. Emerson

2018-01-01

The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GNC software from the set of healthy measurements. This paper explores the trades and analyses that were performed in selecting a set of robust fault-detection algorithms included in the GN&C flight software. These trades included both an assessment of hardware-provided health and status data as well as an evaluation of different algorithms based on time-to-detection, type of failures detected, and probability of detecting false positives. We then provide an overview of the algorithms used for both fault-detection and measurement down selection. We next discuss the role of trajectory design, flexible-body models, and vehicle response to off-nominal conditions in setting the detection thresholds. Lastly, we present lessons learned from software integration and hardware-in-the-loop testing.

Detection of small earthquakes with dense array data: example from the San Jacinto fault zone, southern California

NASA Astrophysics Data System (ADS)

Meng, Haoran; Ben-Zion, Yehuda

2018-01-01

We present a technique to detect small earthquakes not included in standard catalogues using data from a dense seismic array. The technique is illustrated with continuous waveforms recorded in a test day by 1108 vertical geophones in a tight array on the San Jacinto fault zone. Waveforms are first stacked without time-shift in nine non-overlapping subarrays to increase the signal-to-noise ratio. The nine envelope functions of the stacked records are then multiplied with each other to suppress signals associated with sources affecting only some of the nine subarrays. Running a short-term moving average/long-term moving average (STA/LTA) detection algorithm on the product leads to 723 triggers in the test day. Using a local P-wave velocity model derived for the surface layer from Betsy gunshot data, 5 s long waveforms of all sensors around each STA/LTA trigger are beamformed for various incident directions. Of the 723 triggers, 220 are found to have localized energy sources and 103 of these are confirmed as earthquakes by verifying their observation at 4 or more stations of the regional seismic network. This demonstrates the general validity of the method and allows processing further the validated events using standard techniques. The number of validated events in the test day is >5 times larger than that in the standard catalogue. Using these events as templates can lead to additional detections of many more earthquakes.

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

NASA Technical Reports Server (NTRS)

Keesler, E. L.

1974-01-01

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

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

PubMed

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

2018-05-22

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

On damage detection in wind turbine gearboxes using outlier analysis

NASA Astrophysics Data System (ADS)

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

2012-04-01

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

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.

Meeting the Challenges of Exploration Systems: Health Management Technologies for Aerospace Systems With Emphasis on Propulsion

NASA Technical Reports Server (NTRS)

Melcher, Kevin J.; Sowers, T. Shane; Maul, William A.

2005-01-01

The constraints of future Exploration Missions will require unique Integrated System Health Management (ISHM) capabilities throughout the mission. An ambitious launch schedule, human-rating requirements, long quiescent periods, limited human access for repair or replacement, and long communication delays all require an ISHM system that can span distinct yet interdependent vehicle subsystems, anticipate failure states, provide autonomous remediation, and support the Exploration Mission from beginning to end. NASA Glenn Research Center has developed and applied health management system technologies to aerospace propulsion systems for almost two decades. Lessons learned from past activities help define the approach to proper ISHM development: sensor selection- identifies sensor sets required for accurate health assessment; data qualification and validation-ensures the integrity of measurement data from sensor to data system; fault detection and isolation-uses measurements in a component/subsystem context to detect faults and identify their point of origin; information fusion and diagnostic decision criteria-aligns data from similar and disparate sources in time and use that data to perform higher-level system diagnosis; and verification and validation-uses data, real or simulated, to provide variable exposure to the diagnostic system for faults that may only manifest themselves in actual implementation, as well as faults that are detectable via hardware testing. This presentation describes a framework for developing health management systems and highlights the health management research activities performed by the Controls and Dynamics Branch at the NASA Glenn Research Center. It illustrates how those activities contribute to the development of solutions for Integrated System Health Management.

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

USGS Publications Warehouse

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

2001-01-01

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

Reset Tree-Based Optical Fault Detection

PubMed Central

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

2013-01-01

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

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

PubMed

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

2017-05-24

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

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

PubMed

Chen, Xianglong; Feng, Fuzhou; Zhang, Bingzhi

2016-09-13

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

The Identification of Software Failure Regions

DTIC Science & Technology

1990-06-01

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

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

USGS Publications Warehouse

Shelly, David R.; Hardebeck, Jeanne L.

2010-01-01

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

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

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

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

PubMed Central

2018-01-01

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

A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition.

PubMed

Zhang, Xiaorong; Huang, He

2015-02-19

Unreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. While the proposed SFTM has shown great promise, the previous design is impractical. A practical SFTM has to be fast enough, lightweight, automatic, and robust under different conditions with or without disturbances. This paper presented a real-time, practical SFTM towards robust EMG PR. A novel fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect disturbances and recover the PR performance immediately. These components of SFTM were then integrated with the EMG PR module and tested on five able-bodied subjects and a transradial amputee in real-time for classifying multiple hand and wrist motions under different conditions with different disturbance types and levels. The proposed fast LDA retraining algorithm significantly shortened the retraining time from nearly 1 s to less than 4 ms when tested on the embedded system prototype, which demonstrated the feasibility of a nearly "zero-delay" SFTM that is imperceptible to the users. The results of the real-time tests suggested that the SFTM was able to handle different types of disturbances investigated in this study and significantly improve the classification performance when one or multiple EMG signals were disturbed. In addition, the SFTM could also maintain the system's classification performance when there was no disturbance. This paper presented a real-time, lightweight, and automatic SFTM, which paved the way for reliable and robust EMG PR for prosthesis control.

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 aspects of seismicity recorded prior to and during drilling operations.

Detection of inter-turn faults in transformer winding using the capacitor discharge method

NASA Astrophysics Data System (ADS)

Michna, Michał; Wilk, Andrzej; Ziółko, Michał; Wołoszyk, Marek; Swędrowski, Leon; Szwangruber, Piotr

2017-12-01

The paper presents results of an analysis of inter-turn fault effects on the voltage and current waveforms of a capacitor discharge through transformer windings. The research was conducted in the frame of the Facility of Antiproton and Ion Research project which goal is to build a new international accelerator facility that utilizes superconducting magnets. For the sake of electrical quality assurance of the superconducting magnet circuits, a measurement and diagnostic system is currently under development at Gdansk University of Technology (GUT). Appropriate measurements and simulations of the special transformer system were performed to verify the proposed diagnostic method. In order to take into account the nonlinearity and hysteresis of the magnetic yoke, a novel mathematical model of the transformer was developed. A special test bench was constructed to emulate the inter-turn faults within transformer windings.

Estimating residual fault hitting rates by recapture sampling

NASA Technical Reports Server (NTRS)

Lee, Larry; Gupta, Rajan

1988-01-01

For the recapture debugging design introduced by Nayak (1988) the problem of estimating the hitting rates of the faults remaining in the system is considered. In the context of a conditional likelihood, moment estimators are derived and are shown to be asymptotically normal and fully efficient. Fixed sample properties of the moment estimators are compared, through simulation, with those of the conditional maximum likelihood estimators. Properties of the conditional model are investigated such as the asymptotic distribution of linear functions of the fault hitting frequencies and a representation of the full data vector in terms of a sequence of independent random vectors. It is assumed that the residual hitting rates follow a log linear rate model and that the testing process is truncated when the gaps between the detection of new errors exceed a fixed amount of time.

Research on recognition of the geologic framework of porphyry copper deposits on ERTS-1 imagery. [New Guinea, Alaska, and Colorado

NASA Technical Reports Server (NTRS)

Wilson, J. C. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Many new linear and circular features were found. These features prompted novel tectonic classification and analysis especially in the Ray and Ely areas. Tectonic analyses of the Ok Tedi, Tanacross, and Silvertone areas follow conventional interpretations. Circular features are mapped in many cases and are interpreted as exposed or covered intrusive centers. The small circular features reported in the Ok Tedi test area are valid and useful correlations with tertiary intrusion and volcanism in this remote part of New Guinea. Several major faults of regional dimensions, such as the Denali fault in Alaska and the Colorado mineral belt structures in Colorado are detected in the imagery. Many more faults and regional structures are found in the imagery than exist on present maps.

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

NASA Technical Reports Server (NTRS)

Tejada, Arturo

2009-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Multiple sensor fault diagnosis for dynamic processes.

PubMed

Li, Cheng-Chih; Jeng, Jyh-Cheng

2010-10-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

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

Automated touch sensing in the mouse tapered beam test using Raspberry Pi.

PubMed

Ardesch, Dirk Jan; Balbi, Matilde; Murphy, Timothy H

2017-11-01

Rodent models of neurological disease such as stroke are often characterized by motor deficits. One of the tests that are used to assess these motor deficits is the tapered beam test, which provides a sensitive measure of bilateral motor function based on foot faults (slips) made by a rodent traversing a gradually narrowing beam. However, manual frame-by-frame scoring of video recordings is necessary to obtain test results, which is time-consuming and prone to human rater bias. We present a cost-effective method for automated touch sensing in the tapered beam test. Capacitive touch sensors detect foot faults onto the beam through a layer of conductive paint, and results are processed and stored on a Raspberry Pi computer. Automated touch sensing using this method achieved high sensitivity (96.2%) as compared to 'gold standard' manual video scoring. Furthermore, it provided a reliable measure of lateralized motor deficits in mice with unilateral photothrombotic stroke: results indicated an increased number of contralesional foot faults for up to 6days after ischemia. The automated adaptation of the tapered beam test produces results immediately after each trial, without the need for labor-intensive post-hoc video scoring. It also increases objectivity of the data as it requires less experimenter involvement during analysis. Automated touch sensing may provide a useful adaptation to the existing tapered beam test in mice, while the simplicity of the hardware lends itself to potential further adaptations to related behavioral tests. Copyright © 2017 Elsevier B.V. All rights reserved.

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

NASA Technical Reports Server (NTRS)

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

2003-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

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

PubMed Central

Jeon, Namju; Lee, Hyeongcheol

2016-01-01

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

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

PubMed Central

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

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

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

PubMed

Lu, Feng; Huang, Jinquan; Xing, Yaodong

2012-01-01

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

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

DOEpatents

Andrews, L.B.

1998-08-18

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