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Sample records for fault detection method

  1. Fault detection with principal component pursuit method

    NASA Astrophysics Data System (ADS)

    Pan, Yijun; Yang, Chunjie; Sun, Youxian; An, Ruqiao; Wang, Lin

    2015-11-01

    Data-driven approaches are widely applied for fault detection in industrial process. Recently, a new method for fault detection called principal component pursuit(PCP) is introduced. PCP is not only robust to outliers, but also can accomplish the objectives of model building, fault detection, fault isolation and process reconstruction simultaneously. PCP divides the data matrix into two parts: a fault-free low rank matrix and a sparse matrix with sensor noise and process fault. The statistics presented in this paper fully utilize the information in data matrix. Since the low rank matrix in PCP is similar to principal components matrix in PCA, a T2 statistic is proposed for fault detection in low rank matrix. And this statistic can illustrate that PCP is more sensitive to small variations in variables than PCA. In addition, in sparse matrix, a new monitored statistic performing the online fault detection with PCP-based method is introduced. This statistic uses the mean and the correlation coefficient of variables. Monte Carlo simulation and Tennessee Eastman (TE) benchmark process are provided to illustrate the effectiveness of monitored statistics.

  2. Active fault detection: A comparison of probabilistic methods

    NASA Astrophysics Data System (ADS)

    Škach, Jan; Pun?ochá?, Ivo

    2015-11-01

    The paper deals with probabilistic methods for designing the active fault detectors that improve the quality of detection using an auxiliary input signal. Two probabilistic methods that assume a similar stochastic model of a monitored system are considered and compared with a special attention to various difficulties associated with active fault detector designs. The active fault detector design based on a general detection cost function is compared with the model sequence selection error minimization design in terms of assumptions and theoretical properties. Practical aspects of both methods are also considered and demonstrated through a numerical example.

  3. Method of Fault Detection and Rerouting

    NASA Technical Reports Server (NTRS)

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

  4. Improved Hidden-Markov-Model Method Of Detecting Faults

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic J.

    1994-01-01

    Method of automated, continuous monitoring to detect faults in complicated dynamic system based on hidden-Markov-model (HMM) approach. Simpler than another, recently proposed HMM method, but retains advantages of that method, including low susceptibility to false alarms, no need for mathematical model of dynamics of system under normal or faulty conditions, and ability to detect subtle changes in characteristics of monitored signals. Examples of systems monitored by use of this method include motors, turbines, and pumps critical in their applications; chemical-processing plants; powerplants; and biomedical systems.

  5. Fault detection in electromagnetic suspension systems with state estimation methods

    SciTech Connect

    Sinha, P.K.; Zhou, F.B.; Kutiyal, R.S. . Dept. of Engineering)

    1993-11-01

    High-speed maglev vehicles need a high level of safety that depends on the whole vehicle system's reliability. There are many ways of attaining high reliability for the system. Conventional method uses redundant hardware with majority vote logic circuits. Hardware redundancy costs more, weigh more and occupy more space than that of analytically redundant methods. Analytically redundant systems use parameter identification and state estimation methods based on the system models to detect and isolate the fault of instruments (sensors), actuator and components. In this paper the authors use the Luenberger observer to estimate three state variables of the electromagnetic suspension system: position (airgap), vehicle velocity, and vertical acceleration. These estimates are compared with the corresponding sensor outputs for fault detection. In this paper, they consider FDI of the accelerometer, the sensor which provides the ride quality.

  6. High resolution seismics methods in application to fault zone detection

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  7. Failure Detecting Method of Fault Current Limiter System with Rectifier

    NASA Astrophysics Data System (ADS)

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

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

  8. Fault detection, isolation and reconfiguration in FTMP Methods and experimental results. [fault tolerant multiprocessor

    NASA Technical Reports Server (NTRS)

    Lala, J. H.

    1983-01-01

    The Fault-Tolerant Multiprocessor (FTMP) is a highly reliable computer designed to meet a goal of 10 to the -10th failures per hour and built with the objective of flying an active-control transport aircraft. Fault detection, identification, and recovery software is described, and experimental results obtained by injecting faults in the pin level in the FTMP are presented. Over 21,000 faults were injected in the CPU, memory, bus interface circuits, and error detection, masking, and error reporting circuits of one LRU of the multiprocessor. Detection, isolation, and reconfiguration times were recorded for each fault, and the results were found to agree well with earlier assumptions made in reliability modeling.

  9. An underwater ship fault detection method based on Sonar image processing

    NASA Astrophysics Data System (ADS)

    Hong, Shi; Fang-jian, Shan; Bo, Cong; Wei, Qiu

    2016-02-01

    For the research of underwater ship fault detection method in conditions of sailing on the ocean especially in poor visibility muddy sea, a fault detection method under the assist of sonar image processing was proposed. Firstly, did sonar image denoising using the algorithm of pulse coupled neural network (PCNN); secondly, edge feature extraction for the image after denoising was carried out by morphological wavelet transform; Finally, interested regions Using relevant tracking method were taken, namely fault area mapping. The simulation results presented here proved the feasibility and effectiveness of the sonar image processing in underwater fault detection system.

  10. Fault detection and isolation

    NASA Technical Reports Server (NTRS)

    Bernath, Greg

    1993-01-01

    Erroneous measurements in multisensor navigation systems must be detected and isolated. A recursive estimator can find fast growing errors; a least squares batch estimator can find slow growing errors. This process is called fault detection. A protection radius can be calculated as a function of time for a given location. This protection radius can be used to guarantee the integrity of the navigation data. Fault isolation can be accomplished using either a snapshot method or by examining the history of the fault detection statistics.

  11. 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.

  12. Solar system fault detection

    DOEpatents

    Farrington, Robert B. (Wheatridge, CO); Pruett, Jr., James C. (Lakewood, CO)

    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.

  13. Time-series methods for fault detection and identification in vibrating structures.

    PubMed

    Fassois, Spilios D; Sakellariou, John S

    2007-02-15

    An overview of the principles and techniques of time-series methods for fault detection, identification and estimation in vibrating structures is presented, and certain new methods are introduced. The methods are classified, and their features and operation are discussed. Their practicality and effectiveness are demonstrated through brief presentations of three case studies pertaining to fault detection, identification and estimation in an aircraft panel, a scale aircraft skeleton structure and a simple nonlinear simulated structure. PMID:17255046

  14. Fault Detection Using Resistivity Image Profiling Method at Hsinchu Science Industrial Park, Hsinchu, Taiwan

    NASA Astrophysics Data System (ADS)

    Liu, H.

    2004-12-01

    As Taiwan is located in the Neotectonic belt along the western Pacific Ocean, the detection of active faults is important for earthquake risk analysis. In addition, some active faults in Taiwan lie typically at the bases of urban or industrial area, their identification is often challenging because of environment changes and interferences, etc. The Hsinchu Science Industrial Park (HSIP), Hsinchu, N.W. Taiwan is one of the most important high technology sites in Taiwan. Based on previous geophysical and geologic surveys, the well-known active Hsinchen fault is crossed the site, although the exact location of active fault zone remains unclear. Different geophysical methods are used to detect an active fault. However, a successful recognition of a fault depends on the physical properties such as density contrast, acoustic impedance, and reisitivity contrast, etc. of the target and its surroundings. Due to a large resistivity contrast between the hanging wall (low resistivity Choulan shale) and the footwall (high resistivity Toukoshan gravel beds) of the Hsinchen fault, geoelectrical sounding may be one of the best ways to trace this fault. For the above reasons, the resistivity image profiling method was used to investigate the fault and also attempted to relate these DC resistivity measurements to the fault parameters. Correlated the electrical and fault parameters is one of the most important tasks for this study. All of the field measurements to be discussed were made during the period of 2000 to 2003. A portable resistivity meter used to make all resistivity measurements. Electrode spread was pole-pole arrays. Combined the RIP sounding results to the local topographic maps, geologic data and formal open pits information, fault zones are being recognized. Keywords: active fault; resistivity change; Hsinchen fault; Taiwan

  15. 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.

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

    DOEpatents

    Zhou, Wei (Los Angeles, CA); Lu, Bin (Kenosha, WI); Nowak, Michael P. (Menomonee Falls, WI); Dimino, Steven A. (Wauwatosa, WI)

    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.

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

    DOEpatents

    Zhou, Wei (Los Angeles, CA); Lu, Bin (Kenosha, WI); Habetler, Thomas G. (Snellville, GA); Harley, Ronald G. (Lawrenceville, GA); Theisen, Peter J. (West Bend, WI)

    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.

  18. Fault detection in an air-handling unit using residual and recursive parameter identification methods

    SciTech Connect

    Lee, W.Y.; Park, C.; Kelly, G.E.

    1996-11-01

    A scheme for detecting faults in an air-handling unit using residual and parameter identification methods is presented. Faults can be detected by comparing the normal or expected operating condition data with the abnormal, measured data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system parameter identification technique. In this study, autoregressive moving average with exogenous input (ARMAX) and autoregressive with exogenous input (ARX) models with both single-input/single-output (SISO) and multi-input/single-output (MISO) structures are examined. Model parameters are determined using the Kalman filter recursive identification method. This approach is tested using experimental data from a laboratory`s variable-air-volume (VAV) air-handling unit operated with and without faults.

  19. Method of detecting a fault of an exhaust gas recirculation system

    SciTech Connect

    Hashimoto, T.; Takahashi, A.; Imaizuma, T.; Saito, S.; Tanaka, H.; Jimbo, T

    1989-05-30

    This patent describes a method of detecting a fault of an exhaust gas recirculation system of an internal combustion engine, wherein a temperature relating to a temperature of the exhaust gas recirculating through the exhaust gas recirculation system is detected when the exhaust gas recirculation system is in a condition in which the system should be operated to return part of the exhaust gas of the engine to an intake passage, and it is detected that the exhaust gas recirculation system in defective, when the detected temperature is lower than a fault discriminating value. The method consists of: detecting a condition of air to be sucked into the engine, and setting the fault discriminating value in accordance with the detected condition of air.

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

    DOEpatents

    Almasi, Gheorghe (Ardsley, NY) [Ardsley, NY; Blumrich, Matthias Augustin (Ridgefield, CT) [Ridgefield, CT; Chen, Dong (Croton-On-Hudson, NY) [Croton-On-Hudson, NY; Coteus, Paul (Yorktown, NY) [Yorktown, NY; Gara, Alan (Mount Kisco, NY) [Mount Kisco, NY; Giampapa, Mark E. (Irvington, NY) [Irvington, NY; Heidelberger, Philip (Cortlandt Manor, NY) [Cortlandt Manor, NY; Hoenicke, Dirk I. (Ossining, NY) [Ossining, NY; Singh, Sarabjeet (Mississauga, CA) [Mississauga, CA; Steinmacher-Burow, Burkhard D. (Wernau, DE) [Wernau, DE; Takken, Todd (Brewster, NY) [Brewster, NY; Vranas, Pavlos (Bedford Hills, NY) [Bedford Hills, NY

    2008-06-03

    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 in 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.

  1. 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.

  2. Method and system for early detection of incipient faults in electric motors

    DOEpatents

    Parlos, Alexander G; Kim, Kyusung

    2003-07-08

    A method and system for early detection of incipient faults in an electric motor are disclosed. First, current and voltage values for one or more phases of the electric motor are measured during motor operations. A set of current predictions is then determined via a neural network-based current predictor based on the measured voltage values and an estimate of motor speed values of the electric motor. Next, a set of residuals is generated by combining the set of current predictions with the measured current values. A set of fault indicators is subsequently computed from the set of residuals and the measured current values. Finally, a determination is made as to whether or not there is an incipient electrical, mechanical, and/or electromechanical fault occurring based on the comparison result of the set of fault indicators and a set of predetermined baseline values.

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  4. A Method Based on Multi-Sensor Data Fusion for Fault Detection of Planetary Gearboxes

    PubMed Central

    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. PMID:22438750

  5. 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. PMID:22438750

  6. 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.

  7. Randomness fault detection system

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

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

  8. 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.

  9. Fault detection for T-S fuzzy time-delay systems: delta operator and input-output methods.

    PubMed

    Li, Hongyi; Gao, Yabin; Wu, Ligang; Lam, H K

    2015-02-01

    This paper focuses on the problem of fault detection for Takagi-Sugeno fuzzy systems with time-varying delays via delta operator approach. By designing a filter to generate a residual signal, the fault detection problem addressed in this paper can be converted into a filtering problem. The time-varying delay is approximated by the two-term approximation method. Fuzzy augmented fault detection system is constructed in ? -domain, and a threshold function is given. By applying the scaled small gain theorem and choosing a Lyapunov-Krasovskii functional in ? -domain, a sufficient condition of asymptotic stability with a prescribed H? disturbance attenuation level is derived for the proposed fault detection system. Then, a solvability condition for the designed fault detection filter is established, with which the desired filter can be obtained by solving a convex optimization problem. Finally, an example is given to demonstrate the feasibility and effectiveness of the proposed method. PMID:24919207

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

  12. 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.

  13. A New Method for Node Fault Detection in Wireless Sensor Networks

    PubMed Central

    Jiang, Peng

    2009-01-01

    Wireless sensor networks (WSNs) are an important tool for monitoring distributed remote environments. As one of the key technologies involved in WSNs, node fault detection is indispensable in most WSN applications. It is well known that the distributed fault detection (DFD) scheme checks out the failed nodes by exchanging data and mutually testing among neighbor nodes in this network., but the fault detection accuracy of a DFD scheme would decrease rapidly when the number of neighbor nodes to be diagnosed is small and the node's failure ratio is high. In this paper, an improved DFD scheme is proposed by defining new detection criteria. Simulation results demonstrate that the improved DFD scheme performs well in the above situation and can increase the fault detection accuracy greatly. PMID:22399967

  14. Discrete Data Qualification System and Method Comprising Noise Series Fault Detection

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

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

  15. Dynamic Fault Detection Chassis

    SciTech Connect

    Mize, Jeffery J

    2007-01-01

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

  16. Row fault detection system

    DOEpatents

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

    2012-02-07

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

  17. Row fault detection system

    SciTech Connect

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

    2008-10-14

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

  18. 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.

  19. Method of locating ground faults

    NASA Technical Reports Server (NTRS)

    Patterson, Richard L. (Inventor); Rose, Allen H. (Inventor); Cull, Ronald C. (Inventor)

    1994-01-01

    The present invention discloses a method of detecting and locating current imbalances such as ground faults in multiwire systems using the Faraday effect. As an example, for 2-wire or 3-wire (1 ground wire) electrical systems, light is transmitted along an optical path which is exposed to magnetic fields produced by currents flowing in the hot and neutral wires. The rotations produced by these two magnetic fields cancel each other, therefore light on the optical path does not read the effect of either. However, when a ground fault occurs, the optical path is exposed to a net Faraday effect rotation due to the current imbalance thereby exposing the ground fault.

  20. 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.

  1. Arc fault detection system

    DOEpatents

    Jha, Kamal N.

    1999-01-01

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

  2. Arc fault detection system

    DOEpatents

    Jha, K.N.

    1999-05-18

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

  3. An investigation into a method of detecting the fault induced high frequency voltage signals of EHV transmission lines for protection applications

    SciTech Connect

    Agrawal, P. )

    1991-01-01

    This paper investigates a method of detecting the fault induced high frequency voltage signals of the EHV transmission lines for the protection applications. The method is based on the principle of power line carrier communication system using a stack tuner for detecting the high frequency voltage signals in a particular frequency bandwidth. The digitally simulated fault responses of different frequency bandwidths in kilo Hertz range and at different fault inception angles of the voltage waveform have been studied.

  4. 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.

  5. 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.

  6. 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.

  7. Fault detection using genetic programming

    NASA Astrophysics Data System (ADS)

    Zhang, Liang; B. Jack, Lindsay; Nandi, Asoke K.

    2005-03-01

    Genetic programming (GP) is a stochastic process for automatically generating computer programs. GP has been applied to a variety of problems which are too wide to reasonably enumerate. As far as the authors are aware, it has rarely been used in condition monitoring (CM). In this paper, GP is used to detect faults in rotating machinery. Featuresets from two different machines are used to examine the performance of two-class normal/fault recognition. The results are compared with a few other methods for fault detection: Artificial neural networks (ANNs) have been used in this field for many years, while support vector machines (SVMs) also offer successful solutions. For ANNs and SVMs, genetic algorithms have been used to do feature selection, which is an inherent function of GP. In all cases, the GP demonstrates performance which equals or betters that of the previous best performing approaches on these data sets. The training times are also found to be considerably shorter than the other approaches, whilst the generated classification rules are easy to understand and independently validate.

  8. Performance factors as a basis of practical fault detection and diagnostic methods for air-handling units

    SciTech Connect

    Kaerki, S.H.; Karjalainen, S.J.

    1999-07-01

    The technical term performance is defined as how well a system fulfills its intended purpose in different operational circumstances. This paper describes the process of establishing the performance factors of air-handling units (AHUs), defining the performance requirements, and connecting them to fault detection and diagnosis methods. The most important performance requirements of AHUs are related to heating and cooling energy, the supply airflow rate and purity, energy efficiency, and control quality. Many solutions made during different life-cycle phases affect the final system performance. These solutions are discussed in this paper. Diagnostic tools and methods can be developed for monitoring the defined performance criteria. Practical FDD methods have been developed for the system considered here. The methods are simple and easy to apply in practice. Methods for monitoring the heat recovery unit and the AHU energy use are presented. Examples of utilizing characteristic curves and fault-symptom trees are also described.

  9. 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.

  10. Final Technical Report: PV Fault Detection Tool.

    SciTech Connect

    King, Bruce Hardison; Jones, Christian Birk

    2015-12-01

    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.

  11. Row fault detection system

    DOEpatents

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

    2010-02-23

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

  12. Cell boundary fault detection system

    DOEpatents

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

    2009-05-05

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

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

    NASA Astrophysics Data System (ADS)

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

    1999-11-01

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

  14. Fault Detection in Differential Algebraic Equations

    NASA Astrophysics Data System (ADS)

    Scott, Jason Roderick

    Fault detection and identification (FDI) is important in almost all real systems. Fault detection is the supervision of technical processes aimed at detecting undesired or unpermitted states (faults) and taking appropriate actions to avoid dangerous situations, or to ensure efficiency in a system. This dissertation develops and extends fault detection techniques for systems modeled by differential algebraic equations (DAEs). First, a passive, observer-based approach is developed and linear filters are constructed to identify faults by filtering residual information. The method presented here uses the least squares completion to compute an ordinary differential equation (ODE) that contains the solution of the DAE and applies the observer directly to this ODE. While observers have been applied to ODE models for the purpose of fault detection in the past, the use of observers on completions of DAEs is a new idea. Moreover, the resulting residuals are modified requiring additional analysis. Robustness with respect to disturbances is also addressed by a novel frequency filtering technique. Active detection, as opposed to passive detection where outputs are passively monitored, allows the injection of an auxiliary control signal to test the system. These algorithms compute an auxiliary input signal guaranteeing fault detection, assuming bounded noise. In the second part of this dissertation, a novel active detection approach for DAE models is developed by taking linear transformations of the DAEs and solving a bi-layer optimization problem. An efficient real-time detection algorithm is also provided, as is the extension to model uncertainty. The existence of a class of problems where the algorithm breaks down is revealed and an alternative algorithm that finds a nearly minimal auxiliary signal is presented. Finally, asynchronous signal design, that is, applying the test signal on a different interval than the observation window, is explored and discussed.

  15. Including Faults Detected By Near-Surface Seismic Methods in the USGS National Seismic Hazard Maps - Some Restrictions Apply

    NASA Astrophysics Data System (ADS)

    Williams, R. A.; Haller, K. M.

    2014-12-01

    Every 6 years, the USGS updates the National Seismic Hazard Maps (new version released July 2014) that are intended to help society reduce risk from earthquakes. These maps affect hundreds of billions of dollars in construction costs each year as they are used to develop seismic-design criteria of buildings, bridges, highways, railroads, and provide data for risk assessment that help determine insurance rates. Seismic source characterization, an essential component of hazard model development, ranges from detailed trench excavations across faults at the ground surface to less detailed analysis of broad regions defined mainly on the basis of historical seismicity. Though it is a priority for the USGS to discover new Quaternary fault sources, the discovered faults only become a part of the hazard model if there are corresponding constraints on their geometry (length and depth extent) and slip-rate (or recurrence interval). When combined with fault geometry and slip-rate constraints, near-surface seismic studies that detect young (Quaternary) faults have become important parts of the hazard source model. Examples of seismic imaging studies with significant hazard impact include the Southern Whidbey Island fault, Washington; Santa Monica fault, San Andreas fault, and Palos Verdes fault zone, California; and Commerce fault, Missouri. There are many more faults in the hazard model in the western U.S. than in the expansive region east of the Rocky Mountains due to the higher rate of tectonic deformation, frequent surface-rupturing earthquakes and, in some cases, lower erosion rates. However, the recent increase in earthquakes in the central U.S. has revealed previously unknown faults for which we need additional constraints before we can include them in the seismic hazard maps. Some of these new faults may be opportunities for seismic imaging studies to provide basic data on location, dip, style of faulting, and recurrence.

  16. An innovative application of electrical resistivity methods for detecting faults in the mining industry

    SciTech Connect

    Fromm, A.J.; Taylor, R.W. ); Shapiro, A.R.; Curley, P.G.; Barden, D.E. )

    1992-01-01

    A drilling program, intended to define Silurian dolomite reserves and hydrogeologic conditions at a potential quarry near the Sandwich fault system of northeastern Illinois, revealed the presence of a 30 meter brecciated zone. For concerns over the community, quarry operations, and economic projections a geophysical investigation was conducted to access the possible existence of a fault within the site. The interpretation of the geophysical survey would then be used to guide an expanded drilling program, which would further define subsurface conditions. A discontinuity in the apparent resistivities of subsurface materials was delineated by nineteen electrical depth soundings. The discontinuity suggested the possibility of a fault along the southern end of the property. Four azimuthal resistivity surveys indicated isotropic conditions, which supported the absence of fault enhanced permeability throughout most of the property. Significant anisotropy calculated from a fifth azimuthal survey, near the southern end of the property, indicated a fault trending 300 degrees with a secondary porosity of approximately 3 percent.

  17. 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.

  18. In-situ fault detection apparatus and method for an encased energy storing device

    DOEpatents

    Hagen, Ronald A.; Comte, Christophe; Knudson, Orlin B.; Rosenthal, Brian; Rouillard, Jean

    2000-01-01

    An apparatus and method for detecting a breach in an electrically insulating surface of an electrically conductive power system enclosure within which a number of series connected energy storing devices are disposed. The energy storing devices disposed in the enclosure are connected to a series power connection. A detector is coupled to the series connection and detects a change of state in a test signal derived from the series connected energy storing devices. The detector detects a breach in the insulating layer of the enclosure by detecting a state change in the test signal from a nominal state to a non-nominal state. A voltage detector detects a state change of the test signals from a nominal state, represented by a voltage of a selected end energy storing device, to a non-nominal state, represented by a voltage that substantially exceeds the voltage of the selected opposing end energy storing device. Alternatively, the detector may comprise a signal generator that produces the test signal as a time-varying or modulated test signal and injects the test signal into the series connection. The detector detects the state change of the time-varying or modulated test signal from a nominal state, represented by a signal substantially equivalent to the test signal, to a non-nominal state, representative by an absence of the test signal.

  19. Bisectional fault detection system

    DOEpatents

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

    2012-02-14

    An apparatus, program product and method logically divide a group of nodes and causes node pairs comprising a node from each section to communicate. Results from the communications may be analyzed to determine performance characteristics, such as bandwidth and proper connectivity.

  20. Bisectional fault detection system

    SciTech Connect

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

    2008-11-11

    An apparatus, program product and method logically divides a group of nodes and causes node pairs comprising a node from each section to communicate. Results from the communications may be analyzed to determine performance characteristics, such as bandwidth and proper connectivity.

  1. 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.

  2. All row, planar fault detection system

    DOEpatents

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

    2013-07-23

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

  3. Implementation of a model based fault detection and diagnosis technique for actuation faults of the SSME

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

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

  4. Faults Detection And Level Control In Coupled Tanks

    NASA Astrophysics Data System (ADS)

    Maican, Camelia

    2015-09-01

    This paper presents a mechanism for the detection and localization of faults in a plant, which is equipped with two coupled tanks, using residual methods. The water circuit consists of the upper and lower tanks connected through a system of pipes and valves. The controlled variable is the level in the upper tank. The level control system and the faults detection structure were developed under Matlab Simulink. The faults detection structure allows us to detect two faults that can occur in the plant, separately. The proposed method was theoretically developed and experimentally verified on the plant model.

  5. 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.

  6. Fault detection and analysis in nuclear research facility using artificial intelligence methods

    NASA Astrophysics Data System (ADS)

    Ghazali, Abu Bakar; Ibrahim, Maslina Mohd

    2016-01-01

    In this article, an online detection of transducer and actuator condition is discussed. A case study is on the reading of area radiation monitor (ARM) installed at the chimney of PUSPATI TRIGA nuclear reactor building, located at Bangi, Malaysia. There are at least five categories of abnormal ARM reading that could happen during the transducer failure, namely either the reading becomes very high, or very low/ zero, or with high fluctuation and noise. Moreover, the reading may be significantly higher or significantly lower as compared to the normal reading. An artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are good methods for modeling this plant dynamics. The failure of equipment is based on ARM reading so it is then to compare with the estimated ARM data from ANN/ ANFIS function. The failure categories in either `yes' or `no' state are obtained from a comparison between the actual online data and the estimated output from ANN/ ANFIS function. It is found that this system design can correctly report the condition of ARM equipment in a simulated environment and later be implemented for online monitoring. This approach can also be extended to other transducers, such as the temperature profile of reactor core and also to include other critical actuator conditions such as the valves and pumps in the reactor facility provided that the failure symptoms are clearly defined.

  7. 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.

  8. 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

  9. Method and apparatus for generating motor current spectra to enhance motor system fault detection

    DOEpatents

    Linehan, Daniel J. (Knoxville, TN); Bunch, Stanley L. (Oak Ridge, TN); Lyster, Carl T. (Knoxville, TN)

    1995-01-01

    A method and circuitry for sampling periodic amplitude modulations in a nonstationary periodic carrier wave to determine frequencies in the amplitude modulations. The method and circuit are described in terms of an improved motor current signature analysis. The method insures that the sampled data set contains an exact whole number of carrier wave cycles by defining the rate at which samples of motor current data are collected. The circuitry insures that a sampled data set containing stationary carrier waves is recreated from the analog motor current signal containing nonstationary carrier waves by conditioning the actual sampling rate to adjust with the frequency variations in the carrier wave. After the sampled data is transformed to the frequency domain via the Discrete Fourier Transform, the frequency distribution in the discrete spectra of those components due to the carrier wave and its harmonics will be minimized so that signals of interest are more easily analyzed.

  10. Method and apparatus for generating motor current spectra to enhance motor system fault detection

    DOEpatents

    Linehan, D.J.; Bunch, S.L.; Lyster, C.T.

    1995-10-24

    A method and circuitry are disclosed for sampling periodic amplitude modulations in a nonstationary periodic carrier wave to determine frequencies in the amplitude modulations. The method and circuit are described in terms of an improved motor current signature analysis. The method insures that the sampled data set contains an exact whole number of carrier wave cycles by defining the rate at which samples of motor current data are collected. The circuitry insures that a sampled data set containing stationary carrier waves is recreated from the analog motor current signal containing nonstationary carrier waves by conditioning the actual sampling rate to adjust with the frequency variations in the carrier wave. After the sampled data is transformed to the frequency domain via the Discrete Fourier Transform, the frequency distribution in the discrete spectra of those components due to the carrier wave and its harmonics will be minimized so that signals of interest are more easily analyzed. 29 figs.

  11. 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.

  12. 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.

  13. 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.

  14. Fault detection in finite frequency domain for Takagi-Sugeno fuzzy systems with sensor faults.

    PubMed

    Li, Xiao-Jian; Yang, Guang-Hong

    2014-08-01

    This paper is concerned with the fault detection (FD) problem in finite frequency domain for continuous-time Takagi-Sugeno fuzzy systems with sensor faults. Some finite-frequency performance indices are initially introduced to measure the fault/reference input sensitivity and disturbance robustness. Based on these performance indices, an effective FD scheme is then presented such that the generated residual is designed to be sensitive to both fault and reference input for faulty cases, while robust against the reference input for fault-free case. As the additional reference input sensitivity for faulty cases is considered, it is shown that the proposed method improves the existing FD techniques and achieves a better FD performance. The theory is supported by simulation results related to the detection of sensor faults in a tunnel-diode circuit. PMID:24184791

  15. 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.

  16. Typical faults of air conditioning systems and fault detection by ARX model and extended Kalman filter

    SciTech Connect

    Yoshida, Harunori; Iwami, Tatsuhiro; Yuzawa, Hideki; Suzuki, Masami

    1996-11-01

    Since faulty operation of heating, ventilating, and air-conditioning (HVAC) systems is detrimental to energy conservation, and maintenance experts are no longer able to detect faults due to the sophistication of current air-handling units (AHUs), automated fault detection and diagnosis (FDD) is increasingly important. In the present study, the results of a survey about typical faults that are commonly encountered in air-handling systems are summarized, and two methods of finding abrupt faults are described. To investigate the development of automated fault detection schemes, two methods to detect an abrupt fault are tested, and the effectiveness of the methods is analyzed. Both are based on a mathematical model of system dynamics. The first one is an autoregressive exogenous (ARX) model and the second is based on an extended Kalman filter. It is shown that faults that are difficult to detect by a simple limit checker method can be detected in both cases on the basis of computer simulation by HVACSIM+.

  17. Output-Only Techniques for Fault Detection

    NASA Astrophysics Data System (ADS)

    Brzezinski, Adam John

    Fault detection is relevant to many applications, including structural health monitoring and machine health monitoring. Furthermore, output measurement data may be the only information known about a system. Hence we develop and demonstrate techniques for output-only fault detection. We also investigate implementation issues, including computational complexity and output noise. First, we consider real-time detection of an abrupt change in a noisy signal. Existing techniques exhibit sensitivity to gradual (incipient) changes in the data, as well as detection delays, false alarms, and missed detections. Hence, we propose an adjacent moving window peak detection (AMWPD) approach that uses an approximate low-pass filter and statistical process control techniques to determine whether an abrupt change has occurred. We compare the AMWPD approach with existing techniques for change detection and show that the AMWPD approach exhibits comparable detection speed and number of missed detections while providing fewer false alarms. Second, we consider feature extraction and clustering for classification. For industrial applications, existing methods provide insufficient classification accuracy and require significant training time. Hence, we propose new features that improve classification accuracy and apply a modified tabu search and probabilistic neural network (mTS + PNN) approach to select and cluster the features and thereby classify the data. We compare the mTS + PNN approach with an existing feature selection and clustering technique that employs principal component analysis and a multi-layer perceptron neural network. Using an application example, we demonstrate that the mTS + PNN approach provides higher classification accuracy while requiring less training and classification time. Finally, we consider identification of output-to-output relationships in linear system dynamics. Existing approaches, including operational modal analysis, assume that the excitation signal is a realization of a white random process, which may not be true. Hence, we define and characterize pseudo transfer functions (PTFs), which relate output measurements, and we use changes in the identified PTF to detect faults. We demonstrate the effects of non-zero initial conditions, non-white excitation, unknown model order, and output noise on the accuracy of the identification and fault detection results.

  18. 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.

  19. 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.

  20. Field testing of component-level model-based fault detection methods for mixing boxes and VAV fan systems

    SciTech Connect

    Xu, Peng; Haves, Philip

    2002-05-16

    An automated fault detection and diagnosis tool for HVAC systems is being developed, based on an integrated, life-cycle, approach to commissioning and performance monitoring. The tool uses component-level HVAC equipment models implemented in the SPARK equation-based simulation environment. The models are configured using design information and component manufacturers' data and then fine-tuned to match the actual performance of the equipment by using data measured during functional tests of the sort using in commissioning. This paper presents the results of field tests of mixing box and VAV fan system models in an experimental facility and a commercial office building. The models were found to be capable of representing the performance of correctly operating mixing box and VAV fan systems and detecting several types of incorrect operation.

  1. Detecting Latent Faults In Digital Flight Controls

    NASA Technical Reports Server (NTRS)

    Mcgough, John; Mulcare, Dennis; Larsen, William E.

    1992-01-01

    Report discusses theory, conduct, and results of tests involving deliberate injection of low-level faults into digital flight-control system. Part of study of effectiveness of techniques for detection of and recovery from faults, based on statistical assessment of inputs and outputs of parts of control systems. Offers exceptional new capability to establish reliabilities of critical digital electronic systems in aircraft.

  2. 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.

  3. A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals

    NASA Astrophysics Data System (ADS)

    Bozchalooi, I. Soltani; Liang, Ming

    2008-05-01

    The vibration signal measured from a bearing contains vital information for the prognostic and health assessment purposes. However, when bearings are installed as part of a complex mechanical system, the measured signal is often heavily clouded by various noises due to the compounded effect of interferences of other machine elements and background noises present in the measuring device. As such, reliable condition monitoring would not be possible without proper de-noising. This is particularly true for incipient bearing faults with very weak signature signals. A new de-noising scheme is proposed in this paper to enhance the vibration signals acquired from faulty bearings. This de-noising scheme features a spectral subtraction to trim down the in-band noise prior to wavelet filtering. The Gabor wavelet is used in the wavelet transform and its parameters, i.e., scale and shape factor are selected in separate steps. The proper scale is found based on a novel resonance estimation algorithm. This algorithm makes use of the information derived from the variable shaft rotational speed though such variation is highly undesirable in fault detection since it complicates the process substantially. The shape factor value is then selected by minimizing a smoothness index. This index is defined as the ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli. De-noising results are presented for simulated signals and experimental data acquired from both normal and faulty bearings with defective outer race, inner race, and rolling element.

  4. 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.

  5. 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. PMID:12858981

  6. Cell boundary fault detection system

    DOEpatents

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

    2011-04-19

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

  7. Data Fault Detection in Medical Sensor Networks

    PubMed Central

    Yang, Yang; Liu, Qian; Gao, Zhipeng; Qiu, Xuesong; Meng, Luoming

    2015-01-01

    Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians’ diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren’t changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M). Its mechanism includes: (1) use of a dynamic-local outlier factor (D-LOF) algorithm to identify outlying sensed data vectors; (2) use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3) the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M. PMID:25774708

  8. Data fault detection in medical sensor networks.

    PubMed

    Yang, Yang; Liu, Qian; Gao, Zhipeng; Qiu, Xuesong; Meng, Luoming

    2015-01-01

    Medical body sensors can be implanted or attached to the human body to monitor the physiological parameters of patients all the time. Inaccurate data due to sensor faults or incorrect placement on the body will seriously influence clinicians' diagnosis, therefore detecting sensor data faults has been widely researched in recent years. Most of the typical approaches to sensor fault detection in the medical area ignore the fact that the physiological indexes of patients aren't changing synchronously at the same time, and fault values mixed with abnormal physiological data due to illness make it difficult to determine true faults. Based on these facts, we propose a Data Fault Detection mechanism in Medical sensor networks (DFD-M). Its mechanism includes: (1) use of a dynamic-local outlier factor (D-LOF) algorithm to identify outlying sensed data vectors; (2) use of a linear regression model based on trapezoidal fuzzy numbers to predict which readings in the outlying data vector are suspected to be faulty; (3) the proposal of a novel judgment criterion of fault state according to the prediction values. The simulation results demonstrate the efficiency and superiority of DFD-M. PMID:25774708

  9. 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.

  10. 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

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

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

  12. 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.

  13. Detecting Faults By Use Of Hidden Markov Models

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic J.

    1995-01-01

    Frequency of false alarms reduced. Faults in complicated dynamic system (e.g., antenna-aiming system, telecommunication network, or human heart) detected automatically by method of automated, continuous monitoring. Obtains time-series data by sampling multiple sensor outputs at discrete intervals of t and processes data via algorithm determining whether system in normal or faulty state. Algorithm implements, among other things, hidden first-order temporal Markov model of states of system. Mathematical model of dynamics of system not needed. Present method is "prior" method mentioned in "Improved Hidden-Markov-Model Method of Detecting Faults" (NPO-18982).

  14. Fault detection in programmable logic arrays

    NASA Astrophysics Data System (ADS)

    Somenzi, F.; Gai, S.

    1986-05-01

    When designing fault-tolerant systems including programmable logic arrays (PLAs), the various aspects of these circuits concerning fault diagnosis have to be taken into account. The peculiarity of these aspects, ranging from fault models to test generation algorithms and to self-checking structures, is due to the regularity of PLAs. The fault model generally accepted for PLAs is the crosspoint defect; it is employed by dedicated test generation algorithms, based on the fact that PLAs implement a two-level combinational function. The problem of accessing inputs and outputs of the PLA can be alleviated by augmenting the PLA itself so as to simplify the test vectors to be applied, making them function independent in the limit. A further step consists in the addition of the circuitry required to generate test vectors and to evaluate the answer, thus obtaining a built-in self-test (BIST) architecture. Finally, high reliability can be achieved with PLAs featuring concurrent error detection.

  15. Additive fault detection in nonlinear dynamic systems with saturation.

    PubMed

    Blázquez, L Felipe; de Miguel, Luis J

    2005-10-01

    This paper describes the effects of input saturation on the performance of a model-based fault detection method based on the input-output parity equation approach. For this purpose, the level control of a chemical reactor has been chosen as the control process to be analyzed, where the saturation of the dynamic process is due to the inflow control valve, and only additive faults have been considered. This study has been carried out in two ways: first by simulation techniques and second on a real industrial system. In the simulated case, the decrease in the fault detectability due to the saturation effects is shown, and some ways of achieving higher fault detectability are explored. The results obtained in the industrial case complement those obtained in the simulated case, and also reveal the existence of a relation between the control strategy used in the process and additive fault detectability, in the sense that increases in fault detectability are obtained due to the use of faster control strategies. PMID:16294778

  16. A fault detection tool for school buildings

    SciTech Connect

    Visier, J.C.; Vaezi-Nejad, H.; Corrales, P.

    1999-07-01

    Energy management and control systems (EMCSs) are widely used for automating HVAC system operation, for its remote control, and for detecting operating faults; however, manufacturers offer very few tools to assist the operator in diagnosing the defects that cause faulty process operation. This paper presents the results of research aimed at providing the service departments of French towns with tools that enable them to easily detect the most common faults occurring in the hydronic space heating systems used in school buildings. The tool developed is then presented. Finally, the results of the application of the tool in two different towns are discussed.

  17. 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.

  18. Fault detection and diagnosis of photovoltaic systems

    NASA Astrophysics Data System (ADS)

    Wu, Xing

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

  19. PV Systems Reliability Final Technical Report: Ground Fault Detection

    SciTech Connect

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

    2016-01-01

    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.

  20. Fault detection in reciprocating compressor valves under varying load conditions

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  1. Catastrophic fault diagnosis in dynamic systems using bond graph methods

    SciTech Connect

    Yarom, Tamar.

    1990-01-01

    Detection and diagnosis of faults has become a critical issue in high performance engineering systems as well as in mass-produced equipment. It is particularly helpful when the diagnosis can be made at the initial design level with respect to a prospective fault list. A number of powerful methods have been developed for aiding in the general fault analysis of designs. Catastrophic faults represent the limit case of complete local failure of connections or components. They result in the interruption of energy transfer between corresponding points in the system. In this work the conventional approach to fault detection and diagnosis is extended by means of bond-graph methods to a wide variety of engineering systems. Attention is focused on catastrophic fault diagnosis. A catastrophic fault dictionary is generated from the system model based on topological properties of the bond graph. The dictionary is processed by existing methods to extract a catastrophic fault report to aid the engineer in performing a design analysis.

  2. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    PubMed Central

    Lee, Wonhee; Park, Chan Gook

    2014-01-01

    A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU). PMID:24556675

  3. 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.

  4. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection

    NASA Astrophysics Data System (ADS)

    McDonald, Geoff L.; Zhao, Qing; Zuo, Ming J.

    2012-11-01

    In this paper a new deconvolution method is presented for the detection of gear and bearing faults from vibration data. The proposed maximum correlated Kurtosis deconvolution method takes advantage of the periodic nature of the faults as well as the impulse-like vibration behaviour associated with most types of faults. The results are compared to the standard minimum entropy deconvolution method on both simulated and experimental data. The experimental data is from a gearbox with gear chip fault, and the results are compared between healthy and faulty vibrations. The results indicate that the proposed maximum correlated Kurtosis deconvolution method performs considerably better than the traditional minimum entropy deconvolution method, and often performs several times better at fault detection. In addition to this improved performance, deconvolution of separate fault periods is possible; allowing for concurrent fault detection. Finally, an online implementation is proposed and shown to perform well and be computationally achievable on a personal computer.

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

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

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

  6. Multi-directional fault detection system

    DOEpatents

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

    2010-11-23

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

  7. Multi-directional fault detection system

    DOEpatents

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

    2010-06-29

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

  8. Multi-directional fault detection system

    DOEpatents

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

    2009-03-17

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

  9. Fault detection and diagnosis of HVAC systems

    SciTech Connect

    Han, C.Y.; Xiao, Y.; Ruther, C.J.

    1999-07-01

    This paper presents a model-based fault detection and diagnosis (FDD) system for building heating, ventilating, and air conditioning (HVAC). Model-based fault detection is based on the strategy of determining the difference or the residuals between the normal and the existing patterns. Their approach was to attack the problem on many levels of abstraction: from the signal level, controller programming level, and system component, all the way up to the information and knowledge processing level. The various issues of real implementation of the system and the processing of real-time on-line data in actual systems of campus buildings using the proven technology and off-the-shelf commercial tools are discussed. The research was based on input and output points and software control programs found in typical direct digital control systems used for variable-air-volume air handlers and VAV cooling and hot water reheat terminal units.

  10. Performance Analysis of Fault Detection and Identification for Multiple Faults in GNSS and GNSS/INS Integration

    NASA Astrophysics Data System (ADS)

    Alqurashi, Muwaffaq; Wang, Jinling

    2015-03-01

    For positioning, navigation and timing (PNT) purposes, GNSS or GNSS/INS integration is utilised to provide real-time solutions. However, any potential sensor failures or faulty measurements due to malfunctions of sensor components or harsh operating environments may cause unsatisfactory estimation for PNT parameters. The inability for immediate detecting faulty measurements or sensor component failures will reduce the overall performance of the system. So, real time detection and identification of faulty measurements is required to make the system more accurate and reliable for different applications that need real time solutions such as real time mapping for safety or emergency purposes. Consequently, it is necessary to implement an online fault detection and isolation (FDI) algorithm which is a statistic-based approach to detect and identify multiple faults.However, further investigations on the performance of the FDI for multiple fault scenarios is still required. In this paper, the performance of the FDI method under multiple fault scenarios is evaluated, e.g., for two, three and four faults in the GNSS and GNSS/INS measurements under different conditions of visible satellites and satellites geometry. Besides, the reliability (e.g., MDB) and separability (correlation coefficients between faults detection statistics) measures are also investigated to measure the capability of the FDI method. A performance analysis of the FDI method is conducted under the geometric constraints, to show the importance of the FDI method in terms of fault detectability and separability for robust positioning and navigation for real time applications.

  11. 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.

  12. Statistical Fault Detection & Diagnosis Expert System

    Energy Science and Technology Software Center (ESTSC)

    1996-12-18

    STATMON is an expert system that performs real-time fault detection and diagnosis of redundant sensors in any industrial process requiring high reliability. After a training period performed during normal operation, the expert system monitors the statistical properties of the incoming signals using a pattern recognition test. If the test determines that statistical properties of the signals have changed, the expert system performs a sequence of logical steps to determine which sensor or machine component hasmore »degraded.« less

  13. Statistical Fault Detection & Diagnosis Expert System

    SciTech Connect

    1996-12-18

    STATMON is an expert system that performs real-time fault detection and diagnosis of redundant sensors in any industrial process requiring high reliability. After a training period performed during normal operation, the expert system monitors the statistical properties of the incoming signals using a pattern recognition test. If the test determines that statistical properties of the signals have changed, the expert system performs a sequence of logical steps to determine which sensor or machine component has degraded.

  14. Envelope order tracking for fault detection in rolling element bearings

    NASA Astrophysics Data System (ADS)

    Guo, Yu; Liu, Ting-Wei; Na, Jing; Fung, Rong-Fong

    2012-12-01

    An envelope order tracking analysis scheme is proposed in the paper for the fault detection of rolling element bearing (REB) under varying-speed running condition. The developed method takes the advantages of order tracking, envelope analysis and spectral kurtosis. The fast kurtogram algorithm is utilized to obtain both optimal center frequency and bandwidth of the band-pass filter based on the maximum spectral kurtosis. The envelope containing vibration features of the incipient REB fault can be extracted adaptively. The envelope is re-sampled by the even-angle sampling scheme, and thus the non-stationary signal in the time domain is represented as a quasi-stationary signal in the angular domain. As a result, the frequency-smear problem can be eliminated in order spectrum and the fault diagnosis of REB in the varying-speed running condition of the rotating machinery is achieved. Experiments are conducted to verify the validity of the proposed method.

  15. Sliding mode fault detection and fault-tolerant control of smart dampers in semi-active control of building structures

    NASA Astrophysics Data System (ADS)

    Yeganeh Fallah, Arash; Taghikhany, Touraj

    2015-12-01

    Recent decades have witnessed much interest in the application of active and semi-active control strategies for seismic protection of civil infrastructures. However, the reliability of these systems is still in doubt as there remains the possibility of malfunctioning of their critical components (i.e. actuators and sensors) during an earthquake. This paper focuses on the application of the sliding mode method due to the inherent robustness of its fault detection observer and fault-tolerant control. The robust sliding mode observer estimates the state of the system and reconstructs the actuators’ faults which are used for calculating a fault distribution matrix. Then the fault-tolerant sliding mode controller reconfigures itself by the fault distribution matrix and accommodates the fault effect on the system. Numerical simulation of a three-story structure with magneto-rheological dampers demonstrates the effectiveness of the proposed fault-tolerant control system. It was shown that the fault-tolerant control system maintains the performance of the structure at an acceptable level in the post-fault case.

  16. Statistical Fault Detection & Diagnosis Expert System

    Energy Science and Technology Software Center (ESTSC)

    1996-12-18

    STATMON is an expert system that performs real-time fault detection and diagnosis of redundant sensors in any industrial process requiring high reliability. After a training period performed during normal operation, the expert system monitors the statistical properties of the incoming signals using a pattern recognition test. If the test determines that statistical properties of the signals have changed, the expert system performs a sequence of logical steps to determine which sensor or machine component hasmore » degraded.« less

  17. Fault detection and diagnosis using neural network approaches

    NASA Technical Reports Server (NTRS)

    Kramer, Mark A.

    1992-01-01

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

  18. Modeling, estimation, fault detection and fault diagnosis of spacecraft air contaminants

    NASA Astrophysics Data System (ADS)

    Narayan, Anand P.

    1998-07-01

    The objective of this dissertation is to develop a framework for the modeling, estimation, fault detection and diagnosis of air contaminants aboard spacecraft. Safe air is a vital resource aboard spacecraft for crewed missions, and especially so in long range missions, where the luxury of returning to earth for a clean-up does not exist. This research uses modern control theory in conjunction with advanced fluid mechanics to achieve the objective of developing an implementable comprehensive monitoring systems, suitable for use on space missions. First, a three-dimensional transport model is developed in order to model the dispersion of air contaminants. The flow field, which is an important input to the transport model, is obtained by solving the Navier Stokes equations for the cabin geometry and the appropriate boundary conditions, using a finite element method. Steady flow fields are computed for various conditions for both laminar and turbulent cases. Contamination dispersion studies are undertaken both for routine substances introduced through the inlet ducts and for emissions of toxics inside the cabin volume. The dispersion studies indicate that lumped models and even a two-dimensional model are sometimes inadequate to assure that the Spacecraft Maximum Allowable Concentrations (SMACs) are not exceeded locally. Since the research was targeted at real-time application aboard Spacecraft, a state estimation routine is implemented using Implicit Kalman Filtering. The routine makes use of the model predictions and measurements from the sensor system in order to arrive at an optimal estimate of the state of the system for each time step. Fault detection is accomplished through the use of analytical redundancy, where error residuals from the Kalman filter are monitored in order to detect any faults in the system, and to distinguish between sensor and process faults. Finally, a fault diagnosis system is developed, which is a combination of sensitivity analysis and an Extended Kalman Filter, which is used to estimate the location and capacity of an unknown source emission in the system. The sensitivity analysis involves pre-calculating sensitivity coefficients, which measure the response of each sensor to a source emission at each location in the cabin, and in the event of a fault, current measurements are used and inverted to arrive at an initial guess for the unknown source that is causing the fault. An Extended Implicit Kalman filter, developed especially for this application then makes use of the initial guess to arrive at an optimal estimate for the unknown source, by minimizing the squared estimation error. The fault diagnosis procedure is successfully tested for various test cases.

  19. An iterative inversion method for transmission line fault location

    NASA Astrophysics Data System (ADS)

    Wu, Shang Chieh

    2011-12-01

    This dissertation discusses various transmission line forward modeling techniques in both time and frequency domains. Although time domain methods offer simplicity in most cases, the computational inefficiency and lack of fidelity make these methods less attractive. Therefore, the more efficient frequency domain technique is emphasized - a modified transmission matrix (also known as ABCD) method. One of the most difficult problems in electrical wire fault location nowadays is detecting and locating frayed wiring, where the wire is only partially damaged. This type of fault can be very small and extremely difficult to detect. Most inversion schemes used to locate faults require forward models that accurately represent detailed reflections. Resolving these very small faults requires an especially accurate forward model where not only the fault but also all the other very small changes caused by normal aspects of the wiring system are included. A very high resolution Finite Difference Time Domain (FDTD) method can be used to simulate this type of fault and details of the surrounding wiring system with enough fidelity to distinguish the small fault. However, this is very costly in terms of computational resources. This dissertation demonstrates a quick way of building the fray profile that significantly reduces the simulation time. Finally, the ultimate goal of the highly realistic forward modeling is the inversion, in which a set of measured data is given and the inversion algorithm interprets the location and the nature of fault on the wire. Multiple iterations are typically required, and thus, high efficiency is necessary. A new method introduced in this dissertation is capable of identifying multiple unknown parameters in just a few steps.

  20. Fault detection, identification and reconstruction for gyroscope in satellite based on independent component analysis

    NASA Astrophysics Data System (ADS)

    Li, Zhizhou; Liu, Guohua; Zhang, Rui; Zhu, Zhencai

    2011-04-01

    Although satellites are designed with high reliability, faults do occur when satellites are in orbit. To avoid the important services being affected, redundancy is used in satellites. There are many sensors in satellites. In order to reduce the cost, space, weight and power consumption, redundant sensors should be added to satellite as few as possible. Analytical redundancy is an efficient way to optimize the application of redundant. The gyroscope is the attitude determination sensor of the satellite. The minimum redundant structure of the gyroscope system is as follows: three gyroscopes installed in three-axis orthogonally and one gyroscope installed with slantwise for redundancy(3o+1S). To achieve fault detection, identification and reconstruction, hypothesis of statistical independence between the three-axis angular rates and hypothesis of statistical independence between the angular rates and fault are proposed. The scenario that only one sensor is faulting and there are only additive fault and full fault is supposed. Under these assumptions, firstly a threshold method is used for fault detection. After a fault is detected, independent component analysis (ICA) based algorithm for fault identification is employed. To overcome the ambiguities of ICA, correlation coefficients and prior information of the mixed matrix are used. Finally, the reconstruction matrix is obtained. By using this matrix fault signal is extracted so that the yaw, roll and pitch axes (three-axis) angular rates of the satellite can be recovered. Numerical simulations show this method can fulfill fault detection, identification and reconstruction of the gyroscope system.

  1. VCSEL fault location apparatus and method

    DOEpatents

    Keeler, Gordon A.; Serkland, Darwin K.

    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.

  2. 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.

  3. Induction machine faults detection using stator current parametric spectral estimation

    NASA Astrophysics Data System (ADS)

    El Bouchikhi, El Houssin; Choqueuse, Vincent; Benbouzid, Mohamed

    2015-02-01

    Current spectrum analysis is a proven technique for fault diagnosis in electrical machines. Current spectral estimation is usually performed using classical techniques such as periodogram (FFT) or its extensions. However, these techniques have several drawbacks since their frequency resolution is limited and additional post-processing algorithms are required to extract a relevant fault detection criterion. Therefore, this paper proposes a new parametric spectral estimator that fully exploits the faults sensitive frequencies. The proposed technique is based on the maximum likelihood estimator (MLE) and offers high-resolution capabilities. Based on this approach, a fault criterion is derived for detecting several fault types. The proposed technique is assessed using simulation signals, issued from a coupled electromagnetic circuits approach-based simulation tool for mechanical unbalance and electrical asymmetry faults detection. It is afterward validated using experiments on a 0.75-kW induction machine test bed for the particular case of bearing faults.

  4. Experimental Study of Fault Arc Protection Based on UV Pulse Method in High Voltage Switchgear

    NASA Astrophysics Data System (ADS)

    Wang, Jingang; Xu, Cheng; Sun, Jiaxiang

    2014-06-01

    Based on the relationship between electrical equipment discharge and ultraviolet radiation, this paper proposed the use of UV pulse method to detect switchgear arcing faults. Switchgear protection system based on this new technique detects arcing faults by analyzing the ultraviolet produced by electric arc. This technique was implemented and tested in laboratory, and the performance of the protection module was verified: it is capable of calculating the number of UV pulses quickly and precisely, which indicates the intensity of fault arc, and therefore it can be applied to arcing faults protection system for detecting faults.

  5. 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.

  6. Fault detection for discrete-time switched systems with sensor stuck faults and servo inputs.

    PubMed

    Zhong, Guang-Xin; Yang, Guang-Hong

    2015-09-01

    This paper addresses the fault detection problem of switched systems with servo inputs and sensor stuck faults. The attention is focused on designing a switching law and its associated fault detection filters (FDFs). The proposed switching law uses only the current states of FDFs, which guarantees the residuals are sensitive to the servo inputs with known frequency ranges in faulty cases and robust against them in fault-free case. Thus, the arbitrarily small sensor stuck faults, including outage faults can be detected in finite-frequency domain. The levels of sensitivity and robustness are measured in terms of the finite-frequency H- index and l2-gain. Finally, the switching law and FDFs are obtained by the solution of a convex optimization problem. PMID:26055929

  7. An adaptive envelope spectrum technique for bearing fault detection

    NASA Astrophysics Data System (ADS)

    Sui, Wentao; Osman, Shazali; Wang, Wilson

    2014-09-01

    In this work, an adaptive envelope spectrum (AES) technique is proposed for bearing fault detection, especially for analyzing signals with transient events. The proposed AES technique first modulates the signal using the empirical mode decomposition to formulate the representative intrinsic mode functions (IMF), and then a novel IMF reconstruction method is proposed based on a correlation analysis of the envelope spectra. The reconstructed signal is post-processed by using an adaptive filter to enhance impulsive signatures, where the filter length is optimized by the proposed sparsity analysis technique. Bearing health conditions are diagnosed by examining bearing characteristic frequency information on the envelope power spectrum. The effectiveness of the proposed fault detection technique is verified by a series of experimental tests corresponding to different bearing conditions.

  8. Incipient fault detection and identification in process systems using accelerating neural network learning

    SciTech Connect

    Parlos, A.G.; Muthusami, J.; Atiya, A.F. . Dept. of Nuclear Engineering)

    1994-02-01

    The objective of this paper is to present the development and numerical testing of a robust fault detection and identification (FDI) system using artificial neural networks (ANNs), for incipient (slowly developing) faults occurring in process systems. The challenge in using ANNs in FDI systems arises because of one's desire to detect faults of varying severity, faults from noisy sensors, and multiple simultaneous faults. To address these issues, it becomes essential to have a learning algorithm that ensures quick convergence to a high level of accuracy. A recently developed accelerated learning algorithm, namely a form of an adaptive back propagation (ABP) algorithm, is used for this purpose. The ABP algorithm is used for the development of an FDI system for a process composed of a direct current motor, a centrifugal pump, and the associated piping system. Simulation studies indicate that the FDI system has significantly high sensitivity to incipient fault severity, while exhibiting insensitivity to sensor noise. For multiple simultaneous faults, the FDI system detects the fault with the predominant signature. The major limitation of the developed FDI system is encountered when it is subjected to simultaneous faults with similar signatures. During such faults, the inherent limitation of pattern-recognition-based FDI methods becomes apparent. Thus, alternate, more sophisticated FDI methods become necessary to address such problems. Even though the effectiveness of pattern-recognition-based FDI methods using ANNs has been demonstrated, further testing using real-world data is necessary.

  9. Methods for quantitatively determining fault slip using fault separation

    NASA Astrophysics Data System (ADS)

    Xu, S.-S.; Velasquillo-Martínez, L. G.; Grajales-Nishimura, J. M.; Murillo-Muñetón, G.; Nieto-Samaniego, A. F.

    2007-10-01

    Fault slip and fault separation are generally not equal to each other, however, they are geometrically related. The fault slip ( S) is a vector with a magnitude, a direction, and a sense of the movement. In this paper, a series of approaches are introduced to estimate quantitatively the magnitude and direction of the fault slip using fault separations. For calculation, the known factors are the pitch of slip lineations ( ?), the pitch of a cutoff ( ?), the dip separation ( Smd) or the strike separation ( Smh) for one marker. The two main purposes of this work include: (1) to analyze the relationship between fault slip and fault separation when slickenside lineations of a fault are known; (2) to estimate the slip direction when the parameters Smd or Smh, and ? for two non-parallel markers at a place (e.g., a point) are known. We tested the approaches using an example from a mainly strike-slip fault in East Quantoxhead, United Kingdom, and another example from the Jordan Field, Ector County, Texas. Also, we estimated the relative errors of apparent heave of the normal faults from the Sierra de San Miguelito, central Mexico.

  10. Early fault detection in automotive ball bearings using the minimum variance cepstrum

    NASA Astrophysics Data System (ADS)

    Park, Choon-Su; Choi, Young-Chul; Kim, Yang-Hann

    2013-07-01

    Ball bearings in automotive wheels play an important role in a vehicle. They enable an automobile to run and simultaneously support the vehicle. Once faults are generated, even if they are small, they often grow fast even under normal driving condition and cause vibration and noise. Therefore, it is critical to detect faults as early as possible to prevent bearings from generating harsh noise and vibration. How early faults can be detected is associated with how well a detecting method finds the information of early faults from measured signal. Incipient faults are so small that the fault signal is inherently buried by noise. Minimum variance cepstrum (MVC) has been introduced for the observation of periodic impulse signal under noisy environments. We are particularly focusing on the definition of MVC that goes back to the original definition by Bogert et al. in comparison with the recently prevalent definition of cepstral analysis. In this work, the MVC is, therefore, obtained by liftering a logarithmic power spectrum, and the lifter bank is designed by the minimum variance algorithm. Furthermore, it is also shown how efficient the method is for detecting periodic fault signal made by early faults by using automotive ball bearings, with which an automobile is equipped under running conditions. We were able to detect incipient faults in 4 out of 12 normal bearings which passed acceptance test as well as in bearings that were recalled due to noise and vibration. In addition, we compared the results of the proposed method with results obtained using other older well-established early fault detection methods that were chosen from 4 groups of methods which were classified by the domain of observation. The results demonstrated that MVC determined bearing fault periods more clearly than other methods under the given condition.

  11. Fault detection and isolation of sensors in aeration control systems.

    PubMed

    Carlsson, Bengt; Zambrano, Jesús

    2016-01-01

    In this paper, we consider the problem of fault detection (FD) and isolation in the aeration system of an activated sludge process. For this study, the dissolved oxygen in each aerated zone is assumed to be controlled automatically. As the basis for an FD method we use the ratio of air flow rates into different zones. The method is evaluated in two scenarios: using the Benchmark Simulation Model no. 1 (BSM1) by Monte Carlo simulations and using data from a wastewater treatment plant. The FD method shows good results for a correct and early FD and isolation. PMID:26877049

  12. Distributed fault detection and isolation resilient to network model uncertainties.

    PubMed

    Teixeira, Andre; Shames, Iman; Sandberg, Henrik; Johansson, Karl H

    2014-11-01

    The ability to maintain state awareness in the face of unexpected and unmodeled errors and threats is a defining feature of a resilient control system. Therefore, in this paper, we study the problem of distributed fault detection and isolation (FDI) in large networked systems with uncertain system models. The linear networked system is composed of interconnected subsystems and may be represented as a graph. The subsystems are represented by nodes, while the edges correspond to the interconnections between subsystems. Considering faults that may occur on the interconnections and subsystems, as our first contribution, we propose a distributed scheme to jointly detect and isolate faults occurring in nodes and edges of the system. As our second contribution, we analyze the behavior of the proposed scheme under model uncertainties caused by the addition or removal of edges. Additionally, we propose a novel distributed FDI scheme based on local models and measurements that is resilient to changes outside of the local subsystem and achieves FDI. Our third contribution addresses the complexity reduction of the distributed FDI method, by characterizing the minimum amount of model information and measurements needed to achieve FDI and by reducing the number of monitoring nodes. The proposed methods can be fused to design a scalable and resilient distributed FDI architecture that achieves local FDI despite unknown changes outside the local subsystem. The proposed approach is illustrated by numerical experiments on the IEEE 118-bus power network benchmark. PMID:25222962

  13. Data-driven and adaptive statistical residual evaluation for fault detection with an automotive application

    NASA Astrophysics Data System (ADS)

    Svärd, Carl; Nyberg, Mattias; Frisk, Erik; Krysander, Mattias

    2014-03-01

    An important step in model-based fault detection is residual evaluation, where residuals are evaluated with the aim to detect changes in their behavior caused by faults. To handle residuals subject to time-varying uncertainties and disturbances, which indeed are present in practice, a novel statistical residual evaluation approach is presented. The main contribution is to base the residual evaluation on an explicit comparison of the probability distribution of the residual, estimated online using current data, with a no-fault residual distribution. The no-fault distribution is based on a set of a priori known no-fault residual distributions, and is continuously adapted to the current situation. As a second contribution, a method is proposed for estimating the required set of no-fault residual distributions off-line from no-fault training data. The proposed residual evaluation approach is evaluated with measurement data on a residual for fault detection in the gas-flow system of a Scania truck diesel engine. Results show that small faults can be reliably detected with the proposed approach in cases where regular methods fail.

  14. 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.

  15. Incipient fault detection and isolation of sensors and field devices

    NASA Astrophysics Data System (ADS)

    Ferreira, Paulo Brasko

    The purpose of this research is to develop a robust fault detection and isolation method, for detecting faults in process sensors, actuators, controllers and other field devices. The approach to the solution to this problem is summarized below. A novel approach for the validation of control system components and sensors was developed in this research. The process is composed of detecting a system anomaly, isolating the faulty component (such as sensors, actuators, and controllers), computing its deviation from expected value for a given system's normal condition, and finally reconstructing its output when applicable. A variant of the Group Method of Data Handling (GMDH) was developed in this research for generating analytical redundancy from relationships among different system components. A rational function approximation was used for the data-driven modeling scheme. This analytical redundancy is necessary for detecting system anomalies and isolating faulty components. A rule-base expert system was developed in order to isolate the faulty component. The rule-based was established from model-simulated data. A fuzzy-logic estimator was implemented to compute the magnitude of the loop component fault so that the operator or the controller might take corrective actions. This latter engine allows the system to be operated in a normal condition until the next scheduled shutdown, even if a critical component were detected as degrading. The effectiveness of the method developed in this research was demonstrated through simulation and by implementation to an experimental control loop. The test loop consisted of a level control system, flow, pressure, level and temperature measuring sensors, motor-operated valves, and a pump. Commonly observed device faults were imposed in different system components such as pressure transmitters, pumps, and motor-operated valves. This research has resulted in a framework for system component failure detection and isolation, allowing easy implementation of this method in any process control system (power plants, chemical industry, and other manufacturing industry). The technique would also aid the plant personnel in defining the minimal number of sensors to be installed in a process system, necessary for reliable component validation.

  16. Detection of feed-through faults in CMOS storage elements

    NASA Technical Reports Server (NTRS)

    Al-Assadi, Waleed K.; Malaiya, Yashwant K.; Jayasumana, Anura P.

    1992-01-01

    In testing sequential circuits, internal faults in the storage elements (SE's) are sometimes modeled as stuck-at faults in the combinational circuits surrounding the SE. The detection of some transistor-level faults that cannot be modeled as stuck-at are considered. These feed-through faults cause the cell to become either data-feed-through, which makes the cell combinational, or clock-feed-through, which causes the clock signal or its complement to appear at the output. Under such faults, the cell does not function as a memory element. Here it is shown that such faults may or may not be detected depending on delays involved. Conditions under which race-ahead occurs are identified.

  17. Bearing Fault Detection in Induction Motor-Gearbox Drivetrain

    NASA Astrophysics Data System (ADS)

    Cibulka, Jaroslav; Ebbesen, Morten K.; Robbersmyr, Kjell G.

    2012-05-01

    The main contribution in the hereby presented paper is to investigate the fault detection capability of a motor current signature analysis by expanding its scope to include the gearbox, and not only the induction motor. Detecting bearing faults outside the induction motor through the stator current analysis represents an interesting alternative to traditional vibration analysis. Bearing faults cause changes in the stator current spectrum that can be used for fault diagnosis purposes. A time-domain simulation of the drivetrain model is developed. The drivetrain system consists of a loaded single stage gearbox driven by a line-fed induction motor. Three typical bearing faults in the gearbox are addressed, i.e. defects in the outer raceway, the inner raceway, and the rolling element. The interaction with the fault is modelled by means of kinematical and mechanical relations. The fault region is modelled in order to achieve gradual loss and gain of contact. A bearing fault generates an additional torque component that varies at the specific bearing defect frequency. The presented dynamic electromagnetic dq-model of an induction motor is adjusted for diagnostic purpose and considers such torque variations. The bearing fault is detected as a phase modulation of the stator current sine wave at the expected bearing defect frequency.

  18. Fiber Bragg Grating sensor for fault detection in radial and network transmission lines.

    PubMed

    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

  19. Guaranteed robust fault detection and isolation techniques for small satellites

    NASA Astrophysics Data System (ADS)

    Valavani, L.; Tantouris, N.

    2013-12-01

    The paper presents two generic fault detection and isolation (FDI) techniques which have shown remarkable robustness when applied to the SIMULINK model of a small satellite for thruster failures. While fundamentally different in their design approach, they both generate ?structured residuals' which accurately capture the failure mode. The diagnosis criterion in both methods relies on residuals direction rather than magnitude, which avoids the delays and expense of setting accurate thresholds for residuals magnitudes. Most importantly, this fact can account for the enhanced robustness to disturbances and sensor noise, as well as to significant parametric variations. Extensive Monte Carlo simulations are presented validating the robust performance of the two algorithms.

  20. Computational Effective Fault Detection by Means of Signature Functions.

    PubMed

    Baranski, Przemyslaw; Pietrzak, Piotr

    2016-01-01

    The paper presents a computationally effective method for fault detection. A system's responses are measured under healthy and ill conditions. These signals are used to calculate so-called signature functions that create a signal space. The current system's response is projected into this space. The signal location in this space easily allows to determine the fault. No classifier such as a neural network, hidden Markov models, etc. is required. The advantage of this proposed method is its efficiency, as computing projections amount to calculating dot products. Therefore, this method is suitable for real-time embedded systems due to its simplicity and undemanding processing capabilities which permit the use of low-cost hardware and allow rapid implementation. The approach performs well for systems that can be considered linear and stationary. The communication presents an application, whereby an industrial process of moulding is supervised. The machine is composed of forms (dies) whose alignment must be precisely set and maintained during the work. Typically, the process is stopped periodically to manually control the alignment. The applied algorithm allows on-line monitoring of the device by analysing the acceleration signal from a sensor mounted on a die. This enables to detect failures at an early stage thus prolonging the machine's life. PMID:26949942

  1. Computational Effective Fault Detection by Means of Signature Functions

    PubMed Central

    Baranski, Przemyslaw; Pietrzak, Piotr

    2016-01-01

    The paper presents a computationally effective method for fault detection. A system’s responses are measured under healthy and ill conditions. These signals are used to calculate so-called signature functions that create a signal space. The current system’s response is projected into this space. The signal location in this space easily allows to determine the fault. No classifier such as a neural network, hidden Markov models, etc. is required. The advantage of this proposed method is its efficiency, as computing projections amount to calculating dot products. Therefore, this method is suitable for real-time embedded systems due to its simplicity and undemanding processing capabilities which permit the use of low-cost hardware and allow rapid implementation. The approach performs well for systems that can be considered linear and stationary. The communication presents an application, whereby an industrial process of moulding is supervised. The machine is composed of forms (dies) whose alignment must be precisely set and maintained during the work. Typically, the process is stopped periodically to manually control the alignment. The applied algorithm allows on-line monitoring of the device by analysing the acceleration signal from a sensor mounted on a die. This enables to detect failures at an early stage thus prolonging the machine’s life. PMID:26949942

  2. Construction and selection of lifting-based multiwavelets for mechanical fault detection

    NASA Astrophysics Data System (ADS)

    Yuan, Jing; He, Zhengjia; Zi, Yanyang; Wei, Ying

    2013-11-01

    The essence of wavelet transforms is a similar measurement between the signal and the wavelet basis functions. Thus, the construction and selection of the proper wavelet basis functions similar to the fault feature and possessing good properties such as vanishing moments have vital importance to the effective fault diagnosis. In this paper, the construction of lifting-based adaptive multiwavelets with various vanishing moments and the selection rules for different mechanical fault detection are proposed. On the basis of the fixed cubic Hermite multiwavelets, lifting schemes are adopted to construct new changeable multiwavelets with diverse vanishing moments. Then, the defined local spectral entropy minimization rules are proposed to determine the optimum multiwavelets providing the proper vanishing moments, classified into the typical shaft faults, gear faults and rolling bearing faults. The proposed method is applied to incipient fault diagnosis of rolling bearing and gearbox fault diagnosis of rolling mill to verify its effectiveness and feasibility in comparison with different wavelet transforms and spectral kurtosis. The results show that the proposed method can act as a promising tool for mechanical fault detection.

  3. 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.

  4. Fault detection in rotary blood pumps using motor speed response.

    PubMed

    Soucy, Kevin G; Koenig, Steven C; Sobieski, Michael A; Slaughter, Mark S; Giridharan, Guruprasad A

    2013-01-01

    Clinical acceptance of ventricular assist devices (VADs) as long-term heart failure therapy requires safe and effective circulatory support for a minimum of 5 years. Yet, VAD failure beyond 2 years of support is still a concern. Currently, device controllers cannot consistently predict VAD failure modes, and undetected VAD faults may lead to catastrophic device failure. To minimize this risk, a model-based algorithm for reliable VAD fault detection that only requires VAD revolutions per minute (rpm) was developed. The algorithm was tested using computer models of the human cardiovascular system simulating heart failure and axial flow (AF) or centrifugal flow (CF) VADs. Ventricular assist device rpm was monitored after a step down of motor current for normal and simulated fault conditions (>750 faults). The ability to detect fault conditions with 1%, 5%, and 10% rpm measurement noise was evaluated. All failure modes affected the VAD rpm responses to the motor current step down. Fault detection rates were >95% for AF and >89% for CF VADs, even with 10% rpm measurement noise. The VAD rpm responses were significantly altered by blood viscosity (3.5-6.2 cP), which should be accounted for in clinical application. The proposed VAD fault detection algorithm may deliver a convenient and nonintrusive way to minimize catastrophic device failures. PMID:23820281

  5. Soft Computing Application in Fault Detection of Induction Motor

    SciTech Connect

    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.

  6. 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. PMID:26180754

  7. Detecting Hidden Faults and Other Lineations with UAVSAR

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    Jay Parker, Margaret Glasscoe, Andrea Donnellan Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA The M7.2 El Mayor Cucapah Earthquake of April 4, 2010 is the main earthquake to date observed by the NASA UAVSAR. By observing with repeat passes (October 2009, April 2010 captures the coseismic strain pattern, and subsequent flights capture the postseismic process) over the adjoining portion of California, the interferometric phase maps of geodetic displacements are exceptionally high definition (pixel size is roughly 7 m) records of the extended deformation field from the earthquake process, including revelation of a rich network of plate parallel and conjugate faulting, apparently slipping sympathetically to the earthquake-induced quasistatic changes in stress. While the most significant of these faults have been documented by cooperative use of UAVSAR maps and field research, a subsequent opportunity arises: to use this data to develop and validate an automated approach to detecting faults and other lineations directly from the UAVSAR unwrapped phase product that corresponds to a single-component deformation map. The Canny edge detection algorithm is employed, after a preparation stage to clean the data. This preprocessing step is tailored to the nature of the radar phase data: data dropouts in single pixels and extended areas (blown sand dunes, farms) are a much larger problem than background white noise. Blocks of typically 3x3 pixels are currently reduced to a single value, the average after bad pixels are discarded. The smoothing methods typically used with the Canny method are minimized (smoothing makes data drop-out problems worse). The aperture size that determines a gradient estimation is chosen large (7 vs. the typical 3), as this is found to produce continuous (rather than dashed) lineations. The main Canny threshold is chosen to correspond to a user selected slip threshold in mm. Reasonable maps of lineations in the Salton Trough occur with a threshold of 3mm: higher values result in a loss of interesting lineations; lower ones produce excess clutter. Lineation detection from InSAR phase will miss faults parallel to the aircraft flight line. But because it relies on phase changes over small (~20 m) distances the lineations are unaffected by most atmospheric water vapor effects, uncompensated aircraft motion, and distributed tectonic deformation

  8. A fault detection service for wide area distributed computations.

    SciTech Connect

    Stelling, P.

    1998-06-09

    The potential for faults in distributed computing systems is a significant complicating factor for application developers. While a variety of techniques exist for detecting and correcting faults, the implementation of these techniques in a particular context can be difficult. Hence, we propose a fault detection service designed to be incorporated, in a modular fashion, into distributed computing systems, tools, or applications. This service uses well-known techniques based on unreliable fault detectors to detect and report component failure, while allowing the user to tradeoff timeliness of reporting against false positive rates. We describe the architecture of this service, report on experimental results that quantify its cost and accuracy, and describe its use in two applications, monitoring the status of system components of the GUSTO computational grid testbed and as part of the NetSolve network-enabled numerical solver.

  9. Observer and data-driven-model-based fault detection in power plant coal mills

    SciTech Connect

    Odgaard, P.F.; Lin, B.; Jorgensen, S.B.

    2008-06-15

    This paper presents and compares model-based and data-driven fault detection approaches for coal mill systems. The first approach detects faults with an optimal unknown input observer developed from a simplified energy balance model. Due to the time-consuming effort in developing a first principles model with motor power as the controlled variable, data-driven methods for fault detection are also investigated. Regression models that represent normal operating conditions (NOCs) are developed with both static and dynamic principal component analysis and partial least squares methods. The residual between process measurement and the NOC model prediction is used for fault detection. A hybrid approach, where a data-driven model is employed to derive an optimal unknown input observer, is also implemented. The three methods are evaluated with case studies on coal mill data, which includes a fault caused by a blocked inlet pipe. All three approaches detect the fault as it emerges. The optimal unknown input observer approach is most robust, in that, it has no false positives. On the other hand, the data-driven approaches are more straightforward to implement, since they just require the selection of appropriate confidence limit to avoid false detection. The proposed hybrid approach is promising for systems where a first principles model is cumbersome to obtain.

  10. Detection of fault structures with airborne LiDAR point-cloud data

    NASA Astrophysics Data System (ADS)

    Chen, Jie; Du, Lei

    2015-08-01

    The airborne LiDAR (Light Detection And Ranging) technology is a new type of aerial earth observation method which can be used to produce high-precision DEM (Digital Elevation Model) quickly and reflect ground surface information directly. Fault structure is one of the key forms of crustal movement, and its quantitative description is the key to the research of crustal movement. The airborne LiDAR point-cloud data is used to detect and extract fault structures automatically based on linear extension, elevation mutation and slope abnormal characteristics. Firstly, the LiDAR point-cloud data is processed to filter out buildings, vegetation and other non-surface information with the TIN (Triangulated Irregular Network) filtering method and Burman model calibration method. TIN and DEM are made from the processed data sequentially. Secondly, linear fault structures are extracted based on dual-threshold method. Finally, high-precision DOM (Digital Orthophoto Map) and other geological knowledge are used to check the accuracy of fault structure extraction. An experiment is carried out in Beiya Village of Yunnan Province, China. With LiDAR technology, results reveal that: the airborne LiDAR point-cloud data can be utilized to extract linear fault structures accurately and automatically, measure information such as height, width and slope of fault structures with high precision, and detect faults in areas with vegetation coverage effectively.

  11. 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.

  12. Using a microcomputer in fault detection

    NASA Astrophysics Data System (ADS)

    Wen, Kun-Li; Wu, John H.

    1993-09-01

    X-Ray radiography method is a typicial human-visual testing among NDT (nondestructive testing). Due to the labor's film-identified needs and the long practical experience, the skilled labors are hard to be trained. Not only the labor's skiliness and spiritness will affect the quality of the film-identified, hut also the qualtity parameters of flaw (e.g. the size of flaw) cannot be determined by labor within short time. However, computer vision image processing system can give some good characteristics ,such as, high speed, quantitative parameters and non-human's error etc. Developing this system to assist the labor's film-identified will be certainly assuring the film quality, meanwhile, it will be the most powerful method of on-line flaw testing in the future. This paper just focuses the research topic at the identification of X-ray film for the butt welding steel materials1. First, to analize the defect's image model in the X-ray film, then by the image processing technique to build up the propper edge detecting operator and the edge detecting rule, and finally, by the derived edge detector operator to do the mask operation to the X-ray film image, and to detect the flow contour from the segmented defect image for following identification and classification. In this study, we make use of the fuzzy pattern recognition2 and hierarchy classifier to identify the welding flaws.

  13. A Novel Fault Location Method for Radial Distribution Systems

    NASA Astrophysics Data System (ADS)

    Liao, Yuan

    2015-06-01

    This paper presents a new method for locating faults on radial distribution systems utilizing local voltage and current measurements. The method considers feeder shunt capacitances, is applicable to any type of faults, is suitable for unbalanced networks and does not require fault type information. The method is also independent of source impedance. Analytical analysis is utilized to obtain a generic performance equation for any type of faults, which reduces or eliminates iterative steps to reach the fault location. A process to trim down multiple estimates due to laterals is discussed. Evaluation studies based on simulated data have demonstrated the effectiveness of the proposed solution.

  14. Similarity Ratio Analysis for Early Stage Fault Detection with Optical Emission Spectrometer in Plasma Etching Process

    PubMed Central

    Yang, Jie; McArdle, Conor; Daniels, Stephen

    2014-01-01

    A Similarity Ratio Analysis (SRA) method is proposed for early-stage Fault Detection (FD) in plasma etching processes using real-time Optical Emission Spectrometer (OES) data as input. The SRA method can help to realise a highly precise control system by detecting abnormal etch-rate faults in real-time during an etching process. The method processes spectrum scans at successive time points and uses a windowing mechanism over the time series to alleviate problems with timing uncertainties due to process shift from one process run to another. A SRA library is first built to capture features of a healthy etching process. By comparing with the SRA library, a Similarity Ratio (SR) statistic is then calculated for each spectrum scan as the monitored process progresses. A fault detection mechanism, named 3-Warning-1-Alarm (3W1A), takes the SR values as inputs and triggers a system alarm when certain conditions are satisfied. This design reduces the chance of false alarm, and provides a reliable fault reporting service. The SRA method is demonstrated on a real semiconductor manufacturing dataset. The effectiveness of SRA-based fault detection is evaluated using a time-series SR test and also using a post-process SR test. The time-series SR provides an early-stage fault detection service, so less energy and materials will be wasted by faulty processing. The post-process SR provides a fault detection service with higher reliability than the time-series SR, but with fault testing conducted only after each process run completes. PMID:24755865

  15. Detecting Faults In High-Voltage Transformers

    NASA Technical Reports Server (NTRS)

    Blow, Raymond K.

    1988-01-01

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

  16. 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.

  17. On fault detection filters design with unitary transfer function matrices

    NASA Astrophysics Data System (ADS)

    Krokavec, Dušan; Filasová, Anna; Liščinský, Pavol

    2015-11-01

    An adaptation of unitary system principle in fault detection filter design for continuous-time linear MIMO systems is presented in the paper. The conformation is based on an enhanced fault residual transfer function matrix with unitary construction and offers the key advantages on providing high residual sensitivity with respect to faults. Reflecting the emplacement of singular values in unitary construction, an associated structure of linear matrix inequalities with built-in structured constraints is outlined to verify the filter stability. The proposed design conditions are verified by the numerical illustrative example.

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

    NASA Technical Reports Server (NTRS)

    Bickford, Randall L. (Inventor)

    2005-01-01

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

  19. Automated Fault Detection for DIII-D Tokamak Experiments

    SciTech Connect

    Walker, M.L.; Scoville, J.T.; Johnson, R.D.; Hyatt, A.W.; Lee, J.

    1999-11-01

    An automated fault detection software system has been developed and was used during 1999 DIII-D plasma operations. The Fault Identification and Communication System (FICS) executes automatically after every plasma discharge to check dozens of subsystems for proper operation and communicates the test results to the tokamak operator. This system is now used routinely during DIII-D operations and has led to an increase in tokamak productivity.

  20. 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.

  1. Advanced Information Processing System - Fault detection and error handling

    NASA Technical Reports Server (NTRS)

    Lala, J. H.

    1985-01-01

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

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

    SciTech Connect

    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 in 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.

  3. MIL-M-38510/470 test vectors: Fault detection efficiency measurement via hardware fault simulation. [rca 1802 microprocessor

    NASA Technical Reports Server (NTRS)

    Timoc, C. C.

    1980-01-01

    The stuck fault detection efficiency of the test vectors developed for the MIL-M-38510/470 NASA was measured using a hardware stuck fault simulator for the 1802 microprocessor. Thirty-nine stuck faults were not detected out of a total of 874 injected into the combinatorial and sequential parts of the microprocessor. Since undetected faults can create catastrophic errors in equipment designed for high reliability applications, it is recommended that the MIL-M-38510/470 NASA be enhanced with additional test vectors so as to achieve 100% stuck fault detection efficiency.

  4. A dynamic integrated fault diagnosis method for power transformers.

    PubMed

    Gao, Wensheng; Bai, Cuifen; Liu, Tong

    2015-01-01

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

  5. 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. PMID:22163833

  6. Method and apparatus for fault tolerance

    NASA Technical Reports Server (NTRS)

    Masson, Gerald M. (Inventor); Sullivan, Gregory F. (Inventor)

    1993-01-01

    A method and apparatus for achieving fault tolerance in a computer system having at least a first central processing unit and a second central processing unit. The method comprises the steps of first executing a first algorithm in the first central processing unit on input which produces a first output as well as a certification trail. Next, executing a second algorithm in the second central processing unit on the input and on at least a portion of the certification trail which produces a second output. The second algorithm has a faster execution time than the first algorithm for a given input. Then, comparing the first and second outputs such that an error result is produced if the first and second outputs are not the same. The step of executing a first algorithm and the step of executing a second algorithm preferably takes place over essentially the same time period.

  7. Digital analysis of aeromagnetic maps: Detection of a fault

    NASA Astrophysics Data System (ADS)

    Naidu, Prabhakar S.; Mathew, M. P.

    1998-01-01

    A fault in rock strata would invariably disrupt the pattern of the magnetic field contours from which an experienced geophysicist is able to detect a fault, that is, find its location and orientation. In this paper digital signal processing tools, namely, directional filtering and angular spectrum are developed for the detection of a fault from the digital aeromagnetic maps and their performance is compared. Digital filter design tools are used to design a filter with a wedge shaped passband. Both the angle and direction of the axis of the wedge are user specified. The filter is then applied to a model where there are two faulted magnetic layers, each of which is free of any other directionality. It is shown that the filter is able to detect the fault in the deeper layer even in the presence of another faulted layer at a shallow depth. In the second approach the angular spectrum is used to measure the orientation of a known fault. Some care need to be exercised, as a peak in the angular spectrum may also be caused by other factors, such as, the direction of polarization, or by tapered spectral window, etc. We have computed the angular spectrum of the eastern part of the aeromagnetic map over the south Indian shield. It is found that the angular spectrum in the low frequency band shows a peak corresponding to the east-northeast-west-southwest fault suggested in Reddy et al. (1988) [Reddy, A.G.B., Mathew, M.P., Singh, B., Naidu, P.S., 1988. Aeromagnetic evidence of crustal structure in the granitic terrain of Tamilnadu-Kerala. J. Geol. Soc. India 32, 368-381]. Also there are peaks corresponding to the direction of polarization, the tapered window used in the analysis, and a few shallow lineations.

  8. High Resolution Seismic Imaging of Fault Zones: Methods and Examples From The San Andreas Fault

    NASA Astrophysics Data System (ADS)

    Catchings, R. D.; Rymer, M. J.; Goldman, M.; Prentice, C. S.; Sickler, R. R.; Criley, C.

    2011-12-01

    Seismic imaging of fault zones at shallow depths is challenging. Conventional seismic reflection methods do not work well in fault zones that consist of non-planar strata or that have large variations in velocity structure, two properties that occur in most fault zones. Understanding the structure and geometry of fault zones is important to elucidate the earthquake hazard associated with fault zones and the barrier effect that faults impose on subsurface fluid flow. In collaboration with the San Francisco Public Utilities Commission (SFPUC) at San Andreas Lake on the San Francisco peninsula, we acquired combined seismic P-wave and S-wave reflection, refraction, and guided-wave data to image the principal strand of the San Andreas Fault (SAF) that ruptured the surface during the 1906 San Francisco earthquake and additional fault strands east of the rupture. The locations and geometries of these fault strands are important because the SFPUC is seismically retrofitting the Hetch Hetchy water delivery system, which provides much of the water for the San Francisco Bay area, and the delivery system is close to the SAF at San Andreas Lake. Seismic reflection images did not image the SAF zone well due to the brecciated bedrock, a lack of layered stratigraphy, and widely varying velocities. Tomographic P-wave velocity images clearly delineate the fault zone as a low-velocity zone at about 10 m depth in more competent rock, but due to soil saturation above the rock, the P-waves do not clearly image the fault strands at shallower depths. S-wave velocity images, however, clearly show a diagnostic low-velocity zone at the mapped 1906 surface break. To image the fault zone at greater depths, we utilized guided waves, which exhibit high amplitude seismic energy within fault zones. The guided waves appear to image the fault zone at varying depths depending on the frequency of the seismic waves. At higher frequencies (~30 to 40 Hz), the guided waves show strong amplification at the 1906 surface break and at about 20 m to the east, but at lower frequencies (2-5 Hz), the guided waves show strong amplification approximately 10 m east of the 1906 surface break. We attribute the difference in amplification of guided waves to an east-dipping fault strand that merges with other strands below about 10 m depth. Vp/Vs and Poisson's ratios clearly delineate multiple fault strands about 2 km north of the mapped 1906 surface break at the SFPUC intake structure. Combining these fault-imaging methods provide a powerful set of tools for mapping fault zones in the shallow subsurface in areas of complex geology.

  9. 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.

  10. Enhanced detection of rolling element bearing fault based on stochastic resonance

    NASA Astrophysics Data System (ADS)

    Zhang, Xiaofei; Hu, Niaoqing; Cheng, Zhe; Hu, Lei

    2012-11-01

    Early bearing faults can generate a series of weak impacts. All the influence factors in measurement may degrade the vibration signal. Currently, bearing fault enhanced detection method based on stochastic resonance(SR) is implemented by expensive computation and demands high sampling rate, which requires high quality software and hardware for fault diagnosis. In order to extract bearing characteristic frequencies component, SR normalized scale transform procedures are presented and a circuit module is designed based on parameter-tuning bistable SR. In the simulation test, discrete and analog sinusoidal signals under heavy noise are enhanced by SR normalized scale transform and circuit module respectively. Two bearing fault enhanced detection strategies are proposed. One is realized by pure computation with normalized scale transform for sampled vibration signal, and the other is carried out by designed SR hardware with circuit module for analog vibration signal directly. The first strategy is flexible for discrete signal processing, and the second strategy demands much lower sampling frequency and less computational cost. The application results of the two strategies on bearing inner race fault detection of a test rig show that the local signal to noise ratio of the characteristic components obtained by the proposed methods are enhanced by about 50% compared with the band pass envelope analysis for the bearing with weaker fault. In addition, helicopter transmission bearing fault detection validates the effectiveness of the enhanced detection strategy with hardware. The combination of SR normalized scale transform and circuit module can meet the need of different application fields or conditions, thus providing a practical scheme for enhanced detection of bearing fault.

  11. 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.

  12. Detecting Small Earthquakes on Remote Oceanic Transform Faults

    NASA Astrophysics Data System (ADS)

    Vieceli, R.; Ammon, C. J.; Cleveland, M.

    2014-12-01

    Although oceanic transform faults (OTF) constitute a small fraction of the total plate boundary area, better constraints on OTF tectonic parameters (e.g. fault length, slip rate, thermal structure) compared to other tectonic boundaries make OTFs a useful focus of the investigation of earthquake processes. The large fraction of aseismic deformation that accompanies OTF earthquakes also makes them an interesting target for exploring the interaction of creep with slow and quick earthquakes. Because most typical OTFs are quite remote, even indirectly observing these deformation processes is a serious challenge. Standard teleseismic analysis methods have yielded valuable constraints on the first-order characteristics of moderate-to-large magnitude OTF earthquakes, but fundamental questions rgarding rupture length and area as well as rupture-front propagation speed remain unknown in these systems. Even identifying the smaller-magnitude activity that often provides clues to some of these quantities is difficult. Short-period seismic arrays at least occasionally provide information suitable for locations of small (mb < 4.0) earthquakes along Mid-Atlantic transforms such as the Romanche and Chain. In this work, we explore the possibility of detecting smaller earthquakes along remote OTFs using waveform-based comparisons (e.g. cross correlations) of template signals with the continuous seismic wavefield for seismic stations surrounding several OTFs. We examine our ability to detect these small events using a range of frequency bands from short-to-intermediate periods and investigating effective approaches for identifying small-magnitude events along remote OTFs. Preliminary results suggest that at least some small events can be identified using simple waveform templates. Our goal is to construct a metric that will produce acceptable false-alarm rates and that will allow us to visually confirm detections and extend the seismicity catalogs along OTFs to lower magnitude threshold and allow us to continue to investigate OTF deformation processes using remote seismic observations.

  13. Detection of Rooftop Cooling Unit Faults Based on Electrical Measurements

    SciTech Connect

    Armstrong, Peter R.; Laughman, C R.; Leeb, S B.; Norford, L K.

    2006-01-31

    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. This paper describes changes in the power signatures of fans and compressors that were found, experimentally and theoretically, to be useful for fault detection.

  14. An efficient detect model for crosstalk faults on SOC interconnects

    NASA Astrophysics Data System (ADS)

    Zhang, Jinlin; Chen, Chaoyang; Shen, Xubang

    2005-10-01

    As System-on-Chip (SOC) manufacture technology moves into ultra deep sub-micron (DSM) ear, Crosstalk faults between SoC interconnect result in improper function of the chip. This problem is becoming more and more severe. Based on the in-depth research of the property of crosstalk fault and the MAF model, we presented a simple and efficient model: the Search-Based Maximal Aggressor Fault (SB-MAF) for detecting glitch and delay faults caused by crosstalk effects on interconnects between components of a SOC. The respective efficiency of the presented model and the MAF model is given in the paper. The results of simulation show that two models' efficiency is comparable when crosstalk is weak. However, the efficiency of the SB-MAF model is obviously improved compare to the MAF model when there are strong crosstalk effects between SoC interconnects.

  15. 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. PMID:25747198

  16. Online Monitoring System for Performance Fault Detection

    SciTech Connect

    Gioiosa, Roberto; Kestor, Gokcen; Kerbyson, Darren J.

    2014-05-19

    To achieve the exaFLOPS performance within a contain power budget, next supercomputers will feature hundreds of millions of components operating at low- and near-threshold voltage. As the probability that at least one of these components fails during the execution of an application approaches certainty, it seems unrealistic to expect that any run of a scientific application will not experience some performance faults. We believe that there is need of a new generation of light-weight performance and debugging tools that can be used online even during production runs of parallel applications and that can identify performance anomalies during the application execution. In this work we propose the design and implementation of a monitoring system that continuously inspects the evolution of run

  17. Online Monitoring System for Performance Fault Detection

    SciTech Connect

    Gioiosa, Roberto; Kestor, Gokcen; Kerbyson, Darren J.

    2014-12-31

    To achieve the exaFLOPS performance within a contained power budget, next generation supercomputers will feature hundreds of millions of components operating at low- and near-threshold voltage. As the probability that at least one of these components fails during the execution of an application approaches certainty, it seems unrealistic to expect that any run of a scientific application will not experience some performance faults. We believe that there is need of a new generation of light-weight performance and debugging tools that can be used online even during production runs of parallel applications and that can identify performance anomalies during the application execution. In this work we propose the design and implementation of such a monitoring system.

  18. Fault detection and isolation for multisensor navigation systems

    NASA Technical Reports Server (NTRS)

    Kline, Paul A.; Vangraas, Frank

    1991-01-01

    Increasing attention is being given to the problem of erroneous measurement data for multisensor navigation systems. A recursive estimator can be used in conjunction with a 'snapshot' batch estimator to provide fault detection and isolation (FDI) for these systems. A recursive estimator uses past system states to form a new state estimate and compares it to the calculated state based on a new set of measurements. A 'snapshot' batch estimator uses a set of measurements collected simultaneously and compares solutions based on subsets of measurements. The 'snapshot' approach requires redundant measurements in order to detect and isolate faults. FDI is also referred to as Receiver Autonomous Integrity Monitoring (RAIM).

  19. Application of classification functions to chiller fault detection and diagnosis

    SciTech Connect

    Stylianou, M.

    1997-12-31

    This paper describes the application of a statistical pattern recognition algorithm (SPRA) to fault detection and diagnosis of commercial reciprocating chillers. The developed fault detection and diagnosis module has been trained to recognize five distinct conditions, namely, normal operation, refrigerant leak, restriction in the liquid refrigerant line, and restrictions in the water circuits of the evaporator and condenser. The algorithm used in the development is described, and the results of its application to an experimental test bench are discussed. Experimental results show that the SPRA provides an effective way of classifying patterns in multivariable, multiclass problems without having to explicitly use a rule-based system.

  20. Diagnosis of fault gearbox with wavelet packet decomposition and vector statistics method

    NASA Astrophysics Data System (ADS)

    Ren, Xueping; Shao, Wei; Ma, Wensheng

    2008-12-01

    Vibration signals from fault gearbox are usually complex with many different frequencies. As a result, it is difficult to find early symptoms of a potential fault in a gearbox. WPD (Wavelet Packet Decomposition) have been established as the most wide spread tool to disclose transient information in signals and wavelet packet filter is found to be very effective in detection of symptoms from vibration signals of a gearbox with early fatigue tooth crack. The paper presents a method to decompose the fault vibration signals with WPD and analysis the decomposed vectors with statistic algorithm to diagnosis the gearbox fault. The method is considered to be effective with the aim of gearbox fault detection and diagnosis.

  1. 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

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

    PubMed

    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

  3. Fault Detection of Rotating Machinery using the Spectral Distribution Function

    NASA Technical Reports Server (NTRS)

    Davis, Sanford S.

    1997-01-01

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

  4. Method and system for environmentally adaptive fault tolerant computing

    NASA Technical Reports Server (NTRS)

    Copenhaver, Jason L. (Inventor); Jeremy, Ramos (Inventor); Wolfe, Jeffrey M. (Inventor); Brenner, Dean (Inventor)

    2010-01-01

    A method and system for adapting fault tolerant computing. The method includes the steps of measuring an environmental condition representative of an environment. An on-board processing system's sensitivity to the measured environmental condition is measured. It is determined whether to reconfigure a fault tolerance of the on-board processing system based in part on the measured environmental condition. The fault tolerance of the on-board processing system may be reconfigured based in part on the measured environmental condition.

  5. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  7. 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. PMID:26521723

  8. 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

  9. POD Model Reconstruction for Gray-Box Fault Detection

    NASA Technical Reports Server (NTRS)

    Park, Han; Zak, Michail

    2007-01-01

    Proper orthogonal decomposition (POD) is the mathematical basis of a method of constructing low-order mathematical models for the "gray-box" fault-detection algorithm that is a component of a diagnostic system known as beacon-based exception analysis for multi-missions (BEAM). POD has been successfully applied in reducing computational complexity by generating simple models that can be used for control and simulation for complex systems such as fluid flows. In the present application to BEAM, POD brings the same benefits to automated diagnosis. BEAM is a method of real-time or offline, automated diagnosis of a complex dynamic system.The gray-box approach makes it possible to utilize incomplete or approximate knowledge of the dynamics of the system that one seeks to diagnose. In the gray-box approach, a deterministic model of the system is used to filter a time series of system sensor data to remove the deterministic components of the time series from further examination. What is left after the filtering operation is a time series of residual quantities that represent the unknown (or at least unmodeled) aspects of the behavior of the system. Stochastic modeling techniques are then applied to the residual time series. The procedure for detecting abnormal behavior of the system then becomes one of looking for statistical differences between the residual time series and the predictions of the stochastic model.

  10. 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.

  11. Adaptive redundant multiwavelet denoising with improved neighboring coefficients for gearbox fault detection

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Zi, Yanyang; He, Zhengjia; Wang, Xiaodong

    2013-07-01

    Gearbox fault detection under strong background noise is a challenging task. It is feasible to make the fault feature distinct through multiwavelet denoising. In addition to the advantage of multi-resolution analysis, multiwavelet with several scaling functions and wavelet functions can detect the different fault features effectively. However, the fixed basis functions not related to the given signal may lower the accuracy of fault detection. Moreover, the multiwavelet transform may result in Gibbs phenomena in the step of reconstruction. Furthermore, both traditional term-by-term threshold and neighboring coefficients do not consider the direct spatial dependency of wavelet coefficients at adjacent scale. To overcome these deficiencies, adaptive redundant multiwavelet (ARM) denoising with improved neighboring coefficients (NeighCoeff) is proposed. Based on symmetric multiwavelet lifting scheme (SMLS), taking kurtosis—partial envelope spectrum entropy as the evaluation objective and genetic algorithms as the optimization method, ARM is proposed. Considering the intra-scale and inter-scale dependency of wavelet coefficients, the improved NeighCoeff method is developed and incorporated into ARM. The proposed method is applied to both the simulated signal and the practical gearbox vibration signal under different conditions. The results show its effectiveness and reliance for gearbox fault detection.

  12. A fault-tolerant voltage measurement method for series connected battery packs

    NASA Astrophysics Data System (ADS)

    Xia, Bing; Mi, Chris

    2016-03-01

    This paper proposes a fault-tolerant voltage measurement method for battery management systems. Instead of measuring the voltage of individual cells, the proposed method measures the voltage sum of multiple battery cells without additional voltage sensors. A matrix interpretation is developed to demonstrate the viability of the proposed sensor topology to distinguish between sensor faults and cell faults. A methodology is introduced to isolate sensor and cell faults by locating abnormal signals. A measurement electronic circuit is proposed to implement the design concept. Simulation and experiment results support the mathematical analysis and validate the feasibility and robustness of the proposed method. In addition, the measurement problem is generalized and the condition for valid sensor topology is discovered. The tuning of design parameters are analyzed based on fault detection reliability and noise levels.

  13. Battery Fault Detection with Saturating Transformers

    NASA Technical Reports Server (NTRS)

    Davies, Francis J. (Inventor); Graika, Jason R. (Inventor)

    2013-01-01

    A battery monitoring system utilizes a plurality of transformers interconnected with a battery having a plurality of battery cells. Windings of the transformers are driven with an excitation waveform whereupon signals are responsively detected, which indicate a health of the battery. In one embodiment, excitation windings and sense windings are separately provided for the plurality of transformers such that the excitation waveform is applied to the excitation windings and the signals are detected on the sense windings. In one embodiment, the number of sense windings and/or excitation windings is varied to permit location of underperforming battery cells utilizing a peak voltage detector.

  14. 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.

  15. Fault detection and bypass in a sequence information signal processor

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

  16. Fault Detection and Isolation for Hydraulic Control

    NASA Technical Reports Server (NTRS)

    1987-01-01

    Pressure sensors and isolation valves act to shut down defective servochannel. Redundant hydraulic system indirectly senses failure in any of its electrical control channels and mechanically isolates hydraulic channel controlled by faulty electrical channel so flat it cannot participate in operating system. With failure-detection and isolation technique, system can sustains two failed channels and still functions at full performance levels. Scheme useful on aircraft or other systems with hydraulic servovalves where failure cannot be tolerated.

  17. 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.

  18. 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.

  19. Inversion method of seismic forces at fault using finite element

    NASA Astrophysics Data System (ADS)

    Liu, D.; Xie, Z.; Geng, W.; Cai, Y.

    2013-12-01

    Fault slip inversion using seismic dislocation model has been discussed a lot. In this model, seismogenic fault is considered as an interface. However, geological surveys and seismic channel waves reveal that the fault usually possesses thickness. Rock compression tests also show that micro-cracks develop into a belt in which shear fracture plane takes place. Therefore, to simulate the fault as a narrow belt may be more reasonable to reflect mechanical behavior of earthquake source. This study proposes a method to inverse seismic forces at the fault with thickness. The fault is modeled by transversely isotropic material. Three-dimensional finite element models (FEMs) is used to calculate numerical Green's functions for displacements. The Green's functions are generated by imposing unit couples directly to the node pairs at the fault instead of dislocation. The unit couples are added separately in x, y, z directions of the finite element global coordinate system. A pure thrust earthquake is modeled by reducing shear modulus under tectonic stress field. Selected surface displacements induced by this earthquake are used as 'observation data' of the inversion. We combine numerical Green's functions with standard linear inverse methods with Laplace smoothing constraints to estimate seismic forces at the fault. The earthquake which is simulated by damage of shear modulus has the fault model with transversely isotropic material, therefore there exist no normal forces. When the fault material is isotropic and the earthquake is caused by the reduction of shear or Young's modulus, there are normal forces at the fault. This study shows that we can directly inverse three-dimensional seismic forces with the surface deformation caused by earthquakes. This method is feasible for heterogeneous materials and complicated geometry model. [1] Xie, Zhoumin, Inversion method of seismic stress drop by finite element scheme, Doctor Thesis, Peking University, 2013. [2] Hu, C., Zhou, Y., & Cai, Y., A new finite element model in studying earthquake triggering and continuous evolution of stress field, Science in China Series D: Earth Sciences, 2009

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

    PubMed

    Ferdowsi, Hasan; Jagannathan, Sarangapani; Zawodniok, Maciej

    2014-05-01

    Measured data or states for a nonlinear dynamic system is usually contaminated by outliers. Identifying and removing outliers will make the data (or system states) more trustworthy and reliable since outliers in the measured data (or states) can cause missed or false alarms during fault diagnosis. In addition, faults can make the system states nonstationary needing a novel analytical model-based fault detection (FD) framework. In this paper, an online outlier identification and removal (OIR) scheme is proposed for a nonlinear dynamic system. Since the dynamics of the system can experience unknown changes due to faults, traditional observer-based techniques cannot be used to remove the outliers. The OIR scheme uses a neural network (NN) to estimate the actual system states from measured system states involving outliers. With this method, the outlier detection is performed online at each time instant by finding the difference between the estimated and the measured states and comparing its median with its standard deviation over a moving time window. The NN weight update law in OIR is designed such that the detected outliers will have no effect on the state estimation, which is subsequently used for model-based fault diagnosis. In addition, since the OIR estimator cannot distinguish between the faulty or healthy operating conditions, a separate model-based observer is designed for fault diagnosis, which uses the OIR scheme as a preprocessing unit to improve the FD performance. The stability analysis of both OIR and fault diagnosis schemes are introduced. Finally, a three-tank benchmarking system and a simple linear system are used to verify the proposed scheme in simulations, and then the scheme is applied on an axial piston pump testbed. The scheme can be applied to nonlinear systems whose dynamics and underlying distribution of states are subjected to change due to both unknown faults and operating conditions. PMID:24808037

  1. Construction of customized redundant multiwavelet via increasing multiplicity for fault detection of rotating machinery

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Zuo, Ming J.; Zi, Yanyang; He, Zhengjia

    2014-01-01

    Fault detection from the vibration measurement data of rotating machinery is significant for avoiding serious accidents. However, non-stationary vibration signal with a large amount of noise makes this task challenging. Multiwavelet not only owns the advantage on multi-resolution analysis but also can offer multiple wavelet basis functions. So it has the possibility of detecting various fault features preferably. However, the fixed basis functions which are not related to the given signal may lower the accuracy of fault detection. Moreover, another major intrinsic deficiency of multiwavelet lies in its critically sampled filter-bank, which causes shift-variance and is harmful to extract the feature of periodical impulses. To overcome these deficiencies, a new method called customized redundant multiwavelet (CRM) is constructed via increasing multiplicity (IM). IM is a simple method to design a series of changeable multiwavelet which are available for the subsequent optimization process. By the rule of the envelope spectrum entropy minimum principle, optimal multiwavelet is searched for. Based on the customized multiwavelet filters, the filters of CRM can be calculated by inserting zeros. The proposed method is applied to analyze the simulation, gearbox and rolling element bearing vibration signals. Compared with some other conventional methods, the results demonstrate that the proposed method possesses robust performance in detecting fault features of rotating machinery.

  2. A Compensation Method of Conductor Parameter for Transient Fault Location

    NASA Astrophysics Data System (ADS)

    Ugbome, Chukwunweike Lucky

    Faults in underground distribution systems are predominantly caused by the deterioration of cable insulation. The inherent nature of underground distribution is such that cables are laid underground and exposed to harmful substances which can cause deterioration of cable insulation. The penetration of water into the cable splice is a common cause of cable deterioration and a common source of transitory sub-cycle cable fault in underground distribution systems. The presence of a sub-cycle fault in a distribution line is not necessarily noticeable and may not cause any protective device to operate due to its short live-span but can be destructive if it is sustained and unattended to. The location of transitory sub-cycle fault in underground cable is fundamentally important in preventing and containing a permanent fault which can potentially result to an unplanned outage. However the location of this type of fault is not easy due to so many unknowns. A few numbers of approaches have been developed for determining the location of short-lived sub-cycle (SLSC) faults, but they approximate the conductor parameter which would reduce the accuracy of the location determination. This thesis develops an algorithm for transitory sub-cycle fault location to compensate for the ignored conductor parameter by employing the X/R ratio of the distribution line. First, a model for transient faults at different locations in underground cable is presented and used to generate the voltage and current waveforms at the source side. Also presented is the performance of the fault location by the uncompensated and compensated algorithms under two configurations of the distribution line: a homogeneous distribution circuit and a heterogeneous distribution line. The result obtained from the performance studies show that the proposed compensation method would help the non-compensated fault location approaches to achieve relatively high accuracy in locating transitory sub-cycle faults in numerous configurations of distribution lines.

  3. Dynamic Structural Fault Detection and Identification

    NASA Technical Reports Server (NTRS)

    Smith, Timothy; Reichenbach, Eric; Urnes, James M.

    2009-01-01

    Aircraft structures are designed to guarantee safety of flight in some required operational envelope. When the aircraft becomes structurally impaired, safety of flight may not be guaranteed within that previously safe operational envelope. In this case the safe operational envelope must be redefined in-flight and a means to prevent excursion from this new envelope must be implemented. A specific structural failure mode that may result in a reduced safe operating envelope, the exceedance of which could lead to catastrophic structural failure of the aircraft, will be addressed. The goal of the DFEAP program is the detection of this failure mode coupled with flight controls adaptation to limit critical loads in the damaged aircraft structure. The DFEAP program is working with an F/A-18 aircraft model. The composite wing skins are bonded to metallic spars in the wing substructure. Over time, it is possible that this bonding can deteriorate due to fatigue. In this case, the ability of the wing spar to transfer loading between the wing skins is reduced. This failure mode can translate to a reduced allowable compressive strain on the wing skin and could lead to catastrophic wing buckling if load limiting of the wing structure is not applied. The DFEAP program will make use of a simplified wing strain model for the healthy aircraft. The outputs of this model will be compared in real-time to onboard strain measurements at several locations on the aircraft wing. A damage condition is declared at a given location when the strain measurements differ sufficiently from the strain model. Parameter identification of the damaged structure wing strain parameters will be employed to provide load limiting control adaptation for the aircraft. This paper will discuss the simplified strain models used in the implementation and their interaction with the strain sensor measurements. Also discussed will be the damage detection and identification schemes employed and the means by which the damaged aircraft parameters will be used to provide load limiting that keeps the aircraft within the safe operational envelope.

  4. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2004-01-01

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

  6. Design methods for fault-tolerant finite state machines

    NASA Technical Reports Server (NTRS)

    Niranjan, Shailesh; Frenzel, James F.

    1993-01-01

    VLSI electronic circuits are increasingly being used in space-borne applications where high levels of radiation may induce faults, known as single event upsets. In this paper we review the classical methods of designing fault tolerant digital systems, with an emphasis on those methods which are particularly suitable for VLSI-implementation of finite state machines. Four methods are presented and will be compared in terms of design complexity, circuit size, and estimated circuit delay.

  7. Simulation of secondary fault shear displacements - method and application

    NASA Astrophysics Data System (ADS)

    Fälth, Billy; Hökmark, Harald; Lund, Björn; Mai, P. Martin; Munier, Raymond

    2014-05-01

    We present an earthquake simulation method to calculate dynamically and statically induced shear displacements on faults near a large earthquake. Our results are aimed at improved safety assessment of underground waste storage facilities, e.g. a nuclear waste repository. For our simulations, we use the distinct element code 3DEC. We benchmark 3DEC by running an earthquake simulation and then compare the displacement waveforms at a number of surface receivers with the corresponding results obtained from the COMPSYN code package. The benchmark test shows a good agreement in terms of both phase and amplitude. In our application to a potential earthquake near a storage facility, we use a model with a pre-defined earthquake fault plane (primary fault) surrounded by numerous smaller discontinuities (target fractures) representing faults in which shear movements may be induced by the earthquake. The primary fault and the target fractures are embedded in an elastic medium. Initial stresses are applied and the fault rupture mechanism is simulated through a programmed reduction of the primary fault shear strength, which is initiated at a pre-defined hypocenter. The rupture is propagated at a typical rupture propagation speed and arrested when it reaches the fault plane boundaries. The primary fault residual strength properties are uniform over the fault plane. The method allows for calculation of target fracture shear movements induced by static stress redistribution as well as by dynamic effects. We apply the earthquake simulation method in a model of the Forsmark nuclear waste repository site in Sweden with rock mass properties, in situ stresses and fault geometries according to the description of the site established by the Swedish Nuclear Fuel and Waste Management Co (SKB). The target fracture orientations are based on the Discrete Fracture Network model developed for the site. With parameter values set to provide reasonable upper bound estimates of target fracture displacements, the model generates primary fault slip and slip velocities that are both high compared to those found in real earthquakes. The calculated target fracture movements reach some tens of millimetres on 300 m diameter fractures. We also present results indicating the sensitivity of primary fault slip and target fracture movements to e.g. variation of primary fault residual strength, change of hypocenter location and variations in the initial stress field.

  8. Sensor configuration and test for fault diagnoses of subway braking system based on signed digraph method

    NASA Astrophysics Data System (ADS)

    Zuo, Jianyong; Chen, Zhongkai

    2014-05-01

    Fault diagnosis of various systems on rolling stock has drawn the attention of many researchers. However, obtaining an optimized sensor set of these systems, which is a prerequisite for fault diagnosis, remains a major challenge. Available literature suggests that the configuration of sensors in these systems is presently dependent on the knowledge and engineering experiences of designers, which may lead to insufficient or redundant development of various sensors. In this paper, the optimization of sensor sets is addressed by using the signed digraph (SDG) method. The method is modified for use in braking systems by the introduction of an effect-function method to replace the traditional quantitative methods. Two criteria are adopted to evaluate the capability of the sensor sets, namely, observability and resolution. The sensors configuration method of braking system is proposed. It consists of generating bipartite graphs from SDG models and then solving the set cover problem using a greedy algorithm. To demonstrate the improvement, the sensor configuration of the HP2008 braking system is investigated and fault diagnosis on a test bench is performed. The test results show that SDG algorithm can improve single-fault resolution from 6 faults to 10 faults, and with additional four brake cylinder pressure (BCP) sensors it can cover up to 67 double faults which were not considered by traditional fault diagnosis system. SDG methods are suitable for reducing redundant sensors and that the sensor sets thereby obtained are capable of detecting typical faults, such as the failure of a release valve. This study investigates the formal extension of the SDG method to the sensor configuration of braking system, as well as the adaptation supported by the effect-function method.

  9. Investigation of Active Fault Scarps by Means of Geophysical Prospecting Methods, Javakheti Fault Case, Georgia

    NASA Astrophysics Data System (ADS)

    Elashvili, M.; Sakhelashvili, G.; Gigiberia, M.; Maisaia, I.; Godoladze, T.; Javakhishvili, Z.; Durgaryan, R.; Gevorgyan, M.

    2011-12-01

    Current presentation concerns investigation of Javakheti seismically active fault (Georgia, South Caucasus region) by means of Geophysical prospecting methods, carried out during the past two years. The named fault represents the major seismo tectonic structure at Javakhety volcanic highland. Fault segments at some places are well expressed on surface and several of those were mapped even during the Geologic surveys carried in 60-70's of previous century, though not recognized as a single structure. Detailed study of seismically active faults is an important component for proper seismic hazard assessment. Fault scarps, an evidence of fault's activity, are expressed on the earth surface as a result of accumulated rapid displacements due to earthquakes. Geomorphologic studies could provide us with rather general information about the fault, while much more information can be derived from paleo trenching and borehole coring. Unfortunately these methods are quite expensive and time consuming, requiring significant technical and man resources. Shallow Geophysical prospecting methods seems to be a valuable addition to above mentioned techniques. In our case extensive Geophysical prospecting surveys, preceded by Geomorphologic and Geologic Surveys have provided valuable information, first of all for correct identification of fault but also regarding the fault dynamics and internal structure of scarps. During this year geophysical studies were followed by paleo trenching at two locations, preliminary selected based on Geophysical data. Both trenches appeared to be successful, were revealed tracks of several paleo earthquakes currently under processing. Studies were also focused on development of Geophysical prospecting techniques and Interpretation of the results. During the past two years fault scarps were studied by means of Seismic prospecting methods (refracted waves, 2D tomography and surface waves), electric resistivity and Ground Penetrating Radar (200 and 80 MHz antennas). Al these rather inexpensive methods were applied along the same profiles, supplementing each other and providing favorable conditions for analysis and interpretation. As mentioned above, two of the profiles were excavated providing ground truth data and giving more confidence two our interpretations. Presumably, the approaches developed and accumulated experience could be of interest for future studies.

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

    NASA Technical Reports Server (NTRS)

    Gonzalez, Marcelo C.; Button, Robert M.

    2003-01-01

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

  11. 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.

  12. Geophysical methods applied to fault characterization and earthquake potential assessment in the Lower Tagus Valley, Portugal

    NASA Astrophysics Data System (ADS)

    Carvalho, João; Cabral, João; Gonçalves, Rui; Torres, Luís; Mendes-Victor, Luís

    2006-06-01

    The study region is located in the Lower Tagus Valley, central Portugal, and includes a large portion of the densely populated area of Lisbon. It is characterized by a moderate seismicity with a diffuse pattern, with historical earthquakes causing many casualties, serious damage and economic losses. Occurrence of earthquakes in the area indicates the presence of seismogenic structures at depth that are deficiently known due to a thick Cenozoic sedimentary cover. The hidden character of many of the faults in the Lower Tagus Valley requires the use of indirect methodologies for their study. This paper focuses on the application of high-resolution seismic reflection method for the detection of near-surface faulting on two major tectonic structures that are hidden under the recent alluvial cover of the Tagus Valley, and that have been recognized on deep oil-industry seismic reflection profiles and/or inferred from the surface geology. These are a WNW-ESE-trending fault zone located within the Lower Tagus Cenozoic basin, across the Tagus River estuary (Porto Alto fault), and a NNE-SSW-trending reverse fault zone that borders the Cenozoic Basin at the W (Vila Franca de Xira-Lisbon fault). Vertical electrical soundings were also acquired over the seismic profiles and the refraction interpretation of the reflection data was carried out. According to the interpretation of the collected data, a complex fault pattern disrupts the near surface (first 400 m) at Porto Alto, affecting the Upper Neogene and (at least for one fault) the Quaternary, with a normal offset component. The consistency with the previous oil-industry profiles interpretation supports the location and geometry of this fault zone. Concerning the second structure, two major faults were detected north of Vila Franca de Xira, supporting the extension of the Vila Franca de Xira-Lisbon fault zone northwards. One of these faults presents a reverse geometry apparently displacing Holocene alluvium. Vertical offsets of the Holocene sediments detected in the studied geophysical data of Porto Alto and Vila Franca de Xira-Lisbon faults imply minimum slip rates of 0.15-0.30 mm/year, three times larger than previously inferred for active faults in the Lower Tagus Valley and maximum estimates of average return periods of 2000-5000 years for M 6.5-7 co-seismic ruptures.

  13. State variable method of fault tree analysis

    SciTech Connect

    Bartholomew, R.J.; Knudsen, H.K.; Whan, G.A.

    1984-01-01

    The current technique of Fault Tree Analysis (FTA) generally employs computer codes that calculate the minimal cut sets of the Boolean function, where each cut set comprises basic initiator events (roots) whose intersection implies the occurrence of a TOP (system failure) event. Because the number of calculations can be very large for typical fault trees, the importance of any given cut set is assessed by qualitative algorithms that may include the number of basic events in the cut set, and quantitative importance algorithms that involve probabilistic upper and lower bound estimates, and the sets are culled before quantitative probability calculations are made. The assumption of statistical independence of all events is often imposed as a requirement for quantititative analysis. The question addressed in this paper is: can a tractable mathematical model be found that can perform quantitative calculations without the need of upper or lower bound simplifications and include within its structure the capability of handling common cause/common mode statistical dependence, failure mode coupling interdependence, and sequential failure time dependence. The Failure Mode State Variable (FMSV) model developed here is the affirmative answer to this question. The model is applied to simplified fault trees representing some nuclear power system components and subsystems considered for space electric power generation.

  14. Fault detection system for Argentine Research Reactor instrumentation

    SciTech Connect

    Polenta, H.P. ); Bernard, J.A. ); Ray, A. )

    1993-01-20

    The design and implementation of a redundancy management scheme for the on-line detection and isolation of faulty sensors is presented. Such a device is potentially useful in reactor-powered spacecraft for enhancing the processing capabilities of the main computer. The fault detection device can be used as an integral part of intelligent instrumentation systems. The device has been built using an 8-bit microcontroller and commercially available electronic hardware. The software is completely portable. The operation of this device has been successfully demonstrated for real-time validation of sensor data on Argentina's RA-1 Research Reactor.

  15. A coupled rotor-fuselage vibration analysis for helicopter rotor system fault detection

    NASA Astrophysics Data System (ADS)

    Yang, Mao

    A coupled rotor-fuselage vibration analysis for helicopter rotor system fault detection is developed. The coupled rotor/fuselage/vibration absorbers (bifilar type) system incorporates consistent structural, aerodynamic and inertial couplings. The aeroelastic analysis is based on finite element methods in space and time. The coupled rotor, absorbers and fuselage equations are transformed into the modal space and solved in the fixed coordinate system. A coupled trim procedure is used to solve the responses of rotor, fuselage and vibration absorber, rotor trim control and vehicle orientation simultaneously. Rotor system faults are modeled by changing blade structural, inertial and aerodynamic properties. Both adjustable and component faults, such as misadjusted trim-tab, misadjusted pitch-control rod (PCR), imbalanced mass and pitch-control bearing freeplay, are investigated. Detailed SH-60 helicopter fuselage NASTRAN model is integrated into the analysis. Validation study was performed using SH-60 helicopter flight test data. The prediction of fuselage natural frequencies show fairly large error compared to shake test data. Analytical predictions of fuselage baseline (without fault) 4/rev vibration and fault-induced 1/rev vibration and blade displacement deviations are compared with SH-60 flight test (with prescribed fault) data. The fault-induced 1/rev fuselage vibration (magnitude and phase) predicted by present analysis generally capture the trend of the flight test data, although prediction under-predicts. The large discrepancy of fault-induced 1/rev vibration magnitude at hover between prediction and flight test data partially comes from the variation of flight condition (not perfect hover) and partially due to the effect of the rotor-fuselage aerodynamic interaction (wake effect) at low speed which is not considered in the analysis. Also the differences in the phase prediction is not clear since only the magnitude and phase information were given instead of the original vibration time-history. The imbalanced mass fault causes higher 1/rev roll vibration that is insensitive to the airspeed. The misadjusted trim-tab fault induced 1/rev vertical vibration increases with airspeed. The misadjusted pitch-control rod fault causes high vibration at hover. A parametric study was conducted to identify key factors that affect the fault-induced fuselage vibration. Analysis show that elastic fuselage model and precise hub modeling (inclusion of vibration absorbers) are essential to the vibration pre diction. The analysis shows that a compound fault can be expressed as a linear combination of individual faults involved. Aircraft operational parameters, such as gross-weight; center of gravity location, flight speed, flight path and aircraft configuration, have significant impact on the fault-induced 1/rev vibration. Prediction show that there are certain patterns in the fault-induced 1/rev hub-loads. Thus measuring both fuselage vibration and hub loads may benefit rotor system fault detection.

  16. Design Method of Fault Detector for Injection Unit

    NASA Astrophysics Data System (ADS)

    Ochi, Kiyoshi; Saeki, Masami

    An injection unit is considered as a speed control system utilizing a reaction-force sensor. Our purpose is to design a fault detector that detects and isolates actuator and sensor faults under the condition that the system is disturbed by a reaction force. First described is the fault detector's general structure. In this system, a disturbance observer that estimates the reaction force is designed for the speed control system in order to obtain the residual signals, and then post-filters that separate the specific frequency elements from the residual signals are applied in order to generate the decision signals. Next, we describe a fault detector designed specifically for a model of the injection unit. It is shown that the disturbance imposed on the decision variables can be made significantly small by appropriate adjustments to the observer bandwidth, and that most of the sensor faults and actuator faults can be detected and some of them can be isolated in the frequency domain by setting the frequency characteristics of the post-filters appropriately. Our result is verified by experiments for an actual injection unit.

  17. Bearing Fault Detection Using Artificial Neural Networks and Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Samanta, B.; Al-Balushi, Khamis R.; Al-Araimi, Saeed A.

    2004-12-01

    A study is presented to compare the performance of bearing fault detection using three types of artificial neural networks (ANNs), namely, multilayer perceptron (MLP), radial basis function (RBF) network, and probabilistic neural network (PNN). The time domain vibration signals of a rotating machine with normal and defective bearings are processed for feature extraction. The extracted features from original and preprocessed signals are used as inputs to all three ANN classifiers: MLP, RBF, and PNN for two-class (normal or fault) recognition. The characteristic parameters like number of nodes in the hidden layer of MLP and the width of RBF, in case of RBF and PNN along with the selection of input features, are optimized using genetic algorithms (GA). For each trial, the ANNs are trained with a subset of the experimental data for known machine conditions. The ANNs are tested using the remaining set of data. The procedure is illustrated using the experimental vibration data of a rotating machine with and without bearing faults. The results show the relative effectiveness of three classifiers in detection of the bearing condition.

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

    NASA Astrophysics Data System (ADS)

    Yan, Weizhong

    2001-03-01

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

  19. 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.

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

    NASA Technical Reports Server (NTRS)

    Hanson, Matt

    1990-01-01

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

  1. Use of Integrated MASTER Multispectral Imagery and LiDAR DEM for Active Fault Detection and Evaluation

    NASA Astrophysics Data System (ADS)

    Perez, F. G.; Bryant, W. A.; Treiman, J. A.; Real, C. R.; Hook, S.

    2011-12-01

    Displacement caused by surface fault rupture associated with large earthquakes not only disrupts infrastructure and damages natural and built environments, but also constitutes a life safety hazard. The California Geological Survey (CGS) has the authority and responsibility, under the Alquist-Priolo Earthquake Fault Zoning Act, to identify and map active faults in California for the purpose of surface rupture hazard identification and mitigation through regulatory zoning. Mapping and evaluation of active faults is generally accomplished through conventional aerial photo interpretation and field mapping, which rely on recognizing fault-related geomorphic features and juxtaposition of contrasting rocks, soil, and geologic structure. Faults covered by vegetation or concealed by young alluvium will most likely not be detected by this method. Furthermore, spatial accuracy of photo-interpreted fault traces is limited to the accuracy, scale, and method of transfer to conventional topographic base maps, which generally lack the spatial accuracy of geolocated imagery. The inherent limitations of conventional active fault mapping are expected to be overcome by using integrated MASTER and LiDAR data. MASTER is a multispectral imagery with 50 spectral bands ranging from visible to thermal region of the electromagnetic spectrum. LiDAR on the other hand is a laser-based technology with very high positional accuracy, sub-meter resolution and capability to filter out vegetation. MASTER and LiDAR are integrated via data transformation/fusion and the resulting fused imagery are utilized to interpret active faults through recognition of fault features associated with different distinctive properties related to geology, drainage, vegetation, hydrology, thermal, anthropogenic, and topography. The completeness and accuracy of the fault interpretation is gauged by overlaying it to a baseline data of previously mapped fault traces. The research study, supported by a NASA grant, evaluated a well-mapped, 26-km reach of the southern San Andreas Fault Zone in the Antelope Valley near Palmdale.

  2. Classification techniques for fault detection and diagnosis of an air-handling unit

    SciTech Connect

    House, J.M.; Lee, W.Y.; Shin, D.R.

    1999-07-01

    The objective of this study is to demonstrate the application of several classification techniques to the problem of detecting and diagnosing faults in data generated by a variable-air-volume air-handling unit simulation model and to describe the strengths and weaknesses of the techniques considered. Artificial neural network classifiers, nearest neighbor classifiers, nearest prototype classifiers, a rule-based classifier, and a Bayes classifier are considered for both fault detection and diagnostics. Based on the performance of the classification techniques, the Bayes classifier appears to be a good choice for fault detection. It is a straightforward method that requires limited memory and computational effort, and it consistently yielded the lowest percentage of incorrect diagnosis. For fault diagnosis, the rule-based method is favored for classification problems such as the one considered here, where the various classes of faulty operation are well separated and can be distinguished by a single dominant symptom or feature. Results also indicate that the success or failure of classification techniques hinges to a large degree on an ability to separate different classes of operation in some feature (temperature, pressure, etc.) space. Hence, preprocessing of data to extract dominant features is as important as the selection of the classifier.

  3. Detection of sub-seismic fault footprint from signal-to-noise ratio based on wavelet modulus maximum in the tight reservoir

    NASA Astrophysics Data System (ADS)

    Xu, Huiqun; Sun, Sam Zandong; Gui, Zhixian; Luo, Shuiliang

    2015-03-01

    Subtle fault (sub-seismic fault) interpretation, which lies below the seismic resolution, is restricting to the tight reservoir exploration. To obtain the sub-seismic faults, significant effort is required to identify their footprint in the seismic data. We introduce a method, which is signal-to-noise ratio based on wavelet modulus maximum (SNRWMM), for detecting sub-seismic fault footprint. SNRWMM is calculated by the wavelet modulus maximum. Furthermore, we discuss the calculation procedure. We applied the proposed method on the 2D synthetic and real data, and found that the sub-seismic fault can be visible. And so the sub-seismic fault footprint, which existed in the imaging logging data, can be identified by the seismic interpreter. Consequently, SNRWMM can be used as a proxy for sub-seismic faults, and this method can be used to extract the implicit information to bridge the information gap between sub-seismic faults and well logging data.

  4. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  6. Hidden Markov models for fault detection in dynamic systems

    NASA Astrophysics Data System (ADS)

    Smyth, Padhraic J.

    1993-04-01

    The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

  7. Hidden Markov models for fault detection in dynamic systems

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic J. (Inventor)

    1995-01-01

    The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) (vertical bar)/x), 1 less than or equal to i isless than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

  8. Hidden Markov models for fault detection in dynamic systems

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic J. (Inventor)

    1993-01-01

    The invention is a system failure monitoring method and apparatus which learns the symptom-fault mapping directly from training data. The invention first estimates the state of the system at discrete intervals in time. A feature vector x of dimension k is estimated from sets of successive windows of sensor data. A pattern recognition component then models the instantaneous estimate of the posterior class probability given the features, p(w(sub i) perpendicular to x), 1 less than or equal to i is less than or equal to m. Finally, a hidden Markov model is used to take advantage of temporal context and estimate class probabilities conditioned on recent past history. In this hierarchical pattern of information flow, the time series data is transformed and mapped into a categorical representation (the fault classes) and integrated over time to enable robust decision-making.

  9. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System

    PubMed Central

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

  10. A Novel Mittag-Leffler Kernel Based Hybrid Fault Diagnosis Method for Wheeled Robot Driving System.

    PubMed

    Yuan, Xianfeng; Song, Mumin; Zhou, Fengyu; Chen, Zhumin; Li, Yan

    2015-01-01

    The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods. PMID:26229526

  11. A neural network approach to fault detection in spacecraft attitude determination and control systems

    NASA Astrophysics Data System (ADS)

    Schreiner, John N.

    This thesis proposes a method of performing fault detection and isolation in spacecraft attitude determination and control systems. The proposed method works by deploying a trained neural network to analyze a set of residuals that are defined such that they encompass the attitude control, guidance, and attitude determination subsystems. Eight neural networks were trained using either the resilient backpropagation, Levenberg-Marquardt, or Levenberg-Marquardt with Bayesian regularization training algorithms. The results of each of the neural networks were analyzed to determine the accuracy of the networks with respect to isolating the faulty component or faulty subsystem within the ADCS. The performance of the proposed neural network-based fault detection and isolation method was compared and contrasted with other ADCS FDI methods. The results obtained via simulation showed that the best neural networks employing this method successfully detected the presence of a fault 79% of the time. The faulty subsystem was successfully isolated 75% of the time and the faulty components within the faulty subsystem were isolated 37% of the time.

  12. 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.

  13. Seismogenic Fault Detection by Different Hypocenter Location Algorithms in the Southern Tyrrhenian Sea, Italy

    NASA Astrophysics Data System (ADS)

    Neri, G.; Presti, D.; de Natale, G.; Troise, C.

    2004-12-01

    We investigated the seismicity occurring in the last few decades along the continental margin of the southern Tyrrhenian region. In this portion of the Nubia-Europe contact belt the Tindari fault (TF) is a regional structure capable of up to 6 magnitude earthquakes linking the ongoing contractional and extensional crustal compartments of Western and Eastern Sicily, respectively. According to several investigators, TF represents the northwestward propagation of the Malta escarpment, a normal fault linking Eastern Sicily to Malta island which produced magnitude 7 earthquakes in the last centuries. West of TF in the Tyrrhenian sea the Sisifo fault crosses the compressional compartment and generates seismicity of maximum magnitude over 6. The prevailing off-shore location of these faults has made the data acquisition slow and the definition of the geophysical and geological features of these structures quite problematic. We applied several hypocenter location algorithms to seismometric data collected in the study region by the national and local seismic networks in the last 25 years with the main purpose of improving the accuracy of the local fault detection. Clear improvement in the knowledge of the fault geometry has been obtained applying the Bayesian location method by Presti et al. (BSSA, 2004) to earthquake sequences and swarms recorded between 1978 and 2003. In our investigation of hypocenter locations, we also performed synthetic earthquake simulations to test the significance of the main hypocenter trends found, i.e. we established whether a seismolineament or cluster really reflects fault activity or is a fictitious product of the recording network geometry. The results have been evaluated in the light of the geophysical and geological information available in the literature for the study region.

  14. Planetary gearbox fault diagnosis using an adaptive stochastic resonance method

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Han, Dong; Lin, Jing; He, Zhengjia

    2013-07-01

    Planetary gearboxes are widely used in aerospace, automotive and heavy industry applications due to their large transmission ratio, strong load-bearing capacity and high transmission efficiency. The tough operation conditions of heavy duty and intensive impact load may cause gear tooth damage such as fatigue crack and teeth missed etc. The challenging issues in fault diagnosis of planetary gearboxes include selection of sensitive measurement locations, investigation of vibration transmission paths and weak feature extraction. One of them is how to effectively discover the weak characteristics from noisy signals of faulty components in planetary gearboxes. To address the issue in fault diagnosis of planetary gearboxes, an adaptive stochastic resonance (ASR) method is proposed in this paper. The ASR method utilizes the optimization ability of ant colony algorithms and adaptively realizes the optimal stochastic resonance system matching input signals. Using the ASR method, the noise may be weakened and weak characteristics highlighted, and therefore the faults can be diagnosed accurately. A planetary gearbox test rig is established and experiments with sun gear faults including a chipped tooth and a missing tooth are conducted. And the vibration signals are collected under the loaded condition and various motor speeds. The proposed method is used to process the collected signals and the results of feature extraction and fault diagnosis demonstrate its effectiveness.

  15. Unusual fault detection and loss analysis in optical fiber connections with refractive index matching material

    NASA Astrophysics Data System (ADS)

    Kihara, Mitsuru; Nagano, Ryuichiro; Izumita, Hisashi; Toyonaga, Masanobu

    2012-05-01

    We investigated and analyzed an unusual fault that occurs in optical access fiber networks, which is caused by a defective fiber connection. We developed a fault-detection system to locate such a fault by using both optical power level and optical pulse measurement methods. We investigated a defective mechanical splice in three laboratory tests: outward appearance, non-destructive, and dismantled. As a result, we confirmed that the defective mechanical splice had large gaps of more than 10 ?m. We also analyzed the unusual fault that occurs from such a defective mechanical splice in mechanically transferrable (MT) connector experiments. The experimental results revealed that the optical performance of fiber connections with a mixture of refractive index matching material and air-filled gaps was extremely unstable and varied widely. In the worst case, the insertion loss worsened to more than 30 dB. The case of the fault caused by a mixture of refractive index matching material and air-filled gaps between the ends of optical fibers is thought to occur independently of the sorts or structures of optical fiber connectors and could be a characteristic peculiar to optical fiber connections using refractive index matching material. These findings can be applied to optical fiber connections that use refractive index matching material, such as MT connectors in outside underground facilities, mechanical splices, or field assembly connectors at aerial and home sites in optical access networks. These findings also support the practical construction and operation of optical network systems.

  16. Understanding Vibration Spectra of Planetary Gear Systems for Fault Detection

    NASA Technical Reports Server (NTRS)

    Mosher, Marianne

    2003-01-01

    An understanding of the vibration spectra is very useful for any gear fault detection scheme based upon vibration measurements. The vibration measured from planetary gears is complicated. Sternfeld noted the presence of sidebands about the gear mesh harmonics spaced at the planet passage frequency in spectra measured near the ring gear of a CH-47 helicopter. McFadden proposes a simple model of the vibration transmission that predicts high spectral amplitudes at multiples of the planet passage frequency, for planetary gears with evenly spaced planets. This model correctly predicts no strong signal at the meshing frequency when the number of teeth on the ring gear is not an integer multiple of the number of planets. This paper will describe a model for planetary gear vibration spectra developed from the ideas started in reference. This model predicts vibration to occur only at frequencies that are multiples of the planet repetition passage frequency and clustered around gear mesh harmonics. Vibration measurements will be shown from tri-axial accelerometers mounted on three different planetary gear systems and compared with the model. The model correctly predicts the frequencies with large components around the first several gear mesh harmonics in measurements for systems with uniformly and nonuniformly spaced planet gears. Measurements do not confirm some of the more detailed features predicted by the model. Discrepancies of the ideal model to the measurements are believed due to simplifications in the model and will be discussed. Fault detection will be discussed applying the understanding will be discussed.

  17. Customized multiwavelets for planetary gearbox fault detection based on vibration sensor signals.

    PubMed

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

    2013-01-01

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

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

    PubMed Central

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

    2013-01-01

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

  19. FINDS: A fault inferring nonlinear detection system. User's guide

    NASA Technical Reports Server (NTRS)

    Lancraft, R. E.; Caglayan, A. K.

    1983-01-01

    The computer program FINDS is written in FORTRAN-77, and is intended for operation on a VAX 11-780 or 11-750 super minicomputer, using the VMS operating system. The program detects, isolates, and compensates for failures in navigation aid instruments and onboard flight control and navigation sensors of a Terminal Configured Vehicle aircraft in a Microwave Landing System environment. In addition, FINDS provides sensor fault tolerant estimates for the aircraft states which are then used by an automatic guidance and control system to land the aircraft along a prescribed path. FINDS monitors for failures by evaluating all sensor outputs simultaneously using the nonlinear analytic relationships between the various sensor outputs arising from the aircraft point mass equations of motion. Hence, FINDS is an integrated sensor failure detection and isolation system.

  20. Detection of Crosstalk Faults in Field Programmable Gate Arrays (FPGA)

    NASA Astrophysics Data System (ADS)

    Das, N.; Roy, P.; Rahaman, H.

    2015-09-01

    In this work, a Built-in-Self-Test (BIST) technique has been proposed to detect crosstalk faults in FPGA and run time congestion and to provide the crosstalk aware router for FPGA. The proposed BIST circuits require less overhead as compared to earlier techniques. The proposed detector can detect any logic hazard or delay due to crosstalk. A technique has also been proposed to avoid the crosstalk by routing the path in such a way that no interference occurs between the interconnects. The proposed router has achieved better utilization of routing resource to determine the net as compared to the earlier works. The proposed scheme is simulated in MATLAB and verified using Xilinx ISE tools and Modelsim 6.0. The router is implemented by using class provided by JBits for Xilinx, Vertex-II FPGA. It has been found that the results are quite encouraging.

  1. Fault detection and fault tolerant control of a smart base isolation system with magneto-rheological damper

    NASA Astrophysics Data System (ADS)

    Wang, Han; Song, Gangbing

    2011-08-01

    Fault detection and isolation (FDI) in real-time systems can provide early warnings for faulty sensors and actuator signals to prevent events that lead to catastrophic failures. The main objective of this paper is to develop FDI and fault tolerant control techniques for base isolation systems with magneto-rheological (MR) dampers. Thus, this paper presents a fixed-order FDI filter design procedure based on linear matrix inequalities (LMI). The necessary and sufficient conditions for the existence of a solution for detecting and isolating faults using the H_{\\infty } formulation is provided in the proposed filter design. Furthermore, an FDI-filter-based fuzzy fault tolerant controller (FFTC) for a base isolation structure model was designed to preserve the pre-specified performance of the system in the presence of various unknown faults. Simulation and experimental results demonstrated that the designed filter can successfully detect and isolate faults from displacement sensors and accelerometers while maintaining excellent performance of the base isolation technology under faulty conditions.

  2. 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

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

    PubMed

    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

  4. 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.

  5. Stationary wavelet transform for fault detection in rotating machinery

    NASA Astrophysics Data System (ADS)

    Seker, Serhat; Karatoprak, Erinc; Kayran, A. H.; Senguler, Tayfun

    2007-09-01

    This research presents a different fault diagnostic approach using the Stationary Wavelet Transform (SWT) as an alternative method to Discrete Wavelet Transform (DWT). In this sense, it is aimed to find potential defects, which exist in healthy motor bearings as manufacturing defects as compared to the faulty case. This approach extracts the origin of the bearing damage that develops during the aging process. In this manner, the advantage of the SWT over the DWT is emphasized. Hence, it can be introduced as a new approach for condition monitoring studies in rotating machineries like the induction motors.

  6. Fault detection of planetary gearboxes using new diagnostic parameters

    NASA Astrophysics Data System (ADS)

    Lei, Yaguo; Kong, Detong; Lin, Jing; Zuo, Ming J.

    2012-05-01

    Planetary gearboxes are commonly used in modern industry because of their large transmission ratio and strong load-bearing capacity. They generally work under heavy load and tough working environment and therefore their key components including sun gear, planet gears, ring gear, etc are subject to severe pitting and fatigue crack. Planetary gearboxes significantly differ from fixed-axis gearboxes and exhibit unique behavior, which invalidates the use of the diagnostic parameters developed and suitable for fixed-axis gearboxes. Therefore, there is a need to develop parameters specifically for detecting and diagnosing faults of planetary gearboxes. In this study, two diagnostic parameters are proposed based on the examination of the vibration characteristics of planetary gearboxes in both time and frequency domains. One is the root mean square of the filtered signal (FRMS) and the other is the normalized summation of positive amplitudes of the difference spectrum between the unknown signal and the healthy signal (NSDS). To test the proposed diagnostic parameters, we conducted experiments on a planetary gearbox test rig with sun gear faults including a cracked tooth and a pitted tooth. The vibration signals were measured under different motor speeds. The proposed parameters are compared with the existing parameters reported in the literature. The comparison results show the proposed diagnostic parameters perform better than others.

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

    NASA Technical Reports Server (NTRS)

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

    1997-01-01

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

  8. Fault analysis of space station dc power systems-using neutral network adaptive wavelets to detect faults

    NASA Astrophysics Data System (ADS)

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

    1997-01-01

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

  9. 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.

  10. 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,

  11. A Feature Extraction Method Based on Information Theory for Fault Diagnosis of Reciprocating Machinery

    PubMed Central

    Wang, Huaqing; Chen, Peng

    2009-01-01

    This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to. PMID:22574021

  12. A feature extraction method based on information theory for fault diagnosis of reciprocating machinery.

    PubMed

    Wang, Huaqing; Chen, Peng

    2009-01-01

    This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information waves is also derived, by which the feature spectrum can be extracted clearly and machine faults can be effectively differentiated. This paper also compares the proposed method with the conventional Hilbert-transform-based envelope detection and with a wavelet analysis technique. Practical examples of diagnosis for a rolling element bearing used in a diesel engine are provided to verify the effectiveness of the proposed method. The verification results show that the bearing faults that typically occur in rolling element bearings, such as outer-race, inner-race, and roller defects, can be effectively identified by the proposed method, while these bearing faults are difficult to detect using either of the other techniques it was compared to. PMID:22574021

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

    NASA Technical Reports Server (NTRS)

    He, Yuning

    2015-01-01

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

  14. Online Sensor Fault Detection Based on an Improved Strong Tracking Filter

    PubMed Central

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

    2015-01-01

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

  15. Online sensor fault detection based on an improved strong tracking filter.

    PubMed

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

    2015-01-01

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

  16. Using recurrence plot analysis for software execution interpretation and fault detection

    NASA Astrophysics Data System (ADS)

    Mosdorf, M.

    2015-09-01

    This paper shows a method targeted at software execution interpretation and fault detection using recurrence plot analysis. In in the proposed approach recurrence plot analysis is applied to software execution trace that contains executed assembly instructions. Results of this analysis are subject to further processing with PCA (Principal Component Analysis) method that simplifies number coefficients used for software execution classification. This method was used for the analysis of five algorithms: Bubble Sort, Quick Sort, Median Filter, FIR, SHA-1. Results show that some of the collected traces could be easily assigned to particular algorithms (logs from Bubble Sort and FIR algorithms) while others are more difficult to distinguish.

  17. Robust unknown input observer based fault detection for high-order multi-agent systems with disturbances.

    PubMed

    Liu, Xiuhua; Gao, Xianwen; Han, Jian

    2016-03-01

    This paper is devoted to fault detection (FD) for high-order multi-agent systems with disturbances. In order to detect the fault occurred in one agent, the unknown input observer (UIO) is constructed in its neighbor. Two cases are considered, if the perfect UI decoupling condition is satisfied, the UI does not affect the residual; if the condition is not satisfied, this paper proposes a method of partitioning the UI into two parts, such that a subset of the UI does not appear in residual dynamics, and the influence of the other UI is constrained. Simulations are given to demonstrate the effectiveness of the proposed method. PMID:26724972

  18. Method and system for fault accommodation of machines

    NASA Technical Reports Server (NTRS)

    Goebel, Kai Frank (Inventor); Subbu, Rajesh Venkat (Inventor); Rausch, Randal Thomas (Inventor); Frederick, Dean Kimball (Inventor)

    2011-01-01

    A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

  19. A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition

    PubMed Central

    Wang, Huaqing; Li, Ruitong; Tang, Gang; Yuan, Hongfang; Zhao, Qingliang; Cao, Xi

    2014-01-01

    A Compound fault signal usually contains multiple characteristic signals and strong confusion noise, which makes it difficult to separate week fault signals from them through conventional ways, such as FFT-based envelope detection, wavelet transform or empirical mode decomposition individually. In order to improve the compound faults diagnose of rolling bearings via signals’ separation, the present paper proposes a new method to identify compound faults from measured mixed-signals, which is based on ensemble empirical mode decomposition (EEMD) method and independent component analysis (ICA) technique. With the approach, a vibration signal is firstly decomposed into intrinsic mode functions (IMF) by EEMD method to obtain multichannel signals. Then, according to a cross correlation criterion, the corresponding IMF is selected as the input matrix of ICA. Finally, the compound faults can be separated effectively by executing ICA method, which makes the fault features more easily extracted and more clearly identified. Experimental results validate the effectiveness of the proposed method in compound fault separating, which works not only for the outer race defect, but also for the rollers defect and the unbalance fault of the experimental system. PMID:25289644

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

    NASA Technical Reports Server (NTRS)

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

    2009-01-01

    When setting out to model and/or simulate a complex mechanical or electrical system, a modeler is faced with a vast array of tools, software, equations, algorithms and techniques that may individually or in concert aid in the development of the model. Mature requirements and a well understood purpose for the model may considerably shrink the field of possible tools and algorithms that will suit the modeling solution. Is the model intended to be used in an offline fashion or in real-time? On what platform does it need to execute? How long will the model be allowed to run before it outputs the desired parameters? What resolution is desired? Do the parameters need to be qualitative or quantitative? Is it more important to capture the physics or the function of the system in the model? Does the model need to produce simulated data? All these questions and more will drive the selection of the appropriate tools and algorithms, but the modeler must be diligent to bear in mind the final application throughout the modeling process to ensure the model meets its requirements without needless iterations of the design. The purpose of this paper is to describe the considerations and techniques used in the process of creating a functional fault model of a liquid hydrogen (LH2) system that will be used in a real-time environment to automatically detect and isolate failures.

  1. 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

  2. Fault-Detection Tool Has Companies 'Mining' Own Business

    NASA Technical Reports Server (NTRS)

    2005-01-01

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

  3. Hidden Markov Models for Fault Detection in Dynamic Systems

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic

    1994-01-01

    Continuous monitoring of complex dynamic systems is an increasingly important issue in diverse areas such as nuclear plant safety, production line reliability, and medical health monitoring systems. Recent advances in both sensor technology and computational capabilities have made on-line permanent monitoring much more feasible than it was in the past. In this paper it is shown that a pattern recognition system combined with a finite-state hidden Markov model provides a particularly useful method for modelling temporal context in continuous monitoring. The parameters of the Markov model are derived from gross failure statistics such as the mean time between failures. The model is validated on a real-world fault diagnosis problem and it is shown that Markov modelling in this context offers significant practical benefits.

  4. Application of H-Infinity Fault Detection to Model-Scale Autonomous Aircraft

    NASA Astrophysics Data System (ADS)

    Vasconcelos, J. F.; Rosa, P.; Kerr, Murray; Latorre Sierra, Antonio; Recupero, Cristina; Hernandez, Lucia

    2015-09-01

    This paper describes the development of a fault detection system for a model scale autonomous aircraft. The considered fault scenario is defined by malfunctions in the elevator, namely bias and stuck-in-place of the surface. The H? design methodology is adopted, with an LFT description of the aircraft longitudinal dynamics, that allows for fault detection explicitly synthesized for a wide range of operating airspeeds. The obtained filter is validated in two stages: in a Functional Engineering Simulator (FES), providing preliminary results of the filter performance; and with experimental data, collected in field tests with actual injection of faults in the elevator surface.

  5. 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.

  6. 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

    2014-01-01

    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

  7. 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

  8. 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.

  9. A hybrid fault diagnosis method using morphological filter-translation invariant wavelet and improved ensemble empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Meng, Lingjie; Xiang, Jiawei; Wang, Yanxue; Jiang, Yongying; Gao, Haifeng

    2015-01-01

    Defective rolling bearing response is often characterized by the presence of periodic impulses, which are usually immersed in heavy noise. Therefore, a hybrid fault diagnosis approach is proposed. The morphological filter combining with translation invariant wavelet is taken as the pre-filter process unit to reduce the narrowband impulses and random noises in the original signal, then the purified signal will be decomposed by improved ensemble empirical mode decomposition (EEMD), in which a new selection method integrating autocorrelation analysis with the first two intrinsic mode functions (IMFs) having the maximum energies is put forward to eliminate the pseudo low-frequency components of IMFs. Applying the envelope analysis on those selected IMFs, the defect information is easily extracted. The proposed hybrid approach is evaluated by simulations and vibration signals of defective bearings with outer race fault, inner race fault, rolling element fault. Results show that the approach is feasible and effective for the fault detection of rolling bearing.

  10. Fault section detection system for 275-kV XLPE-insulated cables with optical sensing technique

    SciTech Connect

    Inoue, N.; Tsunekage, T.; Sakai, S.

    1995-07-01

    An on-line fault section detection system, which continuously monitors a 275-kV cross-linked polyethylene-insulated underground transmission line and instantaneously determines the section of fault at a ground fault, was developed. In case of a ground fault, the system detects the fault current which flows from the power source to the point of ground fault and determines the fault section from the magnitude and phase information of the fault current. Optical magnetic field sensors based on Faraday effect are used for detection of fault current, (5,000--50,000 A) or switching surges on signal transmission lines. The distance between an optical magnetic field sensor and the fault section detector/indicator can be as long as 10 km without the use of any repeaters.

  11. Rule based decision support system for single-line fault detection in a delta-delta connected distribution system

    SciTech Connect

    Momoh, J.A.; Dias, L.G.; Thor, T. . Dept. of Electrical Engineering); Laird, D. )

    1994-05-01

    Single-line fault detection, faulted feeder identification, fault type classification, fault location and fault impedance estimation, continue to pose a problem to delta-delta connected distribution systems such as the Los Angeles Department of Water and Power (LADWP) which has over 1,500 feeder circuits at the 4.8kV voltage level. This paper describes a rule based decision support (RBDS) system application to single-line fault detection in a delta-delta connected distribution system. The RBDS system is built from knowledge acquired through exhaustive simulation based on non-arcing type fault situations. It is primarily designed to detect the presence of a fault, identify the faulted feeder, the faulted phase and classify the fault type. It is also designed to gauge the proximity of the fault to the substation and to assess the fault impedance. A fault in the distribution system, upon identification, triggers an alarm with explanatory facility leading to the fault. The RBDS system was tested with different sets of simulated data and proved successful in most cases. Additional tests will be done using field data made available by LADWP. The RBDS system module is a prototype integrated fault detection scheme to be installed in a LADWP distribution substation.

  12. Hierarchical fault detection, isolation and recovery applied to cof and atv avionics

    NASA Astrophysics Data System (ADS)

    Durou, O.; Godet, V.; Mangane, L.; Pérarnaud, D.; Roques, R.

    2002-05-01

    The avionics architecture of in-orbit infrastructure elements is driven by safety. Safety of the crew inside the Columbus Orbital Facility (COF) laboratory module, safety of the Space Station as a whole for the automated transfer vehicle (ATV) when performing a rendezvous manoeuvre. The design answers on safety requirements, methods and tools used for the development stem from a common concept. The paper first describes the COF Data Management System architecture, basically organised in two layers with a strict hierarchical relationship. The vital layer is in charge of COF initial activation, safety supervision and emergency modes management. The nominal layer is a distributed system, organised around a local area Ethernet network. Under normal conditions, it is in charge of its own fault management supported by management agents distributed in the system. Fault detection criteria are derived from an FMECA (failure mode, effect and criticality analysis) and also from a SEEA (software error effect analysis). Recovery actions are allocated to various decision levels in the hierarchy depending on their time criticality. In ATV, the same principles apply, but the implementation is adapted to the peculiarities of an automated vehicle. The nominal layer, because of the time constraints bearing upon any reconfiguration, implements fault masking (majority voting) instead of fault detection and recovery.The vital layer is allocated the very critical task of monitoring the spacecraft attitude and velocity, and performing if necessary a collision avoidance manoeuvre. An end-to-end comprehensive methodology is put in place to be able to demonstrate the compliance of the systems to technical, product assurance and safety requirements.

  13. Fault Activity Investigations in the Lower Tagus Valley (Portugal) With Seismic and Geoelectric Methods

    NASA Astrophysics Data System (ADS)

    Carvalho, J. G.; Gonçalves, R.; Torres, L. M.; Cabral, J.; Mendes-Victor, L. A.

    2004-05-01

    The Lower Tagus River Valley is located in Central Portugal, and includes a large portion of the densely populated area of Lisbon. It is sited in the Lower Tagus Cenozoic Basin, a tectonic depression where up to 2,000 m of Cenozoic sediments are preserved, which was developed in the Neogene as a compressive foredeep basin related to tectonic inversion of former Mesozoic extensional structures. It is only a few hundred kilometers distant from the Eurasia-Africa plate boundary, and is characterized by a moderate seismicity presenting a diffuse pattern, with historical earthquakes having caused serious damage, loss of lives and economical problems. It has therefore been the target of several seismic hazard studies in which extensive geological and geophysical research was carried out on several geological structures. This work focuses on the application of seismic and geoelectric methods to investigate an important NW-SE trending normal fault detected on deep oil-industry seismic reflection profiles in the Tagus Cenozoic Basin. In these seismic sections this fault clearly offsets horizons that are ascribed to the Upper Miocene. However, due to the poor near surface resolution of the seismic data and the fact that the fault is hidden under the recent alluvial cover of the Tagus River, it was not clear whether it displaced the upper sediments of Holocene age. In order to constrain the fault geometry and kinematics and to evaluate its recent tectonic activity, a few high-resolution seismic reflection profiles were acquired and refraction interpretation of the reflection data was performed. Some vertical electrical soundings were also carried out. A complex fault system was detected, apparently with normal and reverse faulting. The collected data strongly supports the possibility that one of the detected faults affects the uppermost Neogene sediments and very probably the Holocene alluvial sediments of the Tagus River. The evidence of recent activity on this fault, its length (at least 10 km), location in an area with significant historical seismicity, and proximity to Lisbon and other small towns, all indicate that it represents a serious hazard to the study region and so should be considered in the regional seismic hazard evalution.

  14. 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.

  15. 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. PMID:16082787

  16. Implementation of a Fractional Model-Based Fault Detection Algorithm into a PLC Controller

    NASA Astrophysics Data System (ADS)

    Kopka, Ryszard

    2014-12-01

    This paper presents results related to the implementation of model-based fault detection and diagnosis procedures into a typical PLC controller. To construct the mathematical model and to implement the PID regulator, a non-integer order differential/integral calculation was used. Such an approach allows for more exact control of the process and more precise modelling. This is very crucial in model-based diagnostic methods. The theoretical results were verified on a real object in the form of a supercapacitor connected to a PLC controller by a dedicated electronic circuit controlled directly from the PLC outputs.

  17. Methods of Melanoma Detection.

    PubMed

    Leachman, Sancy A; Cassidy, Pamela B; Chen, Suephy C; Curiel, Clara; Geller, Alan; Gareau, Daniel; Pellacani, Giovanni; Grichnik, James M; Malvehy, Josep; North, Jeffrey; Jacques, Steven L; Petrie, Tracy; Puig, Susana; Swetter, Susan M; Tofte, Susan; Weinstock, Martin A

    2016-01-01

    Detection and removal of melanoma, before it has metastasized, dramatically improves prognosis and survival. The purpose of this chapter is to (1) summarize current methods of melanoma detection and (2) review state-of-the-art detection methods and technologies that have the potential to reduce melanoma mortality. Current strategies for the detection of melanoma range from population-based educational campaigns and screening to the use of algorithm-driven imaging technologies and performance of assays that identify markers of transformation. This chapter will begin by describing state-of-the-art methods for educating and increasing awareness of at-risk individuals and for performing comprehensive screening examinations. Standard and advanced photographic methods designed to improve reliability and reproducibility of the clinical examination will also be reviewed. Devices that magnify and/or enhance malignant features of individual melanocytic lesions (and algorithms that are available to interpret the results obtained from these devices) will be compared and contrasted. In vivo confocal microscopy and other cellular-level in vivo technologies will be compared to traditional tissue biopsy, and the role of a noninvasive "optical biopsy" in the clinical setting will be discussed. Finally, cellular and molecular methods that have been applied to the diagnosis of melanoma, such as comparative genomic hybridization (CGH), fluorescent in situ hybridization (FISH), and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR), will be discussed. PMID:26601859

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

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  19. 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.

  20. Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling.

    PubMed

    Li, Fan; Upadhyaya, Belle R; Coffey, Lonnie A

    2009-04-01

    This paper presents an incipient fault diagnosis approach based on the Group Method of Data Handling (GMDH) technique. The GMDH algorithm provides a generic framework for characterizing the interrelationships among a set of process variables of fossil power plant sub-systems and is employed to generate estimates of important variables in a data-driven fashion. In this paper, ridge regression techniques are incorporated into the ordinary least squares (OLS) estimator to solve regression coefficients at each layer of the GMDH network. The fault diagnosis method is applied to feedwater heater leak detection with data from an operating coal-fired plant. The results demonstrate the proposed method is capable of providing an early warning to operators when a process fault or an equipment fault occurs in a fossil power plant. PMID:19084227

  1. System for detecting and limiting electrical ground faults within electrical devices

    DOEpatents

    Gaubatz, Donald C. (Cupertino, CA)

    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.

  2. Development, Implementation, and Testing of Fault Detection Strategies on the National Wind Technology Center's Controls Advanced Research Turbines

    SciTech Connect

    Johnson, K. E.; Fleming, P. A.

    2011-06-01

    The National Renewable Energy Laboratory's National Wind Technology Center dedicates two 600 kW turbines for advanced control systems research. A fault detection system for both turbines has been developed, analyzed, and improved across years of experiments to protect the turbines as each new controller is tested. Analysis of field data and ongoing fault detection strategy improvements have resulted in a system of sensors, fault definitions, and detection strategies that have thus far been effective at protecting the turbines. In this paper, we document this fault detection system and provide field data illustrating its operation while detecting a range of failures. In some cases, we discuss the refinement process over time as fault detection strategies were improved. The purpose of this article is to share field experience obtained during the development and field testing of the existing fault detection system, and to offer a possible baseline for comparison with more advanced turbine fault detection controllers.

  3. 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.

  4. a New Online Distributed Process Fault Detection and Isolation Approach Using Potential Clustering Technique

    NASA Astrophysics Data System (ADS)

    Bahrampour, Soheil; Moshiri, Behzad; Salahshoor, Karim

    2009-08-01

    Most of process fault monitoring systems suffer from offline computations and confronting with novel faults that limit their applicabilities. This paper presents a new online fault detection and isolation (FDI) algorithm based on distributed online clustering approach. In the proposed approach, clustering algorithm is used for online detection of a new trend of time series data which indicates faulty condition. On the other hand, distributed technique is used to decompose the overall monitoring task into a series of local monitoring sub-tasks so as to locally track and capture the process faults. This algorithm not only solves the problem of online FDI, but also can handle novel faults. The diagnostic performances of the proposed FDI approach is evaluated on the Tennessee Eastman process plant as a large-scale benchmark problem.

  5. Diagnostic techniques and apparatus for detecting faults in perfluorocarbon liquid immersed transformers

    SciTech Connect

    Mizuno, K.; Ogawa, A.; Ooe, E.; Mori, E.

    1996-04-01

    This paper deals with techniques and an apparatus designed to diagnosis transformer faults by detecting C{sub 2}F{sub 4}, C{sub 2}F{sub 6} and C{sub 3}F{sub 6} gases contained in perfluorocarbon (PFC) liquid. The authors first established fault diagnostic techniques that employ gas patterns, gas composition ratios and fault diagnostic diagram and flow chart, based on the C{sub 2}F{sub 4}, C{sub 2}F{sub 6} and C{sub 3}F{sub 6} gases generated by overheating, partial discharges and arc discharges. Then, the authors verified the possibility of diagnosing internal faults in PFC liquid-immersed transformers when internal fault simulation tests on transformer model are conducted. The C{sub 2}F{sub 4} and C{sub 3}F{sub 6} gases generated there are detected with the gas diagnostic apparatus equipped with a gas sensor.

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

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

    SciTech Connect

    Zhao Jinsong Huang Jianchao; Sun Wei

    2008-11-15

    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 that 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.

  8. A Fault Alarm and Diagnosis Method Based on Sensitive Parameters and Support Vector Machine

    NASA Astrophysics Data System (ADS)

    Zhang, Jinjie; Yao, Ziyun; Lv, Zhiquan; Zhu, Qunxiong; Xu, Fengtian; Jiang, Zhinong

    2015-08-01

    Study on the extraction of fault feature and the diagnostic technique of reciprocating compressor is one of the hot research topics in the field of reciprocating machinery fault diagnosis at present. A large number of feature extraction and classification methods have been widely applied in the related research, but the practical fault alarm and the accuracy of diagnosis have not been effectively improved. Developing feature extraction and classification methods to meet the requirements of typical fault alarm and automatic diagnosis in practical engineering is urgent task. The typical mechanical faults of reciprocating compressor are presented in the paper, and the existing data of online monitoring system is used to extract fault feature parameters within 15 types in total; the inner sensitive connection between faults and the feature parameters has been made clear by using the distance evaluation technique, also sensitive characteristic parameters of different faults have been obtained. On this basis, a method based on fault feature parameters and support vector machine (SVM) is developed, which will be applied to practical fault diagnosis. A better ability of early fault warning has been proved by the experiment and the practical fault cases. Automatic classification by using the SVM to the data of fault alarm has obtained better diagnostic accuracy.

  9. Error detection method

    DOEpatents

    Olson, Eric J.

    2013-06-11

    An apparatus, program product, and method that run an algorithm on a hardware based processor, generate a hardware error as a result of running the algorithm, generate an algorithm output for the algorithm, compare the algorithm output to another output for the algorithm, and detect the hardware error from the comparison. The algorithm is designed to cause the hardware based processor to heat to a degree that increases the likelihood of hardware errors to manifest, and the hardware error is observable in the algorithm output. As such, electronic components may be sufficiently heated and/or sufficiently stressed to create better conditions for generating hardware errors, and the output of the algorithm may be compared at the end of the run to detect a hardware error that occurred anywhere during the run that may otherwise not be detected by traditional methodologies (e.g., due to cooling, insufficient heat and/or stress, etc.).

  10. Proposal for the Software Development of a Radar Power Fault Detection System

    NASA Astrophysics Data System (ADS)

    Jie-zhong, Zhu; Yong-lei, Yao; Su-ting, Chen

    A Smart Component is used in the setup of an expert Knowledge based Computer-aided Power fault detection system. The Smart Component will interpret test commands that a user will input into a database. When the fault detection workflow is finished, the complex task will be decomposed into several separate tasks. Professionals will then tackle these tasks. This will allow for division of labor, the testing system will be convenient and flexible, and a good result will be gotten in practice.

  11. 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.

  12. Detection of stator winding faults in induction machines using flux and vibration analysis

    NASA Astrophysics Data System (ADS)

    Lamim Filho, P. C. M.; Pederiva, R.; Brito, J. N.

    2014-01-01

    This work aims at presenting the detection and diagnosis of electrical faults in the stator winding of three-phase induction motors using magnetic flux and vibration analysis techniques. A relationship was established between the main electrical faults (inter-turn short circuits and unbalanced voltage supplies) and the signals of magnetic flux and vibration, in order to identify the characteristic frequencies of those faults. The experimental results showed the efficiency of the conjugation of these techniques for detection, diagnosis and monitoring tasks. The results were undoubtedly impressive and can be adapted and used in real predictive maintenance programs in industries.

  13. Fault detection and diagnosis of power converters using artificial neural networks

    SciTech Connect

    Swarup, K.S.; Chandrasekharaiah, H.S.

    1995-12-31

    Fault detection and diagnosis in real-time are areas of research interest in knowledge-based expert systems. Rule-based and model-based approaches have been successfully applied to some domains, but are too slow to be effectively applied in a real-time environment. This paper explores the suitability of using artificial neural networks for fault detection and diagnosis of power converter systems. The paper describes a neural network design and simulation environment for real-time fault diagnosis of thyristor converters used in HVDC power transmission systems.

  14. Airborne LiDAR detection of postglacial faults and Pulju moraine in Palojärvi, Finnish Lapland

    NASA Astrophysics Data System (ADS)

    Sutinen, Raimo; Hyvönen, Eija; Middleton, Maarit; Ruskeeniemi, Timo

    2014-04-01

    Postglacial faults (PGFs) are indicative of young tectonic activity providing crucial information for nuclear repository studies. Airborne LiDAR (Light Detection And Ranging) data revealed three previously unrecognized late- or postglacial faults in northernmost Finnish Lapland. Under the canopies of mountain birch (Betula pubescens ssp. czerepanovii) we also found clusters of the Pulju moraine, typically found on the ice-divide zone of the former Fennoscandian ice sheet (FIS), to be spatially associated with the fault-scarps. Tilt derivative (TDR) filtered LiDAR data revealed the previously unknown Palojärvi fault that, by the NE-SW orientation parallels with the well documented Lainio-Suijavaara PGF in northern Sweden. This suggests that PGFs are more extensive features than previously recognized. Two inclined diamond drill holes verified the fractured system of the Palojärvi fault and revealed clear signs of postglacial reactivation. Two other previously unrecognized PGFs, the W-E trending Paatsikkajoki fault and the SE-NW trending Kultima fault, differ from the Palojärvi faulting in orientation and possibly also with regard to age. The Pulju moraine, a morphological feature showing transitions from shallow (< 2-m-high) circular/arcuate ridges to sinusoidal/anastomosing esker networks was found to be concentrated within 6 km from the Kultima fault-scarp. We advocate that some of the past seismic events took place under the retreating wet-base ice sheet and the increased pore-water pressure triggered the sediment mass flows and formation of the Pulju moraine-esker landscape.

  15. Design of sensor and actuator multi model fault detection and isolation system using state space neural networks

    NASA Astrophysics Data System (ADS)

    Czajkowski, Andrzej

    2015-11-01

    This paper deals with the application of state space neural network model to design a Fault Detection and Isolation diagnostic system. The work describes approach based on multimodel solution where the SIMO process is decomposed into simple models (SISO and MISO). With such models it is possible to generate different residual signals which later can be evaluated with simple thresholding method into diagnostic signals. Further, such diagnostic signals with the application of Binary Diagnostic Table (BDT) can be used to fault isolation. All data used in experiments is obtain from the simulator of the real-time laboratory stand of Modular Servo under Matlab/Simulink environment.

  16. 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.

  17. Method for detecting biomolecules

    DOEpatents

    Huo, Qisheng; Liu, Jun

    2008-08-12

    A method for detecting and measuring the concentration of biomolecules in solution, utilizing a conducting electrode in contact with a solution containing target biomolecules, with a film with controllable pore size distribution characteristics applied to at least one surface of the conducting electrode. The film is functionalized with probe molecules that chemically interact with the target biomolecules at the film surface, blocking indicator molecules present in solution from diffusing from the solution to the electrode, thereby changing the electrochemical response of the electrode

  18. Double-Difference Tomography: Method and Application to the Hayward Fault and San Andreas Fault at Parkfield, California

    NASA Astrophysics Data System (ADS)

    Zhang, H.; Thurber, C.; Roberts, K.

    2002-12-01

    We have developed a double-difference seismic tomography method that makes use of both absolute and relative arrival times derived from the waveform cross-correlation technique and/or absolute catalog data. By reducing scatter in event locations using the more accurate relative arrival times, the method produces an improved (sharper) velocity model. Simultaneously, it yields event locations of a quality equivalent to those of the double-difference location method, while avoiding simplifying assumptions about ray path geometries or path anomalies. We test this method on both the Hayward Fault and San Andreas Fault at Parkfield, California. The Hayward fault dataset includes 1489 earthquakes with magnitudes from M0.2 to M4.5 occurring from 1984 to 1998. The earthquakes relocated by this method collapse to a thin line along the fault trace, forming a number of "streaks", consistent with previous results. The velocity model yields a sharper velocity contrast near the source region than the conventional tomography method with only absolute catalog data. For the test on the San Andreas Fault at Parkfield, we mainly use the data from a temporary seismic array known as PASO (Parkfield Area Seismic Observatory), which was installed in stages starting from July 2000. As expected, we also obtain a sharper velocity model and more concentrated event clusters than the conventional tomography method. We will investigate in detail the locations of earthquakes that are potential targets for SAFOD drilling.

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

    PubMed

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

    2012-01-01

    In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors. PMID:24808459

  20. Robust fault tolerant control based on sliding mode method for uncertain linear systems with quantization.

    PubMed

    Hao, Li-Ying; Yang, Guang-Hong

    2013-09-01

    This paper is concerned with the problem of robust fault-tolerant compensation control problem for uncertain linear systems subject to both state and input signal quantization. By incorporating novel matrix full-rank factorization technique with sliding surface design successfully, the total failure of certain actuators can be coped with, under a special actuator redundancy assumption. In order to compensate for quantization errors, an adjustment range of quantization sensitivity for a dynamic uniform quantizer is given through the flexible choices of design parameters. Comparing with the existing results, the derived inequality condition leads to the fault tolerance ability stronger and much wider scope of applicability. With a static adjustment policy of quantization sensitivity, an adaptive sliding mode controller is then designed to maintain the sliding mode, where the gain of the nonlinear unit vector term is updated automatically to compensate for the effects of actuator faults, quantization errors, exogenous disturbances and parameter uncertainties without the need for a fault detection and isolation (FDI) mechanism. Finally, the effectiveness of the proposed design method is illustrated via a model of a rocket fairing structural-acoustic. PMID:23701895

  1. Holocene paleoearthquakes of the Daqingshan fault detected from knickpoint identification and alluvial soil profile

    NASA Astrophysics Data System (ADS)

    He, Zhongtai; Ma, Baoqi

    2015-02-01

    Are there any effective methods to reveal paleoearthquakes on normal faults except traditional trenching technique? In this paper, we study Holocene paleoearthquakes of the Daqingshan fault which is a normal fault along the Daqingshan piedmont of Inner Mongolia in China. We identify knickpoints from stream profiles and study alluvial soil profiles to reconstruct the Holocene paleoearthquakes of the fault. From the fault's footwall we extract 25 gullies from IRS-P5 DEM data, and identify knickpoints in the profile that result from fault motion disturbing each channel. We combine the retreat distances and the knickpoint retreat rates to determine each knickpoint's forming time. We study alluvial fan outcrops that contain various paleosol sequences. As three distinct Holocene paleosols developed in the Daqingshan piedmont alluvial fans, we assume that the soil profile development was interrupted by fault activity preserved by interbedded gravel between the paleosols. The gravel layer between two adjacent paleosol layers represents material transported there after a paleoseismic event. Thus we date paleosol layers which are above and below the gravel layer to constrain paleoseismic events. Since trenches had been made by our predecessors along the fault to reveal the Holocene paleoearthquakes, we identify the Holocene paleoearthquake records from both sides of the fault, and then compare the results with the results from the trenches. The final result demonstrates that the knickpoints' sequence in the footwall and the paleosols' ages in the hanging wall correspond very closely with the Holocene paleoearthquakes along the Daqingshan piedmont fault. Methods in this paper have future application value to study paleoearthquakes on other normal faults with similar structure to the Daqingshan fault.

  2. 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.

  3. 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.

  4. Nucleic Acid Detection Methods

    DOEpatents

    Smith, Cassandra L. (Boston, MA); Yaar, Ron (Brookline, MA); Szafranski, Przemyslaw (Boston, MA); Cantor, Charles R. (Boston, MA)

    1998-05-19

    The invention relates to methods for rapidly determining the sequence and/or length a target sequence. The target sequence may be a series of known or unknown repeat sequences which are hybridized to an array of probes. The hybridized array is digested with a single-strand nuclease and free 3'-hydroxyl groups extended with a nucleic acid polymerase. Nuclease cleaved heteroduplexes can be easily distinguish from nuclease uncleaved heteroduplexes by differential labeling. Probes and target can be differentially labeled with detectable labels. Matched target can be detected by cleaving resulting loops from the hybridized target and creating free 3-hydroxyl groups. These groups are recognized and extended by polymerases added into the reaction system which also adds or releases one label into solution. Analysis of the resulting products using either solid phase or solution. These methods can be used to detect characteristic nucleic acid sequences, to determine target sequence and to screen for genetic defects and disorders. Assays can be conducted on solid surfaces allowing for multiple reactions to be conducted in parallel and, if desired, automated.

  5. Nucleic acid detection methods

    DOEpatents

    Smith, C.L.; Yaar, R.; Szafranski, P.; Cantor, C.R.

    1998-05-19

    The invention relates to methods for rapidly determining the sequence and/or length a target sequence. The target sequence may be a series of known or unknown repeat sequences which are hybridized to an array of probes. The hybridized array is digested with a single-strand nuclease and free 3{prime}-hydroxyl groups extended with a nucleic acid polymerase. Nuclease cleaved heteroduplexes can be easily distinguish from nuclease uncleaved heteroduplexes by differential labeling. Probes and target can be differentially labeled with detectable labels. Matched target can be detected by cleaving resulting loops from the hybridized target and creating free 3-hydroxyl groups. These groups are recognized and extended by polymerases added into the reaction system which also adds or releases one label into solution. Analysis of the resulting products using either solid phase or solution. These methods can be used to detect characteristic nucleic acid sequences, to determine target sequence and to screen for genetic defects and disorders. Assays can be conducted on solid surfaces allowing for multiple reactions to be conducted in parallel and, if desired, automated. 18 figs.

  6. A Fuzzy Reasoning Design for Fault Detection and Diagnosis of a Computer-Controlled System

    PubMed Central

    Ting, Y.; Lu, W.B.; Chen, C.H.; Wang, G.K.

    2008-01-01

    A Fuzzy Reasoning and Verification Petri Nets (FRVPNs) model is established for an error detection and diagnosis mechanism (EDDM) applied to a complex fault-tolerant PC-controlled system. The inference accuracy can be improved through the hierarchical design of a two-level fuzzy rule decision tree (FRDT) and a Petri nets (PNs) technique to transform the fuzzy rule into the FRVPNs model. Several simulation examples of the assumed failure events were carried out by using the FRVPNs and the Mamdani fuzzy method with MATLAB tools. The reasoning performance of the developed FRVPNs was verified by comparing the inference outcome to that of the Mamdani method. Both methods result in the same conclusions. Thus, the present study demonstratrates that the proposed FRVPNs model is able to achieve the purpose of reasoning, and furthermore, determining of the failure event of the monitored application program. PMID:19255619

  7. Fault detection and isolation of aircraft air data/inertial system

    NASA Astrophysics Data System (ADS)

    Berdjag, D.; Cieslak, J.; Zolghadri, A.

    2013-12-01

    A method for failure detection and isolation (FDI) for redundant aircraft sensors is presented. The outputs of the concerned sensors are involved in the computation of flight control laws, and the objective is to eliminate any fault before propagation in the control loop when selecting a unique flight parameter among a set (generally, three) of redundant measurements. The particular case of an oscillatory failure is investigated. The proposed method allows an accurate FDI of erroneous sensor and computes a consolidated parameter based on the fusion of data from remaining valid sensors. The benefits of the presented method are to enhance the data fusion process with FDI techniques which improve the performance of the fusion when only few sources (less than three) are initially valid.

  8. Faults simulations for three-dimensional reservoir-geomechanical models with the extended finite element method

    NASA Astrophysics Data System (ADS)

    Prévost, Jean H.; Sukumar, N.

    2016-01-01

    Faults are geological entities with thicknesses several orders of magnitude smaller than the grid blocks typically used to discretize reservoir and/or over-under-burden geological formations. Introducing faults in a complex reservoir and/or geomechanical mesh therefore poses significant meshing difficulties. In this paper, we consider the strong-coupling of solid displacement and fluid pressure in a three-dimensional poro-mechanical (reservoir-geomechanical) model. We introduce faults in the mesh without meshing them explicitly, by using the extended finite element method (X-FEM) in which the nodes whose basis function support intersects the fault are enriched within the framework of partition of unity. For the geomechanics, the fault is treated as an internal displacement discontinuity that allows slipping to occur using a Mohr-Coulomb type criterion. For the reservoir, the fault is either an internal fluid flow conduit that allows fluid flow in the fault as well as to enter/leave the fault or is a barrier to flow (sealing fault). For internal fluid flow conduits, the continuous fluid pressure approximation admits a discontinuity in its normal derivative across the fault, whereas for an impermeable fault, the pressure approximation is discontinuous across the fault. Equal-order displacement and pressure approximations are used. Two- and three-dimensional benchmark computations are presented to verify the accuracy of the approach, and simulations are presented that reveal the influence of the rate of loading on the activation of faults.

  9. H∞ mode-dependent fault detection filter design for stochastic Markovian jump systems with time-varying delays and parameter uncertainties.

    PubMed

    Zhuang, Guangming; Xia, Jianwei; Chu, Yuming; Chen, Fu

    2014-07-01

    This paper deals with the problem of robust H∞ fault detection for a class of stochastic Markovian jump systems (SMJSs) The aim is to design a linear mode-dependent fault detection filter such that the fault detection system is not only stochastically asymptotically stable in the large, but also satisfies a prescribed H∞-norm level for all admissible uncertainties. By using Lyapunov stability theory and generalized Itô formula, some novel mode-dependent and delay-dependent sufficient conditions in terms of linear matrix inequality (LMI) are proposed to insure the existence of the desired fault detection filter. A simulation example and an industrial nonisothermal continuous stirred tank reactor (CSTR) system are employed to show the effectiveness of the proposed method. PMID:24929630

  10. Statistic-based spectral indicator for bearing fault detection in permanent-magnet synchronous machines using the stator current

    NASA Astrophysics Data System (ADS)

    Picot, A.; Obeid, Z.; Régnier, J.; Poignant, S.; Darnis, O.; Maussion, P.

    2014-06-01

    In this paper, an original method for bearing fault detection in high speed synchronous machines is presented. This method is based on the statistical process of Welch's periodogram of the stator currents in order to obtain stable and normalized fault indicators. The principle of the method is to statistically compare the current spectrum to a healthy reference so as to quantify the changes over the time. A statistic-based indicator is then constructed by monitoring specific harmonic family. The proposed method was tested on two experimental test campaigns for four different speeds and compared to a vibration indicator. The method was evaluated using a rigorous performance evaluation metric. A threshold evaluation was performed and shows that the proposed method is very tolerant to the machine speed. Thus, the use of a unique fault threshold whatever the speed can be considered. Results showed excellent agreement as compared with the vibration indicator, with an overall correlation of r=0.74 and only 4% of false alarms. Performance demonstrated by this novel method was superior to those of a classical energy-based indicator in terms of correlation with the vibration indicator and detection stability. Moreover, results also showed a better robustness of the proposed method since good performance can be obtained with the same detection threshold whatever the speed or the measure campaign whereas it needs to be redefined for each case with the classical indicator. This work shows the advantages of a statistic-based approach in order to increase the robustness of bearing fault detection in permanent-magnet synchronous machines.

  11. A universal, fault-tolerant, non-linear analytic network for modeling and fault detection

    SciTech Connect

    Mott, J.E.; King, R.W.; Monson, L.R.; Olson, D.L.; Staffon, J.D.

    1992-03-06

    The similarities and differences of a universal network to normal neural networks are outlined. The description and application of a universal network is discussed by showing how a simple linear system is modeled by normal techniques and by universal network techniques. A full implementation of the universal network as universal process modeling software on a dedicated computer system at EBR-II is described and example results are presented. It is concluded that the universal network provides different feature recognition capabilities than a neural network and that the universal network can provide extremely fast, accurate, and fault-tolerant estimation, validation, and replacement of signals in a real system.

  12. Detection of frictional heat in seismic faults by coal reflectance

    NASA Astrophysics Data System (ADS)

    Kitamura, M.; Mukoyoshi, H.; Fulton, P. M.; Hirose, T.

    2012-12-01

    Quantitative assessment of heat generation along a fault during coseismic faulting is of primary importance in understanding the dynamics of earthquakes. Evidence of substantial frictional heating along a fault is also a reliable indicator determining whether a fault has slipped at high velocity in the past, which is crucial for assessing earthquake and tsunami hazard. The reflectance measurement of vitrinite (one of the primary components of coals) has been considered a possible geothermometer of fault zones, especially in accretionary wedges where vitrinite fragments are common [e.g., Sakaguchi et al., 2011]. Under normal burial conditions, vitrinite reflectance (Ro) increases by irreversible maturation reaction as temperature is elevated and thus sensitively records the maximum temperature to which the vitrinite is subjected. However, the commonly used kinetic models of vitrinite maturation [e.g., Sweeney and Burnham, 1990] may not yield accurate estimates of the peak temperature in a fault zone resulting from fast frictional heating rates [Fulton and Harris, 2012]. Whether or not coal can mature in typical earthquake rise time (e.g., ~10 seconds) remains uncertain. Here we present the results of friction experiments aimed at revealing coal maturation by frictional heat generated at slip velocities representative of natural earthquakes of up to 1.3 m/s. All friction experiments were conducted on a mixture of 90 wt% quartz powder and 10 wt% coal grains for simulated fault gouge at three different velocities of 0.0013 m/s, 0.65 m/s and 1.3 m/s, a constant normal stress of 1.0 MPa and ~15 m displacement under anoxic, dry nitrogen atmosphere at room temperature. We also measured temperature in the gouge zone during faulting by thermocouples. The initial coal fragments consist of vitrinite, inertinite and liptinite. Although liptinite was easy to identify microscopically, it was difficult to discriminate between vitrinite and inertinite grains as their grain size became less than 50 ?m. Thus we measured reflectance (R) of all vitrinite and inertinite grains in this study. The reflectance increases from ~1.3 to ~5.9% during an experiment where temperature reached 266 °C in 11 seconds and ~15 m of displacement. Even with a temperature rise to only 28.7 °C over 15 m displacement in ~3 hours, R values of some coal grains, particularly in the shear localized zone, change from 1.3% to >2.0%. The commonly used kinetic model of vitrinite maturation cannot predict the experimental results. In addition to frictional heat, the observed increase in R values likely result from a combination of two possible effects: flash temperature at asperity contacts between quartz and coal grains and mechanochemical reaction associated with deformation increasing defect density in particles and greatly enhancing the reaction kinetics. A kinetic model involving such mechanical effects is necessary to better estimate heat generation along a fault.

  13. 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.

  14. Fault Detection in the Blade and Pitch System of a Wind Turbine with H2 PI Observers

    NASA Astrophysics Data System (ADS)

    Sales-Setién, Ester; Peñarrocha, Ignacio; Dolz, Daniel; Sanchis, Roberto

    2015-11-01

    In this work, we present a fault detection strategy applicable to the blade and pitch system in offshore wind turbines. First, we model the system and possible faults and propose a PI observer to identify the faults. Then, the observer is designed accounting the sensors measurement noise, and addressing a trade off between the needs of false alarm rate, minimum detectable fault and detection time. By means of a well known benchmark, several simulations show the goodness of the approach and its flexibility to explicitly fix the fault detector performance.

  15. New insight into the detection of high-impedance arcing faults on DC trolley systems

    SciTech Connect

    Li, J.; Kohler, J.L.

    1999-10-01

    High-impedance arcing faults are difficult to detect with conventional switchgear, and the presence of these faults in coal mine power systems represents a significant fire hazard. Research was performed to identify plausible techniques that would discriminate between the high-impedance arcing faults and legitimate load currents on the dc trolley system. This paper briefly summarizes that effort and focuses on the frequency characteristics of the arc current. After the arc was modeled as a stochastic process, good agreement was obtained between experimental observations and mathematical predictions.

  16. 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.

  17. 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.

  18. Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays.

    PubMed

    Zheng, Ying; Fang, Huajing; Wang, Hua O

    2006-08-01

    A Takagi-Sugeno (T-S) model is employed to represent a networked control system (NCS) with different network-induced delays. Comparing with existing NCS modeling methods, this approach does not require the knowledge of exact values of network-induced delays. Instead, it addresses situations involving all possible network-induced delays. Moreover, this approach also handles data-packet loss. As an application of the T-S-based modeling method, a parity-equation approach and a fuzzy-observer-based approach for fault detection of an NCS were developed. An example of a two-link inverted pendulum is used to illustrate the utility and viability of the proposed approaches. PMID:16903375

  19. Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds.

    PubMed

    Zhao, Ming; Lin, Jing; Xu, Xiaoqiang; Lei, Yaguo

    2013-01-01

    Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of rolling element bearings. Conventional diagnostic methods are based on the stationary assumption, thus they are not applicable to the diagnosis of bearings working under varying speed. This constraint limits the bearing diagnosis to the industrial application significantly. In order to extend the conventional diagnostic methods to speed variation cases, a tacholess envelope order analysis technique is proposed in this paper. In the proposed technique, a tacholess order tracking (TLOT) method is first introduced to extract the tachometer information from the vibration signal itself. On this basis, an envelope order spectrum (EOS) is utilized to recover the bearing characteristic frequencies in the order domain. By combining the advantages of TLOT and EOS, the proposed technique is capable of detecting bearing faults under varying speeds, even without the use of a tachometer. The effectiveness of the proposed method is demonstrated by both simulated signals and real vibration signals collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Analyzed results show that the proposed method could identify different bearing faults effectively and accurately under speed varying conditions. PMID:23959244

  20. Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method

    NASA Astrophysics Data System (ADS)

    Li, Wei; Zhu, Zhencai; Jiang, Fan; Zhou, Gongbo; Chen, Guoan

    2015-01-01

    Fault diagnosis of rotating machinery is receiving more and more attentions. Vibration signals of rotating machinery are commonly analyzed to extract features of faults, and the features are identified with classifiers, e.g. artificial neural networks (ANNs) and support vector machines (SVMs). Due to nonlinear behaviors and unknown noises in machinery, the extracted features are varying from sample to sample, which may result in false classifications. It is also difficult to analytically ensure the accuracy of fault diagnosis. In this paper, a feature extraction and evaluation method is proposed for fault diagnosis of rotating machinery. Based on the central limit theory, an extraction procedure is given to obtain the statistical features with the help of existing signal processing tools. The obtained statistical features approximately obey normal distributions. They can significantly improve the performance of fault classification, and it is verified by taking ANN and SVM classifiers as examples. Then the statistical features are evaluated with a decoupling technique and compared with thresholds to make the decision on fault classification. The proposed evaluation method only requires simple algebraic computation, and the accuracy of fault classification can be analytically guaranteed in terms of the so-called false classification rate (FCR). An experiment is carried out to verify the effectiveness of the proposed method, where the unbalanced fault of rotor, inner race fault, outer race fault and ball fault of bearings are considered.

  1. Applications of pattern recognition techniques to online fault detection

    SciTech Connect

    Singer, R.M.; Gross, K.C.; King, R.W.

    1993-11-01

    A common problem to operators of complex industrial systems is the early detection of incipient degradation of sensors and components in order to avoid unplanned outages, to orderly plan for anticipated maintenance activities and to assure continued safe operation. In such systems, there usually are a large number of sensors (upwards of several thousand is not uncommon) serving many functions, ranging from input to control systems, monitoring of safety parameters and component performance limits, system environmental conditions, etc. Although sensors deemed to measure important process conditions are generally alarmed, the alarm set points usually are just high-low limits and the operator`s response to such alarms is based on written procedures and his or her experience and training. In many systems this approach has been successful, but in situations where the cost of a forced outage is high an improved method is needed. In such cases it is desirable, if not necessary, to detect disturbances in either sensors or the process prior to any actual failure that could either shut down the process or challenge any safety system that is present. Recent advances in various artificial intelligence techniques have provided the opportunity to perform such functions of early detection and diagnosis. In this paper, the experience gained through the application of several pattern-recognition techniques to the on-line monitoring and incipient disturbance detection of several coolant pumps and numerous sensors at the Experimental Breeder Reactor-II (EBR-II) which is located at the Idaho National Engineering Laboratory is presented.

  2. [Application of optimized parameters SVM based on photoacoustic spectroscopy method in fault diagnosis of power transformer].

    PubMed

    Zhang, Yu-xin; Cheng, Zhi-feng; Xu, Zheng-ping; Bai, Jing

    2015-01-01

    In order to solve the problems such as complex operation, consumption for the carrier gas and long test period in traditional power transformer fault diagnosis approach based on dissolved gas analysis (DGA), this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH4, C2H2, C2H4, C2H6 and H2 based on photoacoustic Spectroscopy and C2H2/C2H4, CH4/H2, C2H4/C2H6 three-ratios data are calculated. The support vector machine model was constructed using cross validation method under five support vector machine functions and four kernel functions, heuristic algorithms were used in parameter optimization for penalty factor c and g, which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed. Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization. The simulation result shows that SVM model composed of C-SVC, RBF kernel functions and genetic algorithm obtain 97. 5% accuracy in test sample set and 98. 333 3% accuracy in train sample set, and genetic algorithm was about two times faster than particles swarm optimization in computing speed. The methods described in this paper has many advantages such as simple operation, non-contact measurement, no consumption for the carrier gas, long test period, high stability and sensitivity, the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual project needs in transformer fault diagnosis. PMID:25993810

  3. Waveguide disturbance detection method

    DOEpatents

    Korneev, Valeri A. (Albany, CA); Nihei, Kurt T. (Oakland, CA); Myer, Larry R. (Benicia, CA)

    2000-01-01

    A method for detection of a disturbance in a waveguide comprising transmitting a wavefield having symmetric and antisymmetric components from a horizontally and/or vertically polarized source and/or pressure source disposed symmetrically with respect to the longitudinal central axis of the waveguide at one end of the waveguide, recording the horizontal and/or vertical component or a pressure of the wavefield with a vertical array of receivers disposed at the opposite end of the waveguide, separating the wavenumber transform of the wavefield into the symmetric and antisymmetric components, integrating the symmetric and antisymmetric components over a broad frequency range, and comparing the magnitude of the symmetric components and the antisymmetric components to an expected magnitude for the symmetric components and the antisymmetric components for a waveguide of uniform thickness and properties thereby determining whether or not a disturbance is present inside the waveguide.

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

    NASA Astrophysics Data System (ADS)

    Sari, Endah Puspita; Subakti, Hendri

    2015-04-01

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

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

    PubMed

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

    2009-01-01

    This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented. PMID:22400008

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

    PubMed Central

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

    2009-01-01

    This paper presents a method to increase the reliability of Unmanned Aerial Vehicle (UAV) sensor Fault Detection and Identification (FDI) in a multi-UAV context. Differential Global Positioning System (DGPS) and inertial sensors are used for sensor FDI in each UAV. The method uses additional position estimations that augment individual UAV FDI system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factors, dynamic replanning of the multi-UAV team can be used to obtain a better estimation in case of faults caused by slow growing errors of absolute position estimation that cannot be detected by using local FDI in the UAVs. Experimental results with data from two real UAVs are also presented. PMID:22400008

  7. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection

    PubMed Central

    Liu, Zhiwen; Guo, Wei; Tang, Zhangchun; Chen, Yongqiang

    2015-01-01

    Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM) based on an ant colony optimization algorithm (ACO-RVM) for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM). RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD) is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET) is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV). PMID:26334280

  8. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection.

    PubMed

    Liu, Zhiwen; Guo, Wei; Tang, Zhangchun; Chen, Yongqiang

    2015-01-01

    Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM) based on an ant colony optimization algorithm (ACO-RVM) for gearboxes' fault detection. RVM is a sparse probability model based on support vector machine (SVM). RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD) is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET) is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV). PMID:26334280

  9. A method for generating volumetric fault zone grids for pillar gridded reservoir models

    NASA Astrophysics Data System (ADS)

    Qu, Dongfang; Røe, Per; Tveranger, Jan

    2015-08-01

    The internal structure and petrophysical property distribution of fault zones are commonly exceedingly complex compared to the surrounding host rock from which they are derived. This in turn produces highly complex fluid flow patterns which affect petroleum migration and trapping as well as reservoir behavior during production and injection. Detailed rendering and forecasting of fluid flow inside fault zones require high-resolution, explicit models of fault zone structure and properties. A fundamental requirement for achieving this is the ability to create volumetric grids in which modeling of fault zone structures and properties can be performed. Answering this need, a method for generating volumetric fault zone grids which can be seamlessly integrated into existing standard reservoir modeling tools is presented. The algorithm has been tested on a wide range of fault configurations of varying complexity, providing flexible modeling grids which in turn can be populated with fault zone structures and properties.

  10. SOM Neural Network Fault Diagnosis Method of Polymerization Kettle Equipment Optimized by Improved PSO Algorithm

    PubMed Central

    Wang, Jie-sheng; Li, Shu-xia; Gao, Jie

    2014-01-01

    For meeting the real-time fault diagnosis and the optimization monitoring requirements of the polymerization kettle in the polyvinyl chloride resin (PVC) production process, a fault diagnosis strategy based on the self-organizing map (SOM) neural network is proposed. Firstly, a mapping between the polymerization process data and the fault pattern is established by analyzing the production technology of polymerization kettle equipment. The particle swarm optimization (PSO) algorithm with a new dynamical adjustment method of inertial weights is adopted to optimize the structural parameters of SOM neural network. The fault pattern classification of the polymerization kettle equipment is to realize the nonlinear mapping from symptom set to fault set according to the given symptom set. Finally, the simulation experiments of fault diagnosis are conducted by combining with the industrial on-site historical data of the polymerization kettle and the simulation results show that the proposed PSO-SOM fault diagnosis strategy is effective. PMID:25152929

  11. Detection of stator winding faults in induction motors using three-phase current monitoring.

    PubMed

    Sharifi, Rasool; Ebrahimi, Mohammad

    2011-01-01

    The objective of this paper is to propose a new method for the detection of inter-turn short circuits in the stator windings of induction motors. In the previous reported methods, the supply voltage unbalance was the major difficulty, and this was solved mostly based on the sequence component impedance or current which are difficult to implement. Some other methods essentially are included in the offline methods. The proposed method is based on the motor current signature analysis and utilizes three phase current spectra to overcome the mentioned problem. Simulation results indicate that under healthy conditions, the rotor slot harmonics have the same magnitude in three phase currents, while under even 1 turn (0.3%) short circuit condition they differ from each other. Although the magnitude of these harmonics depends on the level of unbalanced voltage, they have the same magnitude in three phases in these conditions. Experiments performed under various load, fault, and supply voltage conditions validate the simulation results and demonstrate the effectiveness of the proposed technique. It is shown that the detection of resistive slight short circuits, without sensitivity to supply voltage unbalance is possible. PMID:21074767

  12. Tuning and comparing fault diagnosis methods for aeronautical systems via kriging-based optimization

    NASA Astrophysics Data System (ADS)

    Marzat, J.; Piet-Lahanier, H.; Damongeot, F.; Walter, E.

    2013-12-01

    Many approaches address fault detection and isolation (FDI) based on analytical redundancy. To rank them, it is necessary to define performance indices and realistic sets of test cases on which they will be evaluated. For the ranking to be fair, each of the methods under consideration should have its internal parameters tuned optimally. The work presented uses a combination of tools developed in the context of computer experiments to achieve this tuning from a limited number of numerical evaluations. The methodology is then extended so as to provide a robust tuning in the worst-case sense.

  13. Statistical Fault Detection for Parallel Applications with AutomaDeD

    SciTech Connect

    Bronevetsky, G; Laguna, I; Bagchi, S; de Supinski, B R; Ahn, D; Schulz, M

    2010-03-23

    Today's largest systems have over 100,000 cores, with million-core systems expected over the next few years. The large component count means that these systems fail frequently and often in very complex ways, making them difficult to use and maintain. While prior work on fault detection and diagnosis has focused on faults that significantly reduce system functionality, the wide variety of failure modes in modern systems makes them likely to fail in complex ways that impair system performance but are difficult to detect and diagnose. This paper presents AutomaDeD, a statistical tool that models the timing behavior of each application task and tracks its behavior to identify any abnormalities. If any are observed, AutomaDeD can immediately detect them and report to the system administrator the task where the problem began. This identification of the fault's initial manifestation can provide administrators with valuable insight into the fault's root causes, making it significantly easier and cheaper for them to understand and repair it. Our experimental evaluation shows that AutomaDeD detects a wide range of faults immediately after they occur 80% of the time, with a low false-positive rate. Further, it identifies weaknesses of the current approach that motivate future research.

  14. A consensus-based multi-agent approach for estimation in robust fault detection.

    PubMed

    Jiang, Yulian; Liu, Jianchang; Wang, Shenquan

    2014-09-01

    This paper is devoted to distributed estimation in robust fault detection for sensor networks with networked-induced delays and packet dropouts by using a consensus-based multi-agent approach. Utilizing the information interaction and coordination among the neighboring networks based on multi-agent theory, we design novel and multiple agent-based robust fault detection filters (RFDFs) subject to only partial estimated and measured information. Asymptotically stable sufficient conditions for the innovative constructed filters are derived in the form of linear matrix inequality (LMI) and the threshold fit for each agent-based RFDF is determined. An illustrative example is given to demonstrate the effectiveness of the consensus-based multi-agent approach for the estimation in robust fault detection. PMID:24962935

  15. Early detection of incipient faults in power plants using accelerated neural network learning

    SciTech Connect

    Parlos, A.G.; Jayakumar, M. ); Atiya, A. )

    1992-01-01

    An important aspect of power plant automation is the development of computer systems able to detect and isolate incipient (slowly developing) faults at the earliest possible stages of their occurrence. In this paper, the development and testing of such a fault detection scheme is presented based on recognition of sensor signatures during various failure modes. An accelerated learning algorithm, namely adaptive backpropagation (ABP), has been developed that allows the training of a multilayer perceptron (MLP) network to a high degree of accuracy, with an order of magnitude improvement in convergence speed. An artificial neural network (ANN) has been successfully trained using the ABP algorithm, and it has been extensively tested with simulated data to detect and classify incipient faults of various types and severity and in the presence of varying sensor noise levels.

  16. Bispectrum of stator phase current for fault detection of induction motor.

    PubMed

    Treetrong, Juggrapong; Sinha, Jyoti K; Gu, Fengshu; Ball, Andrew

    2009-07-01

    A number of research studies has shown that faults in a stator or rotor generally show sideband frequencies around the mains frequency (50 Hz) and at higher harmonics in the spectrum of the Motor Current Signature Analysis (MCSA). However in the present experimental studies such observations have not been seen, but any fault either in the stator or the rotor may distort the sinusoidal response of the motor RPM and the mains frequency so the MCSA response may contain a number of harmonics of the motor RPM and the mains frequency. Hence the use of a higher order spectrum (HOS), namely the bispectrum of the MCSA has been proposed here because it relates both amplitude and phase of number of the harmonics in a signal. It has been observed that it not only detects early faults but also indicates the severity of the fault to some extent. PMID:19394927

  17. 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

  18. 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

  19. Fault and dyke detectability in high resolution seismic surveys for coal: a view from numerical modelling*

    NASA Astrophysics Data System (ADS)

    Zhou, Binzhong 13Hatherly, Peter

    2014-10-01

    Modern underground coal mining requires certainty about geological faults, dykes and other structural features. Faults with throws of even just a few metres can create safety issues and lead to costly delays in mine production. In this paper, we use numerical modelling in an ideal, noise-free environment with homogeneous layering to investigate the detectability of small faults by seismic reflection surveying. If the layering is horizontal, faults with throws of 1/8 of the wavelength should be detectable in a 2D survey. In a coal mining setting where the seismic velocity of the overburden ranges from 3000 m/s to 4000 m/s and the dominant seismic frequency is ~100 Hz, this corresponds to a fault with a throw of 4-5 m. However, if the layers are dipping or folded, the faults may be more difficult to detect, especially when their throws oppose the trend of the background structure. In the case of 3D seismic surveying we suggest that faults with throws as small as 1/16 of wavelength (2-2.5 m) can be detectable because of the benefits offered by computer-aided horizon identification and the improved spatial coherence in 3D seismic surveys. With dykes, we find that Berkhout's definition of the Fresnel zone is more consistent with actual experience. At a depth of 500 m, which is typically encountered in coal mining, and a 100 Hz dominant seismic frequency, dykes less than 8 m in width are undetectable, even after migration.

  20. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    Subsidence has been a common occurrence in several cities in central Mexico for the past three decades. This process causes substantial damage to the urban infrastructure and housing in several cities and it is a major factor to be considered when planning urban development, land-use zoning and hazard mitigation strategies. Since the early 1980's the city of Morelia in Central Mexico has experienced subsidence associated with groundwater extraction in excess of natural recharge from rainfall. Previous works have focused on the detection and temporal evolution of the subsidence spatial distribution. The most recent InSAR analysis confirms the permanence of previously detected rapidly subsiding areas such as the Rio Grande Meander area and also defines 2 subsidence patches previously undetected in the newly developed suburban sectors west of Morelia at the Fraccionamiento Del Bosque along, south of Hwy. 15 and another patch located north of Morelia along Gabino Castañeda del Rio Ave. Because subsidence-induced, shallow faulting develops at high horizontal strain localization, newly developed a subsidence areas are particularly prone to faulting and fissuring. Shallow faulting increases groundwater vulnerability because it disrupts discharge hydraulic infrastructure and creates a direct path for transport of surface pollutants into the underlying aquifer. Other sectors in Morelia that have been experiencing subsidence for longer time have already developed well defined faults such as La Colina, Central Camionera, Torremolinos and La Paloma faults. Local construction codes in the vicinity of these faults define a very narrow swath along which housing construction is not allowed. In order to better characterize these fault systems and provide better criteria for future municipal construction codes we have surveyed the La Colina and Torremolinos fault systems in the western sector of Morelia using seismic tomographic techniques. Our results indicate that La Colina Fault include secondary faults at depths up to 4-8m below the surface and located up to 24m away from the main fault trace. The Torremolinos fault system includes secondary faults, which are present up to 8m deep and 12-18m away from the main fault trace. Even though the InSAR analysis provides an unsurpassed synoptic view, a higher temporal resolution observation of fault movement has been pursued using the MOIT continuously operating GPS station, which is located within 100 m from the La Colina main fault trace. GPS data is also particularly useful to decompose horizontal and vertical motion in the absence of both ascending and descending SAR data acquisitions. Observations since July 2009 show a total general displacement trend of -39mm/yr and a total horizontal differential motion of 41.8 mm/yr and -4.7mm/yr in its latitudinal and Longitudinal components respectively in respect to the motion observed at the MOGA GPS station located 5.0 km to the SSE within an area which is not affected by subsidence. In addition to the overall trend, high amplitude excursions at the MOIT station with individual residual amplitudes up to 20mm, 25mm, and 60mm in its latitudinal, longitudinal and vertical components respectively vertical are observed. The correlation of fault motion excursions in relationship to the rainfall records will be analyzed.

  2. Geophysical imaging of near subsurface layers to detect fault and fractured zones in the Tournemire Experimental Platform, France.

    NASA Astrophysics Data System (ADS)

    Nhu Ba, Elise, Vi; Noble, Mark; Gélis, Céline; Gesret, Alexandrine; Cabrera, Justo

    2013-04-01

    IRSN (the French Institute for Radiological Protection and Nuclear Safety) is in charge of the expertise of the safety report of the French deep geological disposal site project in the East of France. With the goal of understanding the various transport and mechanical properties of clay-rocks, IRSN has conducted several research programs at the Tournemire Experimental Platform (TEP, in the Department of Aveyron in the South of France). Three major sub-horizontal layers characterize the sedimentary Jurassic formations of the TEP. At the base of the stratigraphic column, we find a sequence of limestones and dolomites, that is overlain by an argillaceous formation composed of a 250 m thick clay-rock layer. Above this layer, there is another sequence of limestones and dolomites. The TEP is characterized by a 2 km long tunnel, which allows in situ access to the Toarcian clay- rock layer. In addition to the main Cernon fault, secondary fault zones affect the clay-rock formation and have been observed in the galleries and also identified in several underground boreholes. These sub-vertical fault zones or fracture network display mainly subhorizontal offset (decametric scale) and a small vertical one (meter scale). In the upper limestone, these fault zones widen and fracturing becomes more scattered. In an attempt to detect fault zones in clay-rock layers such as the one described above, IRSN carried out in 2001 a 3D high-resolution seismic survey from the surface in collaboration with CGG. A sub-vertical fault was successfully picked out by the seismic data at the interface between the clay-rock formation and the underlying limestones. This fault is interpreted as the downward continuation of one of the fault zones identified in the tunnel. However, because of the weak seismic impedance contrast in the clay-rock layer and the small vertical offset of sub-vertical fault zones, these fault zones could not be identified in the clay-rock formation. No fault or fracture zone could either be detected in the upper limestone formation because of the acquisition geometry. In order to better image the clay-rock and upper limestone layers, IRSN, Mines ParisTech and UPPA conducted large-scale 2D and 3D very high-resolution seismic surveys in 2010 and 2011 from the surface in the framework of the GNR TRASSE. We analyze this new dataset with the first arrival traveltime tomography method in order to assess its potential to detect fault and fracture zones in near subsurface layers. For this purpose, we develop a new fast inversion algorithm that allows introducing a priori information and choosing a specific model parameterization. We validate our approach based on the Simultaneous Iterative Reconstruction Technique with synthetic data and present the first results of the new real dataset processing. We finally compare these results to a 2D high-resolution electrical resistivity profile acquired at the same location. These electrical resistivity data could also be considered as some a priori information in our inversion scheme.

  3. The Marshall Space Flight Center Fault Detection Diagnosis and Recovery Laboratory

    NASA Technical Reports Server (NTRS)

    Burchett, Bradley T.; Gamble, Jonathan; Rabban, Michael

    2008-01-01

    The Fault Detection Diagnosis and Recovery Lab (FDDR) has been developed to support development of,fault detection algorithms for the flight computer aboard the Ares I and follow-on vehicles. It consists of several workstations using Ethernet and TCP/IP to simulate communications between vehicle sensors, flight computers, and ground based support computers. Isolation of tasks between workstations was set up intentionally to limit information flow and provide a realistic simulation of communication channels within the vehicle and between the vehicle and ground station.

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

    NASA Astrophysics Data System (ADS)

    Schlechtingen, Meik; Ferreira Santos, Ilmar

    2011-07-01

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

  5. Optical cable fault locating using Brillouin optical time domain reflectometer and cable localized heating method

    NASA Astrophysics Data System (ADS)

    Lu, Y. G.; Zhang, X. P.; Dong, Y. M.; Wang, F.; Liu, Y. H.

    2007-07-01

    A novel optical cable fault location method, which is based on Brillouin optical time domain reflectometer (BOTDR) and cable localized heating, is proposed and demonstrated. In the method, a BOTDR apparatus is used to measure the optical loss and strain distribution along the fiber in an optical cable, and a heating device is used to heat the cable at its certain local site. Actual experimental results make it clear that the proposed method works effectively without complicated calculation. By means of the new method, we have successfully located the optical cable fault in the 60 km optical fiber composite power cable from Shanghai to Shengshi, Zhejiang. A fault location accuracy of 1 meter was achieved. The fault location uncertainty of the new optical cable fault location method is at least one order of magnitude smaller than that of the traditional OTDR method.

  6. Validation Methods for Fault-Tolerant avionics and control systems, working group meeting 1

    NASA Technical Reports Server (NTRS)

    1979-01-01

    The proceedings of the first working group meeting on validation methods for fault tolerant computer design are presented. The state of the art in fault tolerant computer validation was examined in order to provide a framework for future discussions concerning research issues for the validation of fault tolerant avionics and flight control systems. The development of positions concerning critical aspects of the validation process are given.

  7. To err is robotic, to tolerate immunological: fault detection in multirobot systems.

    PubMed

    Tarapore, Danesh; Lima, Pedro U; Carneiro, Jorge; Christensen, Anders Lyhne

    2015-01-01

    Fault detection and fault tolerance represent two of the most important and largely unsolved issues in the field of multirobot systems (MRS). Efficient, long-term operation requires an accurate, timely detection, and accommodation of abnormally behaving robots. Most existing approaches to fault-tolerance prescribe a characterization of normal robot behaviours, and train a model to recognize these behaviours. Behaviours unrecognized by the model are consequently labelled abnormal or faulty. MRS employing these models do not transition well to scenarios involving temporal variations in behaviour (e.g., online learning of new behaviours, or in response to environment perturbations). The vertebrate immune system is a complex distributed system capable of learning to tolerate the organism's tissues even when they change during puberty or metamorphosis, and to mount specific responses to invading pathogens, all without the need of a genetically hardwired characterization of normality. We present a generic abnormality detection approach based on a model of the adaptive immune system, and evaluate the approach in a swarm of robots. Our results reveal the robust detection of abnormal robots simulating common electro-mechanical and software faults, irrespective of temporal changes in swarm behaviour. Abnormality detection is shown to be scalable in terms of the number of robots in the swarm, and in terms of the size of the behaviour classification space. PMID:25642825

  8. 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.

  9. 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.

  10. Regional methods for mapping major faults in areas of uniform low relief, as used in the London Basin, UK

    NASA Astrophysics Data System (ADS)

    Haslam, Richard; Aldiss, Donald

    2013-04-01

    Most of the London Basin, south-eastern UK, is underlain by the Palaeogene London Clay Formation, comprising a succession of rather uniform marine clay deposits up to 150 m thick, with widespread cover of Quaternary deposits and urban development. Therefore, in this area faults are difficult to delineate (or to detect) by conventional geological surveying methods in the field, and few are shown on the geological maps of the area. However, boreholes and excavations, especially those for civil engineering works, indicate that faults are probably widespread and numerous in the London area. A representative map of fault distribution and patterns of displacement is a pre-requisite for understanding the tectonic development of a region. Moreover, faulting is an important influence on the design and execution of civil engineering works, and on the hydrogeological characteristics of the ground. This paper reviews methods currently being used to map faults in the London Basin area. These are: the interpretation of persistent scatterer interferometry (PSI) data from time-series satellite-borne radar measurements; the interpretation of regional geophysical fields (Bouguer gravity anomaly and aeromagnetic), especially in combination with a digital elevation model; and the construction and interpretation of 3D geological models. Although these methods are generally not as accurate as large-scale geological field surveys, due to the availability of appropriate data in the London Basin they provide the means to recognise and delineate more faults, and with more confidence, than was possible using traditional geological mapping techniques. Together they reveal regional structures arising during Palaeogene crustal extension and subsidence in the North Sea, followed by inversion of a Mesozoic sedimentary basin in the south of the region, probably modified by strike-slip fault motion associated with the relative northward movement of the African Plate and the Alpine orogeny. This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an NERC copyright. This license does not conflict with the regulations of the Crown Copyright.

  11. WETLAND DETECTION METHODS INVESTIGATION

    EPA Science Inventory

    The purpose of this investigation was to research and document the application of remote sensing technology for wetlands detection. arious sensors and platforms are evaluated for: suitability to monitor specific wetland systems; effectiveness of detailing wetland extent and capab...

  12. Dynamic rupture simulations on complex fault zone structures with off-fault plasticity using the ADER-DG method

    NASA Astrophysics Data System (ADS)

    Wollherr, Stephanie; Gabriel, Alice-Agnes; Igel, Heiner

    2015-04-01

    In dynamic rupture models, high stress concentrations at rupture fronts have to to be accommodated by off-fault inelastic processes such as plastic deformation. As presented in (Roten et al., 2014), incorporating plastic yielding can significantly reduce earlier predictions of ground motions in the Los Angeles Basin. Further, an inelastic response of materials surrounding a fault potentially has a strong impact on surface displacement and is therefore a key aspect in understanding the triggering of tsunamis through floor uplifting. We present an implementation of off-fault-plasticity and its verification for the software package SeisSol, an arbitrary high-order derivative discontinuous Galerkin (ADER-DG) method. The software recently reached multi-petaflop/s performance on some of the largest supercomputers worldwide and was a Gordon Bell prize finalist application in 2014 (Heinecke et al., 2014). For the nonelastic calculations we impose a Drucker-Prager yield criterion in shear stress with a viscous regularization following (Andrews, 2005). It permits the smooth relaxation of high stress concentrations induced in the dynamic rupture process. We verify the implementation by comparison to the SCEC/USGS Spontaneous Rupture Code Verification Benchmarks. The results of test problem TPV13 with a 60-degree dipping normal fault show that SeisSol is in good accordance with other codes. Additionally we aim to explore the numerical characteristics of the off-fault plasticity implementation by performing convergence tests for the 2D code. The ADER-DG method is especially suited for complex geometries by using unstructured tetrahedral meshes. Local adaptation of the mesh resolution enables a fine sampling of the cohesive zone on the fault while simultaneously satisfying the dispersion requirements of wave propagation away from the fault. In this context we will investigate the influence of off-fault-plasticity on geometrically complex fault zone structures like subduction zones or branched faults. Studying the interplay of stress conditions and angle dependence of neighbouring branches including inelastic material behaviour and its effects on rupture jumps and seismic activation helps to advance our understanding of earthquake source processes. An application is the simulation of a real large-scale subduction zone scenario including plasticity to validate the coupling of our dynamic rupture calculations to a tsunami model in the framework of the ASCETE project (http://www.ascete.de/). Andrews, D. J. (2005): Rupture dynamics with energy loss outside the slip zone, J. Geophys. Res., 110, B01307. Heinecke, A. (2014), A. Breuer, S. Rettenberger, M. Bader, A.-A. Gabriel, C. Pelties, A. Bode, W. Barth, K. Vaidyanathan, M. Smelyanskiy and P. Dubey: Petascale High Order Dynamic Rupture Earthquake Simulations on Heterogeneous Supercomputers. In Supercomputing 2014, The International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, New Orleans, LA, USA, November 2014. Roten, D. (2014), K. B. Olsen, S.M. Day, Y. Cui, and D. Fäh: Expected seismic shaking in Los Angeles reduced by San Andreas fault zone plasticity, Geophys. Res. Lett., 41, 2769-2777.

  13. Combined expert system/neural networks method for process fault diagnosis

    DOEpatents

    Reifman, J.; Wei, T.Y.C.

    1995-08-15

    A two-level hierarchical approach for process fault diagnosis of an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach. 9 figs.

  14. Combined expert system/neural networks method for process fault diagnosis

    DOEpatents

    Reifman, Jaques; Wei, Thomas Y. C.

    1995-01-01

    A two-level hierarchical approach for process fault diagnosis is an operating system employs a function-oriented approach at a first level and a component characteristic-oriented approach at a second level, where the decision-making procedure is structured in order of decreasing intelligence with increasing precision. At the first level, the diagnostic method is general and has knowledge of the overall process including a wide variety of plant transients and the functional behavior of the process components. An expert system classifies malfunctions by function to narrow the diagnostic focus to a particular set of possible faulty components that could be responsible for the detected functional misbehavior of the operating system. At the second level, the diagnostic method limits its scope to component malfunctions, using more detailed knowledge of component characteristics. Trained artificial neural networks are used to further narrow the diagnosis and to uniquely identify the faulty component by classifying the abnormal condition data as a failure of one of the hypothesized components through component characteristics. Once an anomaly is detected, the hierarchical structure is used to successively narrow the diagnostic focus from a function misbehavior, i.e., a function oriented approach, until the fault can be determined, i.e., a component characteristic-oriented approach.

  15. Fault feature extraction of rolling bearing based on an improved cyclical spectrum density method

    NASA Astrophysics Data System (ADS)

    Li, Min; Yang, Jianhong; Wang, Xiaojing

    2015-07-01

    The traditional cyclical spectrum density(CSD) method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recognizing bearing fault type is difficult. Therefore, a new CSD method based on kurtosis(CSDK) is proposed. The kurtosis value of each cyclic frequency is used to measure the modulation capability of cyclic frequency. When the kurtosis value is large, the modulation capability is strong. Thus, the kurtosis value is regarded as the weight coefficient to accumulate all cyclic frequencies to extract fault features. Compared with the traditional method, CSDK can reduce the interference of harmonic frequency in fault frequency, which makes fault characteristics distinct from background noise. To validate the effectiveness of the method, experiments are performed on the simulation signal, the fault signal of the bearing outer race in the test bed, and the signal gathered from the bearing of the blast furnace belt cylinder. Experimental results show that the CSDK is better than the resonance demodulation method and the CSD in extracting fault features and recognizing degradation trends. The proposed method provides a new solution to fault diagnosis in bearings.

  16. Fault feature extraction of rolling bearing based on an improved cyclical spectrum density method

    NASA Astrophysics Data System (ADS)

    Li, Min; Yang, Jianhong; Wang, Xiaojing

    2015-11-01

    The traditional cyclical spectrum density(CSD) method is widely used to analyze the fault signals of rolling bearing. All modulation frequencies are demodulated in the cyclic frequency spectrum. Consequently, recognizing bearing fault type is difficult. Therefore, a new CSD method based on kurtosis(CSDK) is proposed. The kurtosis value of each cyclic frequency is used to measure the modulation capability of cyclic frequency. When the kurtosis value is large, the modulation capability is strong. Thus, the kurtosis value is regarded as the weight coefficient to accumulate all cyclic frequencies to extract fault features. Compared with the traditional method, CSDK can reduce the interference of harmonic frequency in fault frequency, which makes fault characteristics distinct from background noise. To validate the effectiveness of the method, experiments are performed on the simulation signal, the fault signal of the bearing outer race in the test bed, and the signal gathered from the bearing of the blast furnace belt cylinder. Experimental results show that the CSDK is better than the resonance demodulation method and the CSD in extracting fault features and recognizing degradation trends. The proposed method provides a new solution to fault diagnosis in bearings.

  17. Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system

    NASA Astrophysics Data System (ADS)

    Wang, Yanxue; Markert, Richard; Xiang, Jiawei; Zheng, Weiguang

    2015-08-01

    Multi-component extraction is an available method for vibration signal analysis of rotary machinery, so a novel method of rubbing fault diagnosis based on variational mode decomposition (VMD) is proposed. VMD is a newly developed technique for adaptive signal decomposition, which can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions. The equivalent filtering characteristics of VMD are investigated, and the behavior of wavelet packet-like expansion is first found based on fractional Gaussian noise via numerical simulations. VMD is then applied to detect multiple rubbing-caused signatures for rotor-stator fault diagnosis via numerical simulated response signal and practical vibration signal. A comparison has also been conducted to evaluate the effectiveness of identifying the rubbing-caused signatures by using VMD, empirical wavelet transform (EWT), EEMD and EMD. The analysis results of the rubbing signals show that the multiple features can be better extracted with the VMD, simultaneously.

  18. Fault detection in railway track using piezoelectric impedance

    NASA Astrophysics Data System (ADS)

    Cremins, M.; Shuai, Qi; Xu, Jiawen; Tang, J.

    2014-04-01

    In this research, piezoelectric transducers are incorporated in an impedance-based damage detection approach for railway track health monitoring. The impedance-based damage detection approach utilizes the direct relationship between the mechanical impedance of the track and electrical impedance of the piezoelectric transducer bonded. The effect of damage is shown in the change of a healthy impedance curve to an altered, damaged curve. Using a normalized relative difference outlier analysis, the occurrences of various damages on the track are determined. Furthermore, the integration of inductive circuitry with the piezoelectric transducer is found to be able to considerably increase overall damage detection sensitivity.

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

    PubMed Central

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

    2012-01-01

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

  20. Development of a fault diagnosis method for heating systems using neural networks

    SciTech Connect

    Li, X.; Vaezi-Nejad, H.; Visier, J.C.

    1996-11-01

    The application of artificial neural networks (ANNs) for developing a fault diagnosis (FD) method in complex heating systems is presented in this paper. The six operating modes with faults used to develop this FD method came from the results of a detailed investigation in cooperation with heating system maintenance experts and are among the most important operating faults for this type of system. Because a daily diagnosis is generally sufficient, the ANNs have been developed using the daily values obtained by a preprocessing of the numerical simulation data. This paper presents the first step of the method development. It demonstrates the feasibility of using ANNs for fault diagnosis of a specific heating, ventilating, and air-conditioning (HVAC) system provided training data representative of the behavior of the system with and without faults are available. The next step will consist of developing a generic method that requires less training data.

  1. Remote sensing analysis for fault-zones detection in the Central Andean Plateau (Catamarca, Argentina)

    NASA Astrophysics Data System (ADS)

    Traforti, Anna; Massironi, Matteo; Zampieri, Dario; Carli, Cristian

    2015-04-01

    Remote sensing techniques have been extensively used to detect the structural framework of investigated areas, which includes lineaments, fault zones and fracture patterns. The identification of these features is fundamental in exploration geology, as it allows the definition of suitable sites for the exploitation of different resources (e.g. ore mineral, hydrocarbon, geothermal energy and groundwater). Remote sensing techniques, typically adopted in fault identification, have been applied to assess the geological and structural framework of the Laguna Blanca area (26°35'S-66°49'W). This area represents a sector of the south-central Andes localized in the Argentina region of Catamarca, along the south-eastern margin of the Puna plateau. The study area is characterized by a Precambrian low-grade metamorphic basement intruded by Ordovician granitoids. These rocks are unconformably covered by a volcano-sedimentary sequence of Miocene age, followed by volcanic and volcaniclastic rocks of Upper Miocene to Plio-Pleistocene age. All these units are cut by two systems of major faults, locally characterized by 15-20 m wide damage zones. The detection of main tectonic lineaments in the study area was firstly carried out by classical procedures: image sharpening of Landsat 7 ETM+ images, directional filters applied to ASTER images, medium resolution Digital Elevation Models analysis (SRTM and ASTER GDEM) and hill shades interpretation. In addition, a new approach in fault zone identification, based on multispectral satellite images classification, has been tested in the Laguna Blanca area and in other sectors of south-central Andes. In this perspective, several prominent fault zones affecting basement and granitoid rocks have been sampled. The collected fault gouge samples have been analyzed with a Field-Pro spectrophotometer mounted on a goniometer. We acquired bidirectional reflectance spectra, from 0.35μm to 2.5μm with 1nm spectral sampling, of the sampled fault rocks. Subsequently, two different Spectral Angle Mapper (SAM) classifications were applied to ASTER images: the first one based on fault rock spectral signatures resampled at the ASTER sensor resolution; the second one based on spectral signatures retrieved from specific Region of Interest (ROI), which were directly derived from the ASTER image on the analyzed fault zones. The SAM classification based on the spectral signatures of fault rocks gave outstanding results since it was able to classify the analyzed fault zone, both in terms of length and width. Moreover, in some specific cases, this SAM classification identified not only the sampled fault zone, but also other prominent neighboring faults cutting the same host rock. These results define the SAM supervised classification on ASTER images as a tool to identify prominent fault zones directly on the base of fault-rocks spectra.

  2. 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.

  3. Final Technical Report Recovery Act: Online Nonintrusive Condition Monitoring and Fault Detection for Wind Turbines

    SciTech Connect

    Wei Qiao

    2012-05-29

    The penetration of wind power has increased greatly over the last decade in the United States and across the world. The U.S. wind power industry installed 1,118 MW of new capacity in the first quarter of 2011 alone and entered the second quarter with another 5,600 MW under construction. By 2030, wind energy is expected to provide 20% of the U.S. electricity needs. As the number of wind turbines continues to grow, the need for effective condition monitoring and fault detection (CMFD) systems becomes increasingly important [3]. Online CMFD is an effective means of not only improving the reliability, capacity factor, and lifetime, but it also reduces the downtime, energy loss, and operation and maintenance (O&M) of wind turbines. The goal of this project is to develop novel online nonintrusive CMFD technologies for wind turbines. The proposed technologies use only the current measurements that have been used by the control and protection system of a wind turbine generator (WTG); no additional sensors or data acquisition devices are needed. Current signals are reliable and easily accessible from the ground without intruding on the wind turbine generators (WTGs) that are situated on high towers and installed in remote areas. Therefore, current-based CMFD techniques have great economic benefits and the potential to be adopted by the wind energy industry. Specifically, the following objectives and results have been achieved in this project: (1) Analyzed the effects of faults in a WTG on the generator currents of the WTG operating at variable rotating speed conditions from the perspective of amplitude and frequency modulations of the current measurements; (2) Developed effective amplitude and frequency demodulation methods for appropriate signal conditioning of the current measurements to improve the accuracy and reliability of wind turbine CMFD; (3) Developed a 1P-invariant power spectrum density (PSD) method for effective signature extraction of wind turbine faults with characteristic frequencies in the current or current demodulated signals, where 1P stands for the shaft rotating frequency of a WTG; (4) Developed a wavelet filter for effective signature extraction of wind turbine faults without characteristic frequencies in the current or current demodulated signals; (5) Developed an effective adaptive noise cancellation method as an alternative to the wavelet filter method for signature extraction of wind turbine faults without characteristic frequencies in the current or current demodulated signals; (6) Developed a statistical analysis-based impulse detection method for effective fault signature extraction and evaluation of WTGs based on the 1P-invariant PSD of the current or current demodulated signals; (7) Validated the proposed current-based wind turbine CMFD technologies through extensive computer simulations and experiments for small direct-drive WTGs without gearboxes; and (8) Showed, through extensive experiments for small direct-drive WTGs, that the performance of the proposed current-based wind turbine CMFD technologies is comparable to traditional vibration-based methods. The proposed technologies have been successfully applied for detection of major failures in blades, shafts, bearings, and generators of small direct-drive WTGs. The proposed technologies can be easily integrated into existing wind turbine control, protection, and monitoring systems and can be implemented remotely from the wind turbines being monitored. The proposed technologies provide an alternative to vibration-sensor-based CMFD. This will reduce the cost and hardware complexity of wind turbine CMFD systems. The proposed technologies can also be combined with vibration-sensor-based methods to improve the accuracy and reliability of wind turbine CMFD systems. When there are problems with sensors, the proposed technologies will ensure proper CMFD for the wind turbines, including their sensing systems. In conclusion, the proposed technologies offer an effective means to achieve condition-based smart maintenance for wind turbines and have a great potential to be adopted by the wind energy industry due to their almost no-cost, nonintrusive features. Although only validated for small direct-drive wind turbines without gearboxes, the proposed technologies are also applicable for CMFD of large-size wind turbines with and without gearboxes. However, additional investigations are recommended in order to apply the proposed technologies to those large-size wind turbines.

  4. Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope order analysis.

    PubMed

    Zhao, Ming; Lin, Jing; Xu, Xiaoqiang; Li, Xuejun

    2014-01-01

    When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks. PMID:25353982

  5. Multi-Fault Detection of Rolling Element Bearings under Harsh Working Condition Using IMF-Based Adaptive Envelope Order Analysis

    PubMed Central

    Zhao, Ming; Lin, Jing; Xu, Xiaoqiang; Li, Xuejun

    2014-01-01

    When operating under harsh condition (e.g., time-varying speed and load, large shocks), the vibration signals of rolling element bearings are always manifested as low signal noise ratio, non-stationary statistical parameters, which cause difficulties for current diagnostic methods. As such, an IMF-based adaptive envelope order analysis (IMF-AEOA) is proposed for bearing fault detection under such conditions. This approach is established through combining the ensemble empirical mode decomposition (EEMD), envelope order tracking and fault sensitive analysis. In this scheme, EEMD provides an effective way to adaptively decompose the raw vibration signal into IMFs with different frequency bands. The envelope order tracking is further employed to transform the envelope of each IMF to angular domain to eliminate the spectral smearing induced by speed variation, which makes the bearing characteristic frequencies more clear and discernible in the envelope order spectrum. Finally, a fault sensitive matrix is established to select the optimal IMF containing the richest diagnostic information for final decision making. The effectiveness of IMF-AEOA is validated by simulated signal and experimental data from locomotive bearings. The result shows that IMF-AEOA could accurately identify both single and multiple faults of bearing even under time-varying rotating speed and large extraneous shocks. PMID:25353982

  6. Ability of High-Resolution Resistivity Tomography to Detect Fault and Fracture Zones: Application to the Tournemire Experimental Platform, France

    NASA Astrophysics Data System (ADS)

    Gélis, C.; Noble, M.; Cabrera, J.; Penz, S.; Chauris, H.; Cushing, E. M.

    2016-02-01

    The Experimental Platform of Tournemire (Aveyron, France) developed by IRSN (French Institute for Radiological Protection and Nuclear Safety) is composed of a tunnel excavated in an argillite formation belonging to a limestone-argillite-limestone subhorizontal sedimentary sequence. Subvertical secondary fault zones were intercepted in argillite using drifts and boreholes in the tunnel excavated at a depth of about 250 m located under the Larzac Plateau. A 2D 2.5 km baseline large-scale electrical resistivity survey conducted in 2007 allowed detecting in the upper limestones several significantly low electrical resistivity subvertical zones (G élis et al. Appl Geophys 167(11): 1405-1418, 2010). One of these discontinuities is consistent with the extension towards the surface of the secondary fault zones identified in the argillite formation from the tunnel. In an attempt to better characterize this zone, IRSN and MINES ParisTech conducted a high-resolution electrical resistivity survey located transversally to the fault and fracture zones. A 760-m-long profile was acquired using two array geometries and take-outs of 2, 4 and 8 m, requiring several roll-alongs. These data were first inverted independently for each take-out and then using all take-outs together for a given array geometry. Different inverted 2D electrical resistivity models display the same global features with high (higher than 5000 Ωm) to low (lower than 100 Ωm) electrical resistivity zones. These electrical resistivity models are finally compared with a geological cross-section based on independent data. The subvertical conductive zones are in agreement with the fault and fracture locations inferred from the geological cross-section. Moreover, the top of a more conductive zone, below a high electrical conductive zone and between two subvertical fault zones, is located in a more sandy and argillaceous layer. This conductive zone is interpreted as the presence of a more scattered fracture zone located at depth between two fault zones. This zone could be correlated with the fractured zones identified at 250-m depth in underground works. This study highlights the interest of multi-scale approaches to image complex heterogeneous near subsurface layers. Finally, this study shows that the electrical resistivity tomography is a useful and powerful tool to detect fault and fracture zones in upper limestones. Such a method is complementary to other geophysical and geological data.

  7. Ability of High-Resolution Resistivity Tomography to Detect Fault and Fracture Zones: Application to the Tournemire Experimental Platform, France

    NASA Astrophysics Data System (ADS)

    Gélis, C.; Noble, M.; Cabrera, J.; Penz, S.; Chauris, H.; Cushing, E. M.

    2015-05-01

    The Experimental Platform of Tournemire (Aveyron, France) developed by IRSN (French Institute for Radiological Protection and Nuclear Safety) is composed of a tunnel excavated in an argillite formation belonging to a limestone-argillite-limestone subhorizontal sedimentary sequence. Subvertical secondary fault zones were intercepted in argillite using drifts and boreholes in the tunnel excavated at a depth of about 250 m located under the Larzac Plateau. A 2D 2.5 km baseline large-scale electrical resistivity survey conducted in 2007 allowed detecting in the upper limestones several significantly low electrical resistivity subvertical zones (Guc(élis) et al. Appl Geophys 167(11): 1405-1418, 2010). One of these discontinuities is consistent with the extension towards the surface of the secondary fault zones identified in the argillite formation from the tunnel. In an attempt to better characterize this zone, IRSN and MINES ParisTech conducted a high-resolution electrical resistivity survey located transversally to the fault and fracture zones. A 760-m-long profile was acquired using two array geometries and take-outs of 2, 4 and 8 m, requiring several roll-alongs. These data were first inverted independently for each take-out and then using all take-outs together for a given array geometry. Different inverted 2D electrical resistivity models display the same global features with high (higher than 5000 ?m) to low (lower than 100 ?m) electrical resistivity zones. These electrical resistivity models are finally compared with a geological cross-section based on independent data. The subvertical conductive zones are in agreement with the fault and fracture locations inferred from the geological cross-section. Moreover, the top of a more conductive zone, below a high electrical conductive zone and between two subvertical fault zones, is located in a more sandy and argillaceous layer. This conductive zone is interpreted as the presence of a more scattered fracture zone located at depth between two fault zones. This zone could be correlated with the fractured zones identified at 250-m depth in underground works. This study highlights the interest of multi-scale approaches to image complex heterogeneous near subsurface layers. Finally, this study shows that the electrical resistivity tomography is a useful and powerful tool to detect fault and fracture zones in upper limestones. Such a method is complementary to other geophysical and geological data.

  8. An experiment in software fault elimination and fault tolerance

    SciTech Connect

    Shimeall, T.J.

    1989-01-01

    Three primary approaches have been taken in developing methods to improve software reliability: fault avoidance, fault elimination and fault tolerance. This study investigates the error detection obtained by application of two of these approaches, fault tolerance and fault elimination, on a set of independently developed versions of a program. Different fault detection techniques following each approach are used to provide a broad exposure of each approach on the versions. The fault detection techniques chosen were multi-version voting, programmer-inserted run-time assertions, testing, code reading of uncommented code by stepwise abstraction and static data flow analysis. Voting and run-time assertions are most commonly associated with fault tolerance. Testing, code reading and static data flow analysis are most commonly associated with fault elimination. After application of the techniques following each approach, the errors detected and the circumstances of detection were analyzed as a means of characterizing the differences between the approaches. The results of this study provide insight on a series of research questions. The results demonstrate weaknesses in the fault tolerance approach and specifically in the multi-version voting method. In particular, the results demonstrate that voting of untested software may produce an insufficient improvement in the probability of producing a correct result to consider such use in systems where reliability is important. Voting is not to be a substitute for testing. Examination of the faults detected in this experiment show that the majority of faults were detected by only one technique. The results of this study suggest a series of questions for further research. For example, research is needed on how to broaden the classes of faults detected by each technique.

  9. Method and system for controlling a permanent magnet machine during fault conditions

    DOEpatents

    Krefta, Ronald John; Walters, James E.; Gunawan, Fani S.

    2004-05-25

    Method and system for controlling a permanent magnet machine driven by an inverter is provided. The method allows for monitoring a signal indicative of a fault condition. The method further allows for generating during the fault condition a respective signal configured to maintain a field weakening current even though electrical power from an energy source is absent during said fault condition. The level of the maintained field-weakening current enables the machine to operate in a safe mode so that the inverter is protected from excess voltage.

  10. Methods of DNA methylation detection

    NASA Technical Reports Server (NTRS)

    Maki, Wusi Chen (Inventor); Filanoski, Brian John (Inventor); Mishra, Nirankar (Inventor); Rastogi, Shiva (Inventor)

    2010-01-01

    The present invention provides for methods of DNA methylation detection. The present invention provides for methods of generating and detecting specific electronic signals that report the methylation status of targeted DNA molecules in biological samples.Two methods are described, direct and indirect detection of methylated DNA molecules in a nano transistor based device. In the direct detection, methylated target DNA molecules are captured on the sensing surface resulting in changes in the electrical properties of a nano transistor. These changes generate detectable electronic signals. In the indirect detection, antibody-DNA conjugates are used to identify methylated DNA molecules. RNA signal molecules are generated through an in vitro transcription process. These RNA molecules are captured on the sensing surface change the electrical properties of nano transistor thereby generating detectable electronic signals.

  11. Bio-inspired WSN architecture: event detection and loacalization in a fault tolerant WSN

    NASA Astrophysics Data System (ADS)

    Alayev, Yosef; Damarla, Thyagaraju

    2009-05-01

    One can think of human body as a sensory network. In particular, skin has several neurons that provide the sense of touch with different sensitivities, and neurons for communicating the sensory signals to the brain. Even though skin might occasionally experience some lacerations, it performs remarkably well (fault tolerant) with the failure of some sensors. One of the challenges in collaborative wireless sensor networks (WSN) is fault tolerant detection and localization of targets. In this paper we present a biologically inspired architecture model for WSN. Diagnosis of sensors in WSN model presented here is derived from the concept of the immune system. We present an architecture for WSN for detection and localization of multiple targets inspired by human nervous system. We show that the advantages of such bio-inspired networks are reduced data for communication, self-diagnosis to detect faulty sensors in real-time and the ability to localize events. We present the results of our algorithms on simulation data.

  12. Disk Crack Detection for Seeded Fault Engine Test

    NASA Technical Reports Server (NTRS)

    Luo, Huageng; Rodriguez, Hector; Hallman, Darren; Corbly, Dennis; Lewicki, David G. (Technical Monitor)

    2004-01-01

    Work was performed to develop and demonstrate vibration diagnostic techniques for the on-line detection of engine rotor disk cracks and other anomalies through a real engine test. An existing single-degree-of-freedom non-resonance-based vibration algorithm was extended to a multi-degree-of-freedom model. In addition, a resonance-based algorithm was also proposed for the case of one or more resonances. The algorithms were integrated into a diagnostic system using state-of-the- art commercial analysis equipment. The system required only non-rotating vibration signals, such as accelerometers and proximity probes, and the rotor shaft 1/rev signal to conduct the health monitoring. Before the engine test, the integrated system was tested in the laboratory by using a small rotor with controlled mass unbalances. The laboratory tests verified the system integration and both the non-resonance and the resonance-based algorithm implementations. In the engine test, the system concluded that after two weeks of cycling, the seeded fan disk flaw did not propagate to a large enough size to be detected by changes in the synchronous vibration. The unbalance induced by mass shifting during the start up and coast down was still the dominant response in the synchronous vibration.

  13. Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform

    NASA Astrophysics Data System (ADS)

    Wang, Yanxue; He, Zhengjia; Zi, Yanyang

    2010-01-01

    In order to enhance the desired features related to some special type of machine fault, a technique based on the dual-tree complex wavelet transform (DTCWT) is proposed in this paper. It is demonstrated that DTCWT enjoys better shift invariance and reduced spectral aliasing than second-generation wavelet transform (SGWT) and empirical mode decomposition by means of numerical simulations. These advantages of the DTCWT arise from the relationship between the two dual-tree wavelet basis functions, instead of the matching of the used single wavelet basis function to the signal being analyzed. Since noise inevitably exists in the measured signals, an enhanced vibration signals denoising algorithm incorporating DTCWT with NeighCoeff shrinkage is also developed. Denoising results of vibration signals resulting from a crack gear indicate the proposed denoising method can effectively remove noise and retain the valuable information as much as possible compared to those DWT- and SGWT-based NeighCoeff shrinkage denoising methods. As is well known, excavation of comprehensive signatures embedded in the vibration signals is of practical importance to clearly clarify the roots of the fault, especially the combined faults. In the case of multiple features detection, diagnosis results of rolling element bearings with combined faults and an actual industrial equipment confirm that the proposed DTCWT-based method is a powerful and versatile tool and consistently outperforms SGWT and fast kurtogram, which are widely used recently. Moreover, it must be noted, the proposed method is completely suitable for on-line surveillance and diagnosis due to its good robustness and efficient algorithm.

  14. NMESys: An expert system for network fault detection

    NASA Technical Reports Server (NTRS)

    Nelson, Peter C.; Warpinski, Janet

    1991-01-01

    The problem of network management is becoming an increasingly difficult and challenging task. It is very common today to find heterogeneous networks consisting of many different types of computers, operating systems, and protocols. The complexity of implementing a network with this many components is difficult enough, while the maintenance of such a network is an even larger problem. A prototype network management expert system, NMESys, implemented in the C Language Integrated Production System (CLIPS). NMESys concentrates on solving some of the critical problems encountered in managing a large network. The major goal of NMESys is to provide a network operator with an expert system tool to quickly and accurately detect hard failures, potential failures, and to minimize or eliminate user down time in a large network.

  15. Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

    NASA Astrophysics Data System (ADS)

    Kong, Changduk; Lim, Semyeong

    2011-12-01

    Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

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

    NASA Technical Reports Server (NTRS)

    Matthews, Bryan L.; Srivastava, Ashok N.

    2010-01-01

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

  17. Abnormality degree detection method using negative potential field group detectors

    NASA Astrophysics Data System (ADS)

    Zhang, Hongli; Liu, Shulin; Li, Dong; Shi, Kunju; Wang, Bo; Cui, Jiqiang

    2015-09-01

    Online monitoring methods have been widely used in many major devices, however the normal and abnormal states of equipment are estimated mainly based on the monitoring results whether monitored parameters exceed the setting thresholds. Using these monitoring methods may cause serious false positive or false negative results. In order to precisely monitor the state of equipment, the problem of abnormality degree detection without fault sample is studied with a new detection method called negative potential field group detectors(NPFG-detectors). This method achieves the quantitative expression of abnormality degree and provides the better detection results compared with other methods. In the process of Iris data set simulation, the new algorithm obtains the successful results in abnormal detection. The detection rates for 3 types of Iris data set respectively reach 100%, 91.6%, and 95.24% with 50% training samples. The problem of Bearing abnormality degree detection via an abnormality degree curve is successfully solved.

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

    SciTech Connect

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

    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 represent 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.

  19. Distributed bearing fault diagnosis based on vibration analysis

    NASA Astrophysics Data System (ADS)

    Dolenc, Boštjan; Boškoski, Pavle; Juričić, Đani

    2016-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Lancraft, R. E.

    1985-01-01

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

  1. 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. PMID:22902083

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

    SciTech Connect

    Zhang Yumin; Lum, Kai-Yew; Wang Qingguo

    2009-03-05

    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.

  3. Developing Advanced Seismic Imaging Methods For Characterizing the Fault Zone Structure

    NASA Astrophysics Data System (ADS)

    Zhang, Haijiang

    2015-04-01

    Here I present a series of recent developments on seismic imaging of fault zone structure. The goals of these advanced methods are to better determine the physical properties (including seismic velocity, attenuation, and anisotropy) around the fault zone and its boundaries. In order to accurately determine the seismic velocity structure of the fault zone, we have recently developed a wavelet-based double-difference seismic tomography method, in which the wavelet coefficients of the velocity model, rather than the model itself, are solved using both the absolute and differential arrival times. This method takes advantage of the multiscale nature of the velocity model and the multiscale wavelet representation property. Because of the velocity model is sparse in the wavelet domain, a sparsity constraint is applied to tomographic inversion. Compared to conventional tomography methods, the new method is both data- and model-adaptive, and thus can better resolve the fault zone structure. In addition to seismic velocity property of the fault zone, seismic anisotropy and attenuation properties are also important to characterize the fault zone structure. For this reason, we developed the seismic anisotropy tomography method to image the three-dimensional anisotropy strength model of the fault zone using shear wave splitting delay times between fast and slow shear waves. The applications to the San Andreas fault around Parkfield, California and north Anatolian fault in Turkey will be shown. To better constrain the seismic attenuation structure, we developed a new seismic attenuation tomography method using measured t* values for first arrival body waves, in which the structures of attenuation and velocity models are similar through the cross-gradient constraint. Seismic tomography can, however, only resolve the smooth variations in elastic properties in Earth's interior. To image structure at length scales smaller than what can be resolved tomographically, including elasticity contrasts across faults, one must use the scattered seismic wavefield (for instance, reflections and phase conversions). Here I present the results of using a generalized Radon transform (GRT) for the passive waveform imaging of near vertical faults in the shallow part of the crust around the SAFOD site, California. The application to the Longmenshan fault system using the aftershock data of the 2013 Mw6.6 Lushan earthquake, China will also be presented.

  4. Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Wang, Yi; Xu, Guanghua; Liang, Lin; Jiang, Kuosheng

    2015-03-01

    The kurtogram-based methods have been proved powerful and practical to detect and characterize transient components in a signal. The basic idea of the kurtogram-based methods is to use the kurtosis as a measure to discover the presence of transient impulse components and to indicate the frequency band where these occur. However, the performance of the kurtogram-based methods is poor due to the low signal-to-noise ratio. As the weak transient signal with a wide spread frequency band can be easily masked by noise. Besides, selecting signal just in one frequency band will leave out some transient features. Aiming at these shortcomings, different frequency bands signal fusion is adopted in this paper. Considering that manifold learning aims at discovering the nonlinear intrinsic structure which embedded in high dimensional data, this paper proposes a waveform feature manifold (WFM) method to extract the weak signature from waveform feature space which obtained by binary wavelet packet transform. Minimum permutation entropy is used to select the optimal parameter in a manifold learning algorithm. A simulated bearing fault signal and two real bearing fault signals are used to validate the improved performance of the proposed method through the comparison with the kurtogram-based methods. The results show that the proposed method outperforms the kurtogram-based methods and is effective in weak signature extraction.

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

    PubMed Central

    Park, Wookje; Jung, Sikhang

    2014-01-01

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

  6. Fault detection of aircraft system with random forest algorithm and similarity measure.

    PubMed

    Lee, Sanghyuk; Park, Wookje; Jung, Sikhang

    2014-01-01

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

  7. 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.

  8. Isolability of faults in sensor fault diagnosis

    NASA Astrophysics Data System (ADS)

    Sharifi, Reza; Langari, Reza

    2011-10-01

    A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.

  9. A Fault Diagnosis Approach for Rolling Bearings Based on EMD Method and Eigenvector Algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Jinyu; Huang, Xianxiang

    Fault diagnosis of rolling bearings is still a very important and difficult research task on engineering. After analyzing the shortcomings of current bearing fault diagnosis technologies, a new approach based on Empirical Mode Decomposition (EMD) and blind equalization eigenvector algorithm (EVA) for rolling bearings fault diagnosis is proposed. In this approach, the characteristic high-frequency signal with amplitude and channel modulation of a rolling bearing with local damage is first separated from the mechanical vibration signal as an Intrinsic Mode Function (IMF) by using EMD, then the source impact vibration signal yielded by local damage is extracted by means of a EVA model and algorithm. Finally, the presented approach is used to analyze an impacting experiment and two real signals collected from rolling bearings with outer race damage or inner race damage. The results show that the EMD and EVA based approach can effectively detect rolling bearing fault.

  10. Chaotic Extension Neural Network Theory-Based XXY Stage Collision Fault Detection Using a Single Accelerometer Sensor

    PubMed Central

    Hsieh, Chin-Tsung; Yau, Her-Terng; Wu, Shang-Yi; Lin, Huo-Cheng

    2014-01-01

    The collision fault detection of a XXY stage is proposed for the first time in this paper. The stage characteristic signals are extracted and imported into the master and slave chaos error systems by signal filtering from the vibratory magnitude of the stage. The trajectory diagram is made from the chaos synchronization dynamic error signals E1 and E2. The distance between characteristic positive and negative centers of gravity, as well as the maximum and minimum distances of trajectory diagram, are captured as the characteristics of fault recognition by observing the variation in various signal trajectory diagrams. The matter-element model of normal status and collision status is built by an extension neural network. The correlation grade of various fault statuses of the XXY stage was calculated for diagnosis. The dSPACE is used for real-time analysis of stage fault status with an accelerometer sensor. Three stage fault statuses are detected in this study, including normal status, Y collision fault and X collision fault. It is shown that the scheme can have at least 75% diagnosis rate for collision faults of the XXY stage. As a result, the fault diagnosis system can be implemented using just one sensor, and consequently the hardware cost is significantly reduced. PMID:25405512

  11. Chaotic extension neural network theory-based XXY stage collision fault detection using a single accelerometer sensor.

    PubMed

    Hsieh, Chin-Tsung; Yau, Her-Terng; Wu, Shang-Yi; Lin, Huo-Cheng

    2014-01-01

    The collision fault detection of a XXY stage is proposed for the first time in this paper. The stage characteristic signals are extracted and imported into the master and slave chaos error systems by signal filtering from the vibratory magnitude of the stage. The trajectory diagram is made from the chaos synchronization dynamic error signals E1 and E2. The distance between characteristic positive and negative centers of gravity, as well as the maximum and minimum distances of trajectory diagram, are captured as the characteristics of fault recognition by observing the variation in various signal trajectory diagrams. The matter-element model of normal status and collision status is built by an extension neural network. The correlation grade of various fault statuses of the XXY stage was calculated for diagnosis. The dSPACE is used for real-time analysis of stage fault status with an accelerometer sensor. Three stage fault statuses are detected in this study, including normal status, Y collision fault and X collision fault. It is shown that the scheme can have at least 75% diagnosis rate for collision faults of the XXY stage. As a result, the fault diagnosis system can be implemented using just one sensor, and consequently the hardware cost is significantly reduced. PMID:25405512

  12. A Diagnosis method of the small end fault on reciprocating compressor connecting rod

    NASA Astrophysics Data System (ADS)

    Jiang, Zhinong; Mao, Zhiwei; Yao, Ziyun; Zhang, Jinjie

    2015-08-01

    The connecting rod is the key moving part of a reciprocating compressor, of which the stress state is extremely complicate and the wear fault of the small end is always a bottleneck problem in the field of fault monitoring and diagnosing. This paper is aimed to present a new method to diagnose the above wear fault. Firstly, a contact model of a clearance in the revolute joint of the small end of a connecting rod bearing (SECRB) was established and a multi-body simulation tool was utilized to simulate the slider-crank mechanism with a clearance, from which the dynamic influence of wear gap in SECRB of a slider-crank mechanism was obtained. Based on the study above, we extracted the characteristics of the wear fault of SECRB and then proposed a brand new approach to monitoring and diagnosing this wear fault by analyzing the angle domain of vibration signals. The availability was verified by conducting an experiment on a reciprocating compressor. And the experimental results show that this method can not only accurately diagnose the wear fault of SECRB but also approximately estimate its severity. This study laid a foundation for the online monitoring and early warning of this fault.

  13. Fault Detection in Distributed Climate Sensor Networks using Dynamic Bayesian Networks

    SciTech Connect

    Chin, George; Choudhury, Sutanay; Kangas, Lars J.; McFarlane, Sally A.; Marquez, Andres

    2010-12-07

    The Atmospheric Radiation Measurement program operated by U.S. Department of Energy is one of the largest climate research programs dedicated to the collection of long-term continuous measurements of cloud properties and other key components of the earth’s climate system. Given the critical role that collected ARM data plays in the analysis of atmospheric processes and conditions and in the enhancement and evaluation of global climate models, the production and distribution of high-quality data is one of ARM’s primary mission objectives. Fault detection in ARM’s distributed sensor network is one critical ingredient towards maintaining high quality and useful data. We are modeling ARM’s distributed sensor network as a dynamic Bayesian network where key measurements are mapped to Bayesian network variables. We then define the conditional dependencies between variables by discovering highly correlated variable pairs from historical data. The resultant dynamic Bayesian network provides an automated approach to identifying whether certain sensors are malfunctioning or failing in the distributed sensor network. A potential fault or failure is detected when an observed measurement is not consistent with its expected measurement and the observed measurements of other related sensors in the Bayesian network. We present some of our experiences and promising results with the fault detection dynamic Bayesian network.

  14. Comparison of chiller models for use in model-based fault detection

    SciTech Connect

    Sreedharan, Priya; Haves, Philip

    2001-06-07

    Selecting the model is an important and essential step in model based fault detection and diagnosis (FDD). Factors that are considered in evaluating a model include accuracy, training data requirements, calibration effort, generality, and computational requirements. The objective of this study was to evaluate different modeling approaches for their applicability to model based FDD of vapor compression chillers. Three different models were studied: the Gordon and Ng Universal Chiller model (2nd generation) and a modified version of the ASHRAE Primary Toolkit model, which are both based on first principles, and the DOE-2 chiller model, as implemented in CoolTools{trademark}, which is empirical. The models were compared in terms of their ability to reproduce the observed performance of an older, centrifugal chiller operating in a commercial office building and a newer centrifugal chiller in a laboratory. All three models displayed similar levels of accuracy. Of the first principles models, the Gordon-Ng model has the advantage of being linear in the parameters, which allows more robust parameter estimation methods to be used and facilitates estimation of the uncertainty in the parameter values. The ASHRAE Toolkit Model may have advantages when refrigerant temperature measurements are also available. The DOE-2 model can be expected to have advantages when very limited data are available to calibrate the model, as long as one of the previously identified models in the CoolTools library matches the performance of the chiller in question.

  15. 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.

  16. 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.

  17. Method for detecting an element

    DOEpatents

    Blackwood, Larry G.; Reber, Edward L.; Rohde, Kenneth W.

    2007-02-06

    A method for detecting an element is disclosed and which includes the steps of providing a gamma-ray spectrum which depicts, at least in part, a test region having boundaries, and which has a small amount of the element to be detected; providing a calculation which detects the small amount of the element to be detected; and providing a moving window and performing the calculation within the moving window, and over a range of possible window boundaries within the test region to determine the location of the optimal test region within the gamma-ray spectrum.

  18. Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter.

    PubMed

    Wang, Tianzhen; Qi, Jie; Xu, Hao; Wang, Yide; Liu, Lei; Gao, Diju

    2016-01-01

    Thanks to reduced switch stress, high quality of load wave, easy packaging and good extensibility, the cascaded H-bridge multilevel inverter is widely used in wind power system. To guarantee stable operation of system, a new fault diagnosis method, based on Fast Fourier Transform (FFT), Relative Principle Component Analysis (RPCA) and Support Vector Machine (SVM), is proposed for H-bridge multilevel inverter. To avoid the influence of load variation on fault diagnosis, the output voltages of the inverter is chosen as the fault characteristic signals. To shorten the time of diagnosis and improve the diagnostic accuracy, the main features of the fault characteristic signals are extracted by FFT. To further reduce the training time of SVM, the feature vector is reduced based on RPCA that can get a lower dimensional feature space. The fault classifier is constructed via SVM. An experimental prototype of the inverter is built to test the proposed method. Compared to other fault diagnosis methods, the experimental results demonstrate the high accuracy and efficiency of the proposed method. PMID:26626623

  19. Fault/fracture density and mineralization: a contouring method for targeting in gold exploration

    NASA Astrophysics Data System (ADS)

    Tripp, Gerard I.; Vearncombe, Julian R.

    2004-06-01

    A widely observed correlation between high fracture density and mineralization throughout terranes and geological time indicates a fundamental underlying ore-forming process. In Archaean greenstone-hosted deposits, high-density fracturing was accompanied by enhanced fluid flow during fault/fracture network development, producing regional-scale fluid pressure gradients that focussed hydrothermal fluids into preferentially fractured areas. Fracture density is both increased and decreased during faulting and fault healing, and fracture density accumulates over time, in zones of high palaeo-fluid flow. Localised zones where the density of fracturing is increased by deformation, become permeability nodes for migrating hydrothermal fluids leading to large zones of alteration and gold precipitation. The Ora Banda mining centre in Western Australia contains significant gold deposits that appear to demonstrate a close association between high-density fracturing and gold precipitation. Fracture density in the Ora Banda mines was enhanced by fault-fault intersections, fault-contact intersections and changes in fault geometry. The mine-scale relationships between fracture density and gold mineralization are repeated at smaller and larger scales, hence these relationships may be used in targeting for gold exploration. Contouring the density of fracturing in a region provides a semi-quantitative way to rank areas for exploration and uses data from mapping, drilling and high-quality geophysical data as a basis for analyses. Fracture density contouring is complementary to other prospectivity-analysis methods.

  20. Fault-tolerant linear optics quantum computation by error-detecting quantum state transfer

    SciTech Connect

    Cho, Jaeyoon

    2007-10-15

    A scheme for linear optical implementation of fault-tolerant quantum computation is proposed, which is based on an error-detecting code. Each computational step is mediated by transfer of quantum information into an ancilla system embedding error-detection capability. Photons are assumed to be subjected to both photon loss and depolarization, and the threshold region of their strengths for scalable quantum computation is obtained, together with the amount of physical resources consumed. Compared to currently known results, the present scheme reduces the resource requirement, while yielding a comparable threshold region.

  1. Fault Scarp Detection Beneath Dense Vegetation Cover: Airborne Lidar Mapping of the Seattle Fault Zone, Bainbridge Island, Washington State

    NASA Technical Reports Server (NTRS)

    Harding, David J.; Berghoff, Gregory S.

    2000-01-01

    The emergence of a commercial airborne laser mapping industry is paying major dividends in an assessment of earthquake hazards in the Puget Lowland of Washington State. Geophysical observations and historical seismicity indicate the presence of active upper-crustal faults in the Puget Lowland, placing the major population centers of Seattle and Tacoma at significant risk. However, until recently the surface trace of these faults had never been identified, neither on the ground nor from remote sensing, due to cover by the dense vegetation of the Pacific Northwest temperate rainforests and extremely thick Pleistocene glacial deposits. A pilot lidar mapping project of Bainbridge Island in the Puget Sound, contracted by the Kitsap Public Utility District (KPUD) and conducted by Airborne Laser Mapping in late 1996, spectacularly revealed geomorphic features associated with fault strands within the Seattle fault zone. The features include a previously unrecognized fault scarp, an uplifted marine wave-cut platform, and tilted sedimentary strata. The United States Geologic Survey (USGS) is now conducting trenching studies across the fault scarp to establish ages, displacements, and recurrence intervals of recent earthquakes on this active fault. The success of this pilot study has inspired the formation of a consortium of federal and local organizations to extend this work to a 2350 square kilometer (580,000 acre) region of the Puget Lowland, covering nearly the entire extent (approx. 85 km) of the Seattle fault. The consortium includes NASA, the USGS, and four local groups consisting of KPUD, Kitsap County, the City of Seattle, and the Puget Sound Regional Council (PSRC). The consortium has selected Terrapoint, a commercial lidar mapping vendor, to acquire the data.

  2. Paleostress reconstruction from calcite twin and fault-slip data using the multiple inverse method in the East Walanae fault zone: Implications for the Neogene contraction in South Sulawesi, Indonesia

    NASA Astrophysics Data System (ADS)

    Jaya, Asri; Nishikawa, Osamu

    2013-10-01

    A new approach for paleostress analysis using the multiple inverse method with calcite twin data including untwinned e-plane was performed in the East Walanae fault (EWF) zone in South Sulawesi, Indonesia. Application of untwinned e-plane data of calcite grain to constrain paleostress determination is the first attempt for this method. Stress states caused by the collision of the south-east margin of Sundaland with the Australian microcontinents during the Pliocene were successfully detected from a combination of calcite-twin data and fault-slip data. This Pliocene NE-SW-to-E-W-directed maximum compression activated the EWF as a reverse fault with a dextral component of slip with pervasive development of secondary structures in the narrow zone between Bone Mountain and Walanae Depression.

  3. Feature Extraction using Wavelet Transform for Multi-class Fault Detection of Induction Motor

    NASA Astrophysics Data System (ADS)

    Chattopadhyay, P.; Konar, P.

    2014-01-01

    In this paper the theoretical aspects and feature extraction capabilities of continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are experimentally verified from the point of view of fault diagnosis of induction motors. Vertical frame vibration signal is analyzed to develop a wavelet based multi-class fault detection scheme. The redundant and high dimensionality information of CWT makes it computationally in-efficient. Using greedy-search feature selection technique (Greedy-CWT) the redundancy is eliminated to a great extent and found much superior to the widely used DWT technique, even in presence of high level of noise. The results are verified using MLP, SVM, RBF classifiers. The feature selection technique has enabled determination of the most relevant CWT scales and corresponding coefficients. Thus, the inherent limitations of CWT like proper selection of scales and redundant information are eliminated. In the present investigation `db8' is found as the best mother wavelet, due to its long period and higher number of vanishing moments, for detection of motor faults.

  4. Fault finder

    DOEpatents

    Bunch, Richard H. (1614 NW. 106th St., Vancouver, WA 98665)

    1986-01-01

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

  5. 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.

  6. Fault-tolerant quantum computation with a soft-decision decoder for error correction and detection by teleportation

    NASA Astrophysics Data System (ADS)

    Goto, Hayato; Uchikawa, Hironori

    2013-06-01

    Fault-tolerant quantum computation with quantum error-correcting codes has been considerably developed over the past decade. However, there are still difficult issues, particularly on the resource requirement. For further improvement of fault-tolerant quantum computation, here we propose a soft-decision decoder for quantum error correction and detection by teleportation. This decoder can achieve almost optimal performance for the depolarizing channel. Applying this decoder to Knill's C4/C6 scheme for fault-tolerant quantum computation, which is one of the best schemes so far and relies heavily on error correction and detection by teleportation, we dramatically improve its performance. This leads to substantial reduction of resources.

  7. Fault detection and isolation of PEM fuel cell system based on nonlinear analytical redundancy. An application via parity space approach

    NASA Astrophysics Data System (ADS)

    Aitouche, A.; Yang, Q.; Ould Bouamama, B.

    2011-05-01

    This paper presents a procedure dealing with the issue of fault detection and isolation (FDI) using nonlinear analytical redundancy (NLAR) technique applied in a proton exchange membrane (PEM) fuel cell system based on its mathematic model. The model is proposed and simplified into a five orders state space representation. The transient phenomena captured in the model include the compressor dynamics, the flow characteristics, mass and energy conservation and manifold fluidic mechanics. Nonlinear analytical residuals are generated based on the elimination of the unknown variables of the system by an extended parity space approach to detect and isolate actuator and sensor faults. Finally, numerical simulation results are given corresponding to a faults signature matrix.

  8. 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.

  9. Fault detection of mechanical drives under variable operating conditions based on wavelet packet Rényi entropy signatures

    NASA Astrophysics Data System (ADS)

    Boškoski, Pavle; Juri?i?, ?ani

    2012-08-01

    In this paper we propose a novel approach for the diagnosis of gearboxes in presumably non-stationary and unknown operating conditions. The approach makes use of information indices based on the Rényi entropy derived from coefficients of the wavelet packet transform of measured vibration records. These indices quantify some statistical properties of instantaneous power of the generated vibration that are largely unaffected by changes in the operating conditions. The analysis is based on probability density of the envelope of a sum of sinusoidal signals with random amplitude and phase. Such an approach requires no a priori information about the operating conditions and no prior data describing physical characteristics of the monitored drive. The fault detection capabilities of the proposed feature set are demonstrated on a two-stage gearbox operating under different rotational speeds and loads with various seeded mechanical faults.

  10. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

    NASA Astrophysics Data System (ADS)

    Wang, Yanxue; Xiang, Jiawei; Markert, Richard; Liang, Ming

    2016-01-01

    Condition-based maintenance via vibration signal processing plays an important role to reduce unscheduled machine downtime and avoid catastrophic accidents in industrial enterprises. Many machine faults, such as local defects in rotating machines, manifest themselves in the acquired vibration signals as a series of impulsive events. The spectral kurtosis (SK) technique extends the concept of kurtosis to that of a function of frequency that indicates how the impulsiveness of a signal. This work intends to review and summarize the recent research developments on the SK theories, for instance, short-time Fourier transform-based SK, kurtogram, adaptive SK and protrugram, as well as the corresponding applications in fault detection and diagnosis of the rotating machines. The potential prospects of prognostics using SK technique are also designated. Some examples have been presented to illustrate their performances. The expectation is that further research and applications of the SK technique will flourish in the future, especially in the fields of the prognostics.

  11. 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.

  12. Detection, isolation and fault estimation of nonlinear systems using a directional study

    NASA Astrophysics Data System (ADS)

    Kallas, Maya; Mourot, Gilles; Maquin, Didier; Ragot, José

    2015-11-01

    In terms of system diagnosis, several studies are generally performed. The diagnosis is composed of three different parts: detecting, isolating and estimating the value of the faults. If many results have been obtained for linear systems with a known model, the situation is quite different in the case of nonlinear systems behavior, especially when the model is not known a priori. This paper proposes to discuss the latter case using a study of the dissimilarities between data. The dissimilarities are evaluated by a nonlinear function of the Euclidean distances. To this end, a radial basis function is used, and a directional study is introduced for fault diagnosis. The relevance of the proposed technique is illustrated on simulated data.

  13. A Feature Extraction Method for Fault Classification of Rolling Bearing based on PCA

    NASA Astrophysics Data System (ADS)

    Wang, Fengtao; Sun, Jian; Yan, Dawen; Zhang, Shenghua; Cui, Liming; Xu, Yong

    2015-07-01

    This paper discusses the fault feature selection using principal component analysis (PCA) for bearing faults classification. Multiple features selected from the time-frequency domain parameters of vibration signals are analyzed. First, calculate the time domain statistical features, such as root mean square and kurtosis; meanwhile, by Fourier transformation and Hilbert transformation, the frequency statistical features are extracted from the frequency spectrum. Then the PCA is used to reduce the dimension of feature vectors drawn from raw vibration signals, which can improve real time performance and accuracy of the fault diagnosis. Finally, a fuzzy C-means (FCM) model is established to implement the diagnosis of rolling bearing faults. Practical rolling bearing experiment data is used to verify the effectiveness of the proposed method.

  14. Implementation and testing of a fault detection software tool for improving control system performance in a large commercial building

    SciTech Connect

    Salsbury, T.I.; Diamond, R.C.

    2000-05-01

    This paper describes a model-based, feedforward control scheme that can detect faults in the controlled process and improve control performance over traditional PID control. The tool uses static simulation models of the system under control to generate feed-forward control action, which acts as a reference of correct operation. Faults that occur in the system cause discrepancies between the feedforward models and the controlled process. The scheme facilitates detection of faults by monitoring the level of these discrepancies. We present results from the first phase of tests on a dual-duct air-handling unit installed in a large office building in San Francisco. We demonstrate the ability of the tool to detect a number of preexisting faults in the system and discuss practical issues related to implementation.

  15. 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.

  16. A novel micro-Raman technique to detect and characterize 4H-SiC stacking faults

    SciTech Connect

    Piluso, N. Camarda, M.; La Via, F.

    2014-10-28

    A novel Micro-Raman technique was designed and used to detect extended defects in 4H-SiC homoepitaxy. The technique uses above band-gap high-power laser densities to induce a local increase of free carriers in undoped epitaxies (n?faults; the obtained morphologies were found to be in excellent agreement with those provided by standard photoluminescence techniques. The results show that the detection of defects via i-LOPC spectroscopy is totally independent from the stacking fault photoluminescence signals that cover a large energy range up to 0.7?eV, thus allowing for a single-scan simultaneous determination of any kind of stacking fault. Combining the i-LOPC method with the analysis of the transverse optical mode, the micro-Raman characterization can determine the most important properties of unintentionally doped film, including the stress status of the wafer, lattice impurities (point defects, polytype inclusions) and a detailed analysis of crystallographic defects, with a high spectral and spatial resolution.

  17. 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.

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

    DOEpatents

    Andrews, Lowell B.

    1998-01-01

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

  19. Method for detecting biological toxins

    SciTech Connect

    Ligler, F.S.; Campbell, J.R.

    1992-01-01

    Biological toxins are indirectly detected by using polymerase chain reaction to amplify unique nucleic acid sequences coding for the toxins or enzymes unique to toxin synthesis. Buffer, primers coding for the unique nucleic acid sequences and an amplifying enzyme are added to a sample suspected of containing the toxin. The mixture is then cycled thermally to exponentially amplify any of these unique nucleic acid sequences present in the sample. The amplified sequences can be detected by various means, including fluorescence. Detection of the amplified sequences is indicative of the presence of toxin in the original sample. By using more than one set of labeled primers, the method can be used to simultaneously detect several toxins in a sample.

  20. 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.

  1. 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.

  2. Towards Certification of a Space System Application of Fault Detection and Isolation

    NASA Technical Reports Server (NTRS)

    Feather, Martin S.; Markosian, Lawrence Z.

    2008-01-01

    Advanced fault detection, isolation and recovery (FDIR) software is being investigated at NASA as a means to the improve reliability and availability of its space systems. Certification is a critical step in the acceptance of such software. Its attainment hinges on performing the necessary verification and validation to show that the software will fulfill its requirements in the intended setting. Presented herein is our ongoing work to plan for the certification of a pilot application of advanced FDIR software in a NASA setting. We describe the application, and the key challenges and opportunities it offers for certification.

  3. Model-based fault detection and identification with online aerodynamic model structure selection

    NASA Astrophysics Data System (ADS)

    Lombaerts, T.

    2013-12-01

    This publication describes a recursive algorithm for the approximation of time-varying nonlinear aerodynamic models by means of a joint adaptive selection of the model structure and parameter estimation. This procedure is called adaptive recursive orthogonal least squares (AROLS) and is an extension and modification of the previously developed ROLS procedure. This algorithm is particularly useful for model-based fault detection and identification (FDI) of aerospace systems. After the failure, a completely new aerodynamic model can be elaborated recursively with respect to structure as well as parameter values. The performance of the identification algorithm is demonstrated on a simulation data set.

  4. Orbital maneuvering subsystem functional path analysis for performance monitoring fault detection and annunciation

    NASA Technical Reports Server (NTRS)

    Keesler, E. L.

    1974-01-01

    The functional paths of the Orbital Maneuver Subsystem (OMS) is defined. The operational flight instrumentation required for performance monitoring, fault detection, and annunciation is described. The OMS is a pressure fed rocket engine propulsion subsystem. One complete OMS shares each of the two auxiliary propulsion subsystem pods with a reaction control subsystem. Each OMS is composed of a pressurization system, a propellant tanking system, and a gimbaled rocket engine. The design, development, and operation of the system are explained. Diagrams of the system are provided.

  5. A general method for calculating co-seismic gravity changes in complex fault systems

    NASA Astrophysics Data System (ADS)

    Hayes, T. J.; Tiampo, K. F.; Rundle, J. B.; Fernández, J.

    2008-11-01

    A general method for calculating the total, dilatational, and free-air gravity for fault systems with arbitrary geometry, slip motion, and number of fault segments is presented. The technique uses a Green's function approach for a fault buried within an elastic half-space with an underlying driver plate forcing the system. The method is easily adapted to investigate a wide range of geophysical applications and is provided as a suite of Fortran90 routines. The Joshua Tree-Landers-Hector Mine sequence is examined and shows remarkable correlation of epicentres with dilatational gravity highs. We also demonstrate the use of the method for large thrusting events, using the Alaska Good Friday earthquake of 1964 as an example.

  6. Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary

    NASA Astrophysics Data System (ADS)

    Cui, Lingli; Wu, Na; Ma, Chunqing; Wang, Huaqing

    2016-02-01

    A novel matching pursuit method based on a new step-impulse dictionary to measure the size of a bearing's spall-like fault is presented in this study. Based on the seemingly double-impact theory and the rolling bearing fault mechanism, a theoretical model for the bearing fault with different spall-like fault sizes is developed and analyzed, and the seemingly double-impact characteristic of the bearing faults is explained. The first action that causes a bearing fault is due to the entry of the roller element into the spall-like fault which can be described as a step-like response. The second action is the exit of the roller element from the spall-like fault, which can be described as an impulse-like response. Based on the quantitative relationship between the time interval of the seemingly double-impact actions and the fault size, a novel matching pursuit method is proposed based on a new step-impulse dictionary. In addition, the quantitative matching pursuit algorithm is proposed for bearing fault diagnosis based on the new dictionary model. Finally, an atomic selection mechanism is proposed to improve the measurement accuracy of bearing fault size. The simulation results of this study indicate that the new matching pursuit method based on the new step-impulse dictionary can be reliably used to measure the sizes of bearing spall-like faults. The applications of this method to the fault signals of bearing outer-races measured at different speeds have shown that the proposed method can effectively measure a bearing's spall-like fault size.

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

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic

    1994-01-01

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

  8. Design considerations for flight test of a fault inferring nonlinear detection system algorithm for avionics sensors

    NASA Technical Reports Server (NTRS)

    Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

    1986-01-01

    The modifications to the design of a fault inferring nonlinear detection system (FINDS) algorithm to accommodate flight computer constraints and the resulting impact on the algorithm performance are summarized. An overview of the flight data-driven FINDS algorithm is presented. This is followed by a brief analysis of the effects of modifications to the algorithm on program size and execution speed. Significant improvements in estimation performance for the aircraft states and normal operating sensor biases, which have resulted from improved noise design parameters and a new steady-state wind model, are documented. The aircraft state and sensor bias estimation performances of the algorithm's extended Kalman filter are presented as a function of update frequency of the piecewise constant filter gains. The results of a new detection system strategy and failure detection performance, as a function of gain update frequency, are also presented.

  9. Design considerations for flight test of a fault inferring nonlinear detection system algorithm for avionics sensors

    NASA Technical Reports Server (NTRS)

    Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

    1986-01-01

    This paper summarizes the modifications made to the design of a fault inferring nonlinear detection system (FINDS) algorithm to accommodate flight computer constraints and the resulting impact on the algorithm performance. An overview of the flight data-driven FINDS algorithm is presented. This is followed by a brief analysis of the effects of modifications to the algorithm on program size and execution speed. Significant improvements in estimation performance for the aircraft states and normal operating sensor biases, which have resulted from improved noise design parameters and a new steady-state wind model, are documented. The aircraft state and sensor bias estimation performances of the algorithm's extended Kalman filter are presented as a function of update frequency of the piecewise constant filter gains. The results of a new detection system strategy and failure detection performance, as a function of an update frequency, are also presented.

  10. A novel intelligent fault diagnosis method for electrical equipment using infrared thermography

    NASA Astrophysics Data System (ADS)

    Zou, Hui; Huang, Fuzhen

    2015-11-01

    Infrared thermography (IRT) has taken a very important role in monitoring and inspecting thermal defects of electrical equipment without shutting down, which has important significance for the stability of power systems. It has many advantages such as non-contact detection, freedom from electromagnetic interference, safety, reliability and providing large inspection coverage. Manual analysis of infrared images for detecting defects and classifying the status of equipment may take a lot of time and efforts, and may also lead to incorrect diagnosis results. To avoid the lack of manual analysis of infrared images, many intelligent fault diagnosis methods for electrical equipment are proposed, but there are two difficulties when using these methods: one is to find the region of interest, another is to extract features which can represent the condition of electrical equipment, as it is difficult to segment infrared images due to their over-centralized distributions and low intensity contrasts, which are quite different from those in visual light images. In this paper, a new intelligent diagnosis method for classification different conditions of electrical equipment using data obtained from infrared images is presented. In the first stage of our method, an infrared image of electrical equipment is clustered using K-means algorithm, then statistical characteristics containing temperature and area information are extracted in each region. In the second stage, in order to select the salient features which can better represent the condition of electrical equipment, some or all statistical characteristics from each region are combined as input data for support vector machine (SVM) classifier. To improve the classification performance of SVM, a coarse-to-fine parameter optimization approach is adopted. The performance of SVM is compared with that of back propagation neural network. The comparison results show that our method can achieve a better performance with accuracy 97.8495%.

  11. Fault detection technique for wavelength division multiplexing passive optical network using chaotic fiber laser

    NASA Astrophysics Data System (ADS)

    Xu, Naijun; Yang, Lingzhen; Zhang, Juan; Zhang, Xiangyuan; Wang, Juanfen; Zhang, Zhaoxia; Liu, Xianglian

    2014-03-01

    We propose a fault localization method for wavelength division multiplexing passive optical network (WDM-PON). A proof-of-concept experiment was demonstrated by utilizing the wavelength tunable chaotic laser generated from an erbium-doped fiber ring laser with a manual tunable fiber Bragg grating (TFBG) filter. The range of the chaotic lasing wavelength can cover the C-band. Basing on the TFBG filter, we can adjust the wavelength of the chaotic laser to match the WDM-PON channel with identical wavelength. We determined the fault location by calculating the cross-correlation between the reference and return signals. Analysis of the characteristics of the wavelength tunable chaotic laser showed that the breakpoint, the loose connector, and the mismatch connector could be precisely located. A dynamic range of approximately 23.8 dB and a spatial resolution of 4 cm, which was independent of the measuring range, were obtained.

  12. Thermodynamic method for generating random stress distributions on an earthquake fault

    USGS Publications Warehouse

    Barall, Michael; Harris, Ruth A.

    2012-01-01

    This report presents a new method for generating random stress distributions on an earthquake fault, suitable for use as initial conditions in a dynamic rupture simulation. The method employs concepts from thermodynamics and statistical mechanics. A pattern of fault slip is considered to be analogous to a micro-state of a thermodynamic system. The energy of the micro-state is taken to be the elastic energy stored in the surrounding medium. Then, the Boltzmann distribution gives the probability of a given pattern of fault slip and stress. We show how to decompose the system into independent degrees of freedom, which makes it computationally feasible to select a random state. However, due to the equipartition theorem, straightforward application of the Boltzmann distribution leads to a divergence which predicts infinite stress. To avoid equipartition, we show that the finite strength of the fault acts to restrict the possible states of the system. By analyzing a set of earthquake scaling relations, we derive a new formula for the expected power spectral density of the stress distribution, which allows us to construct a computer algorithm free of infinities. We then present a new technique for controlling the extent of the rupture by generating a random stress distribution thousands of times larger than the fault surface, and selecting a portion which, by chance, has a positive stress perturbation of the desired size. Finally, we present a new two-stage nucleation method that combines a small zone of forced rupture with a larger zone of reduced fracture energy.

  13. A novel identification method of Volterra series in rotor-bearing system for fault diagnosis

    NASA Astrophysics Data System (ADS)

    Xia, Xin; Zhou, Jianzhong; Xiao, Jian; Xiao, Han

    2016-01-01

    Volterra series is widely employed in the fault diagnosis of rotor-bearing system to prevent dangerous accidents and improve economic efficiency. The identification of the Volterra series involves the infinite-solution problems which is caused by the periodic characteristic of the excitation signal of rotor-bearing system. But this problem has not been considered in the current identification methods of the Volterra series. In this paper, a key kernels-PSO (KK-PSO) method is proposed for Volterra series identification. Instead of identifying the Volterra series directly, the key kernels of Volterra are found out to simply the Volterra model firstly. Then, the Volterra series with the simplest formation is identified by the PSO method. Next, simulation verification is utilized to verify the feasibility and effectiveness of the KK-PSO method by comparison to the least square (LS) method and traditional PSO method. Finally, experimental tests have been done to get the Volterra series of a rotor-bearing test rig in different states, and a fault diagnosis system is built with a neural network to classify different fault conditions by the kernels of the Volterra series. The analysis results indicate that the KK-PSO method performs good capability on the identification of Volterra series of rotor-bearing system, and the proposed method can further improve the accuracy of fault diagnosis.

  14. Numerical simulations of earthquakes and the dynamics of fault systems using the Finite Element method.

    NASA Astrophysics Data System (ADS)

    Kettle, L. M.; Mora, P.; Weatherley, D.; Gross, L.; Xing, H.

    2006-12-01

    Simulations using the Finite Element method are widely used in many engineering applications and for the solution of partial differential equations (PDEs). Computational models based on the solution of PDEs play a key role in earth systems simulations. We present numerical modelling of crustal fault systems where the dynamic elastic wave equation is solved using the Finite Element method. This is achieved using a high level computational modelling language, escript, available as open source software from ACcESS (Australian Computational Earth Systems Simulator), the University of Queensland. Escript is an advanced geophysical simulation software package developed at ACcESS which includes parallel equation solvers, data visualisation and data analysis software. The escript library was implemented to develop a flexible Finite Element model which reliably simulates the mechanism of faulting and the physics of earthquakes. Both 2D and 3D elastodynamic models are being developed to study the dynamics of crustal fault systems. Our final goal is to build a flexible model which can be applied to any fault system with user-defined geometry and input parameters. To study the physics of earthquake processes, two different time scales must be modelled, firstly the quasi-static loading phase which gradually increases stress in the system (~100years), and secondly the dynamic rupture process which rapidly redistributes stress in the system (~100secs). We will discuss the solution of the time-dependent elastic wave equation for an arbitrary fault system using escript. This involves prescribing the correct initial stress distribution in the system to simulate the quasi-static loading of faults to failure; determining a suitable frictional constitutive law which accurately reproduces the dynamics of the stick/slip instability at the faults; and using a robust time integration scheme. These dynamic models generate data and information that can be used for earthquake forecasting.

  15. Paleostress Determination Based on Multiple-Inverse Method using Calcite Twins and Fault-Slip Data in the East Walanae Fault Zone South Sulawesi, Indonesia

    NASA Astrophysics Data System (ADS)

    Jaya, Asri; Nishikawa, Osamu

    2013-04-01

    Paleostress reconstructions from calcite twin and fault-slip data were performed to constrain the activity of the East Walanae Fault (EWF) South Sulawesi, Indonesia. The multiple-inverse method, which has been widely used with fault-slip data, was applied to calcite twin data in this study. Both independent data sets yield consistent stress states and provides a reliable stress tensors (maximum and minimum principal stresses: ?1and ?3, and stress ratio: ?), a predominance of NE-SW trending ?1and vertical to moderately-south-plunging ?3 with generally low ?. These stress states could have activated the EWF as a reverse fault with a dextral shear component and account for contractional deformation structures and landform around the trace of the fault. Most of the calcite twins and mesoscale faults were activated during the latest stage of folding or later. Based on the morphology and width of twin lamellae in the carbonate rocks, twinning of calcite in the deformation zone along the EWF may have occurred under the temperature of 200° C or lower. Inferred paleostress states around the EWF were most likely generated under the tectonic conditions influenced by the collision of Sulawesi with the Australian fragments since the Late Miocene. Radiocarbon dating from sheared soil collected from the outcrop along a major fault yielded ages between 3050 cal BP and 3990 cal BP suggesting a present activity of the EWF.

  16. Structural system reliability calculation using a probabilistic fault tree analysis method

    NASA Technical Reports Server (NTRS)

    Torng, T. Y.; Wu, Y.-T.; Millwater, H. R.

    1992-01-01

    The development of a new probabilistic fault tree analysis (PFTA) method for calculating structural system reliability is summarized. The proposed PFTA procedure includes: developing a fault tree to represent the complex structural system, constructing an approximation function for each bottom event, determining a dominant sampling sequence for all bottom events, and calculating the system reliability using an adaptive importance sampling method. PFTA is suitable for complicated structural problems that require computer-intensive computer calculations. A computer program has been developed to implement the PFTA.

  17. Self-stabilizing byzantine-fault-tolerant clock synchronization system and method

    NASA Technical Reports Server (NTRS)

    Malekpour, Mahyar R. (Inventor)

    2012-01-01

    Systems and methods for rapid Byzantine-fault-tolerant self-stabilizing clock synchronization are provided. The systems and methods are based on a protocol comprising a state machine and a set of monitors that execute once every local oscillator tick. The protocol is independent of specific application specific requirements. The faults are assumed to be arbitrary and/or malicious. All timing measures of variables are based on the node's local clock and thus no central clock or externally generated pulse is used. Instances of the protocol are shown to tolerate bursts of transient failures and deterministically converge with a linear convergence time with respect to the synchronization period as predicted.

  18. Evolution of Ground Deformation Zone on Normal Fault Using Distinct Element Method and Centrifuge Modeling

    NASA Astrophysics Data System (ADS)

    Lyu, Jhen-Yi; Chang, Yu-Yi; Lee, Chung-Jung; Lin, Ming-Lang

    2015-04-01

    The depth and character of the overlying earth deposit contribute to fault rupture path. For cohesive soil, for instance, clay, tension cracks on the ground happen during faulting, limiting the propagation of fracture in soil mass. The cracks propagate downwards while the fracture induced by initial displacement of faulting propagates upwards. The connection of cracks and fracture will form a plane that is related to tri-shear zone. However the mechanism of the connection has not been discussed thoroughly. By obtaining the evolution of ground deformation zone we can understand mechanism of fault propagation and crack-fracture connection. A series of centrifuge tests and numerical modeling are conducted at this study with acceleration conditions of 40g, 50g, 80g and dip angle of 60° on normal faulting. The model is with total overburden thick, H, 0.2m, vertical displacement of moving wall, ∆H. At the beginning, hanging wall and the left-boundary wall moves along the plane of fault. When ∆H/H equals to 25%, both of the walls stop moving. We then can calculate the width of ground deformation in different depth of each model by a logic method. Models of this study consist of two different type overburden material to simulate sand and clay in situ. Different from finite element method, with application of distinct element method the mechanism of fault propagation in soil mass and the development of ground deformation zone can be observed directly in numerical analysis of faulting. The information of force and deformation in the numerical model are also easier to be obtained than centrifuge modeling. Therefore, we take the results of centrifuge modeling as the field outcrop then modify the micro-parameter of numerical analysis to make sure both of them have the same attitude. The results show that in centrifuge modeling narrower ground deformation zone appears in clayey overburden model as that of sandy overburden model is wider on footwall. Increasing the strength of clay will decreases the range of ground deformation. At higher acceleration condition the grounded formation zone become narrower; especially one that is near the fault plane. The results calculated from numerical model show agreement.

  19. Natural roller bearing fault detection by angular measurement of true instantaneous angular speed

    NASA Astrophysics Data System (ADS)

    Renaudin, L.; Bonnardot, F.; Musy, O.; Doray, J. B.; Rémond, D.

    2010-10-01

    The challenge in many production activities involving large mechanical devices like power transmissions consists in reducing the machine downtime, in managing repairs and in improving operating time. Most online monitoring systems are based on conventional vibration measurement devices for gear transmissions or bearings in mechanical components. In this paper, we propose an alternative way of bearing condition monitoring based on the instantaneous angular speed measurement. By the help of a large experimental investigation on two different applications, we prove that localized faults like pitting in bearing generate small angular speed fluctuations which are measurable with optical or magnetic encoders. We also emphasize the benefits of measuring instantaneous angular speed with the pulse timing method through an implicit angular sampling which ensures insensitivity to speed fluctuation. A wide range of operating conditions have been tested for the two applications with varying speed, load, external excitations, gear ratio, etc. The tests performed on an automotive gearbox or on actual operating vehicle wheels also establish the robustness of the proposed methodology. By the means of a conventional Fourier transform, angular frequency channels kinematically related to the fault periodicity show significant magnitude differences related to the damage severity. Sideband effects are evidently seen when the fault is located on rotating parts of the bearing due to load modulation. Additionally, slip effects are also suspected to be at the origin of enlargement of spectrum peaks in the case of double row bearings loaded in a pure radial direction.

  20. Handling Software Faults with Redundancy

    NASA Astrophysics Data System (ADS)

    Carzaniga, Antonio; Gorla, Alessandra; Pezzè, Mauro

    Software engineering methods can increase the dependability of software systems, and yet some faults escape even the most rigorous and methodical development process. Therefore, to guarantee high levels of reliability in the presence of faults, software systems must be designed to reduce the impact of the failures caused by such faults, for example by deploying techniques to detect and compensate for erroneous runtime conditions. In this chapter, we focus on software techniques to handle software faults, and we survey several such techniques developed in the area of fault tolerance and more recently in the area of autonomic computing. Since practically all techniques exploit some form of redundancy, we consider the impact of redundancy on the software architecture, and we propose a taxonomy centered on the nature and use of redundancy in software systems. The primary utility of this taxonomy is to classify and compare techniques to handle software faults.

  1. 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.

  2. A Virtual Sensor for Online Fault Detection of Multitooth-Tools

    PubMed Central

    Bustillo, Andres; Correa, Maritza; Reñones, Anibal

    2011-01-01

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

  3. Fault Detection In Manufacturing Cells Based On Three-Dimensional Visual Information

    NASA Astrophysics Data System (ADS)

    Bourne, David A.; Milligan, Robert; Wright, Paul K.

    1982-11-01

    A three dimensional representation of a part is reconstructed from multiple camera views. Measurements are then collected from this three dimensional data and can be used to detect faults in the manufacturing process. The manufacturing faults are detected as visual abnormalities in the final parts. These abnormalities correspond to error conditions in earlier phases of manufacturing and could represent equipment failure, equipment wear or the use of a faulty control algorithm. A gage station which collects visual information is discussed. The algorithm which converts the visual information into a three dimensional representation of the part is presented and compared to other similar reconstruction strategies. Once the data have been collected and reconstructed, measurements are taken and correlated with possible error conditions. New correlations between the part measurements and manufacturing errors can be added to the control system as problems occur. For example, hammer wear in an open-die forge can be discovered by measuring the length of a work piece after it was struck. Along with each casual relationship there is a suggested course of action which is intended to be an immediate remedy for the error condition. In the forge example, a simple corrective action would be to move the hammers closer together to account for their wear. This makes it possible for the overall system to approach immunity to catastrophic errors while minimizing the number of defective parts.

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

    PubMed

    Bustillo, Andres; Correa, Maritza; Reñones, Anibal

    2011-01-01

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

  5. Method for detecting toxic gases

    DOEpatents

    Stetter, J.R.; Zaromb, S.; Findlay, M.W. Jr.

    1991-10-08

    A method is disclosed which is capable of detecting low concentrations of a pollutant or other component in air or other gas. This method utilizes a combination of a heating filament having a catalytic surface of a noble metal for exposure to the gas and producing a derivative chemical product from the component. An electrochemical sensor responds to the derivative chemical product for providing a signal indicative of the product. At concentrations in the order of about 1-100 ppm of tetrachloroethylene, neither the heating filament nor the electrochemical sensor is individually capable of sensing the pollutant. In the combination, the heating filament converts the benzyl chloride to one or more derivative chemical products which may be detected by the electrochemical sensor. 6 figures.

  6. Research on fast fault identification method of 10.5 kV/1.5 kA superconducting fault current limiter

    NASA Astrophysics Data System (ADS)

    Zhang, Zhifeng; Sun, Qiang; Xiao, Liye; Liu, Daqian; Qiu, Ming; Qiu, Qinquan; Zhang, Guomin; Dai, Shaotao; Lin, Liangzhen

    2014-09-01

    Superconducting fault current limiter (SFCL) is a prospective electric devices connected in series in power grid to limit short-circuit current. A 10.5 kV/1.5 kA 3-phase SFCL with HTS coil of 6.24 mH was developed at IEECAS in China in 2005, which was operated in a local power grid in Hunan province for more than 11,000 h, and integrated lately in a superconducting power substation in Baiyin city in 2011 and is still running safely and reliably. In order to reduce the fault response time and enhance the performance of the SFCL, we analyzed the structure characteristics of the SFCL and discussed the variation of currents and voltages of the HTS coil and the bridge during the fault time. The simulation and tests results of power system validate the feasibility of the fast fault identification method.

  7. Performance analysis of a fault inferring nonlinear detection system algorithm with integrated avionics flight data

    NASA Technical Reports Server (NTRS)

    Caglayan, A. K.; Godiwala, P. M.; Morrell, F. R.

    1985-01-01

    This paper presents the performance analysis results of a fault inferring nonlinear detection system (FINDS) using integrated avionics sensor flight data for the NASA ATOPS B-737 aircraft in a Microwave Landing System (MLS) environment. First, an overview of the FINDS algorithm structure is given. Then, aircraft state estimate time histories and statistics for the flight data sensors are discussed. This is followed by an explanation of modifications made to the detection and decision functions in FINDS to improve false alarm and failure detection performance. Next, the failure detection and false alarm performance of the FINDS algorithm are analyzed by injecting bias failures into fourteen sensor outputs over six repetitive runs of the five minutes of flight data. Results indicate that the detection speed, failure level estimation, and false alarm performance show a marked improvement over the previously reported simulation runs. In agreement with earlier results, detection speed is faster for filter measurement sensors such as MLS than for filter input sensors such as flight control accelerometers. Finally, the progress in modifications of the FINDS algorithm design to accommodate flight computer constraints is discussed.

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

    NASA Technical Reports Server (NTRS)

    Ferrell, Bob A.; Lewis, Mark E.; Perotti, Jose M.; Brown, Barbara L.; Oostdyk, Rebecca L.; Goetz, Jesse W.

    2010-01-01

    This paper's main purpose is to detail issues and lessons learned regarding designing, integrating, and implementing Fault Detection Isolation and Recovery (FDIR) for Constellation Exploration Program (CxP) Ground Operations at Kennedy Space Center (KSC). Part of the0 overall implementation of National Aeronautics and Space Administration's (NASA's) CxP, FDIR is being implemented in three main components of the program (Ares, Orion, and Ground Operations/Processing). While not initially part of the design baseline for the CxP Ground Operations, NASA felt that FDIR is important enough to develop, that NASA's Exploration Systems Mission Directorate's (ESMD's) Exploration Technology Development Program (ETDP) initiated a task for it under their Integrated System Health Management (ISHM) research area. This task, referred to as the FDIIR project, is a multi-year multi-center effort. The primary purpose of the FDIR project is to develop a prototype and pathway upon which Fault Detection and Isolation (FDI) may be transitioned into the Ground Operations baseline. Currently, Qualtech Systems Inc (QSI) Commercial Off The Shelf (COTS) software products Testability Engineering and Maintenance System (TEAMS) Designer and TEAMS RDS/RT are being utilized in the implementation of FDI within the FDIR project. The TEAMS Designer COTS software product is being utilized to model the system with Functional Fault Models (FFMs). A limited set of systems in Ground Operations are being modeled by the FDIR project, and the entire Ares Launch Vehicle is being modeled under the Functional Fault Analysis (FFA) project at Marshall Space Flight Center (MSFC). Integration of the Ares FFMs and the Ground Processing FFMs is being done under the FDIR project also utilizing the TEAMS Designer COTS software product. One of the most significant challenges related to integration is to ensure that FFMs developed by different organizations can be integrated easily and without errors. Software Interface Control Documents (ICDs) for the FFMs and their usage will be addressed as the solution to this issue. In particular, the advantages and disadvantages of these ICDs across physically separate development groups will be delineated.

  9. The Amount and Preferred Orientation of Simple-shear in a Deformation Tensor: Implications for Detecting Shear Zones and Faults with GPS

    NASA Astrophysics Data System (ADS)

    Johnson, A. M.; Griffiths, J. H.

    2007-05-01

    At the 2005 Fall Meeting of the American Geophysical Union, Griffiths and Johnson [2005] introduced a method of extracting from the deformation-gradient (and velocity-gradient) tensor the amount and preferred orientation of simple-shear associated with 2-D shear zones and faults. Noting the 2-D is important because the shear zones and faults in Griffiths and Johnson [2005] were assumed non-dilatant and infinitely long, ignoring the scissors- like action along strike associated with shear zones and faults of finite length. Because shear zones and faults can dilate (and contract) normal to their walls and can have a scissors-like action associated with twisting about an axis normal to their walls, the more general method of detecting simple-shear is introduced and called MODES "method of detecting simple-shear." MODES can thus extract from the deformation-gradient (and velocity- gradient) tensor the amount and preferred orientation of simple-shear associated with 3-D shear zones and faults near or far from the Earth's surface, providing improvements and extensions to existing analytical methods used in active tectonics studies, especially strain analysis and dislocation theory. The derivation of MODES is based on one definition and two assumptions: by definition, simple-shear deformation becomes localized in some way; by assumption, the twirl within the deformation-gradient (or the spin within the velocity-gradient) is due to a combination of simple-shear and twist, and coupled with the simple- shear and twist is a dilatation of the walls of shear zones and faults. The preferred orientation is thus the orientation of the plane containing the simple-shear and satisfying the mechanical and kinematical boundary conditions. Results from a MODES analysis are illustrated by means of a three-dimensional diagram, the cricket- ball, which is reminiscent of the seismologist's "beach ball." In this poster, we present the underlying theory of MODES and illustrate how it works by analyzing the three- dimensional displacements measured with the Global Positioning System across the 1999 Chi-Chi earthquake ground rupture in Taiwan. In contrast to the deformation zone in the upper several meters of the ground below the surface detected by Yu et al. [2001], MODES determines the orientation and direction of shift of a shear zone representing the earthquake fault within the upper several hundred or thousand meters of ground below the surface. Thus, one value of the MODES analysis in this case is to provide boundary conditions for dislocation solutions for the subsurface shape of the main rupture during the earthquake.

  10. A new hybrid inversion method for parametric curved faults and its application to the 2008 Wenchuan (China) earthquake

    NASA Astrophysics Data System (ADS)

    Yin, Zhi; Xu, Caijun; Wen, Yangmao; Jiang, Guoyan; Fan, Qingbiao; Liu, Yang

    2016-02-01

    Planar faults are widely adopted during inversions to determine slip distributions and fault geometries using geodetic observations; however, little research has been conducted with respect to curved faults. We attribute this to the lack of an appropriate parameterized modeling method. In this paper, we present a curved-fault modeling method (CFMM) that describes a curved fault according to specific parameters, and we also develop a corresponding hybrid iterative inversion algorithm (HIIA) to perform inversions for parametric curved-fault geometries and slips. The results of the strike-component and dip-component synthetic tests show that a complex S-shaped fault surface and a circular slip distribution are successfully recovered, indicating the strong performance of the CFMM and HIIA methods. In addition, we describe and verify a scenario for determining the number of necessary geometrical parameters for the HIIA and examine the case study of the Wenchuan earthquake, which occurred on a complex listric fault surface. During the iteration process of the HIIA, both the fault geometry and slip distribution of the Beichuan and Pengguan faults converge to optimal values, indicating a Beichuan fault (BCF) model with a continuous listric shape and gradual steepening from the southwest to the northeast, which is highly consistent with geological survey results. Both the synthetic and real-world case studies show that the HIIA and the CMFF are superior to the conventional fault modeling method based on rectangular planes and that these models have the potential for use in more integrated research involving inversion studies, such as joint slip/curved-fault-geometry inversions that take into account data resolving power.

  11. An intelligent fault diagnosis method of rolling bearings based on regularized kernel Marginal Fisher analysis

    NASA Astrophysics Data System (ADS)

    Jiang, Li; Shi, Tielin; Xuan, Jianping

    2012-05-01

    Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.

  12. Evaluation of chiller modeling approaches and their usability for fault detection

    SciTech Connect

    Sreedharan, Priya

    2001-05-01

    Selecting the model is an important and essential step in model based fault detection and diagnosis (FDD). Several factors must be considered in model evaluation, including accuracy, training data requirements, calibration effort, generality, and computational requirements. All modeling approaches fall somewhere between pure first-principles models, and empirical models. The objective of this study was to evaluate different modeling approaches for their applicability to model based FDD of vapor compression air conditioning units, which are commonly known as chillers. Three different models were studied: two are based on first-principles and the third is empirical in nature. The first-principles models are the Gordon and Ng Universal Chiller model (2nd generation), and a modified version of the ASHRAE Primary Toolkit model, which are both based on first principles. The DOE-2 chiller model as implemented in CoolTools{trademark} was selected for the empirical category. The models were compared in terms of their ability to reproduce the observed performance of an older chiller operating in a commercial building, and a newer chiller in a laboratory. The DOE-2 and Gordon-Ng models were calibrated by linear regression, while a direct-search method was used to calibrate the Toolkit model. The ''CoolTools'' package contains a library of calibrated DOE-2 curves for a variety of different chillers, and was used to calibrate the building chiller to the DOE-2 model. All three models displayed similar levels of accuracy. Of the first principles models, the Gordon-Ng model has the advantage of being linear in the parameters, which allows more robust parameter estimation methods to be used and facilitates estimation of the uncertainty in the parameter values. The ASHRAE Toolkit Model may have advantages when refrigerant temperature measurements are also available. The DOE-2 model can be expected to have advantages when very limited data are available to calibrate the model, as long as one of the previously identified models in the CoolTools library matches the performance of the chiller in question.

  13. Real-time fault detection and isolation in biological wastewater treatment plants.

    PubMed

    Baggiani, F; Marsili-Libelli, S

    2009-01-01

    Automatic fault detection is becoming increasingly important in wastewater treatment plant operation, given the stringent treatment standards and the need to protect the investment costs from the potential damage of an unchecked fault propagating through the plant. This paper describes the development of a real-time Fault Detection and Isolation (FDI) system based on an adaptive Principal Component Analysis (PCA) algorithm, used to compare the current plant operation with a correct performance model based on a reference data set and the output of three ion-specific sensors (Hach-Lange gmbh, Düsseldorf, Germany): two Nitratax NOx UV sensors, in the denitrification tank and downstream of the oxidation tanks, where an Amtax ammonium-N sensor was also installed. The algorithm was initially developed in the Matlab environment and then ported into the LabView 8.20 (National Instruments, Austin, TX, USA) platform for real-time operation using a compact Field Point, a Programmable Automation Controller by National Instruments. The FDI was tested with a large set of operational data with 1 min sampling time from August 2007 through May 2008 from a full-scale plant. After describing the real-time version of the PCA algorithm, this was tested with nine months of operational data which were sequentially processes by the algorithm in order to simulate an on-line operation. The FDI performance was assessed by organizing the sequential data in two differing moving windows: a short-horizon window to test the response to single malfunctions and a longer time-horizon to simulate multiple unrepaired failures. In both cases the algorithm performance was very satisfactory, with a 100% failure detection in the short window case, which decreased to 84% in the long window setting. The short-window performance was very effective in isolating sensor failures and short duration disturbances such as spikes, whereas the long term horizon provided accurate detection of long-term drifts and proved robust enough to allow for some delay in failure recovery. The system robustness is based on the use of multiple statistics which proved instrumental in discriminating among the various causes of malfunctioning. PMID:19934517

  14. LiDAR detection of paleolandslides in the vicinity of the Suasselkä postglacial fault, Finnish Lapland

    NASA Astrophysics Data System (ADS)

    Sutinen, Raimo; Hyvönen, Eija; Kukkonen, Ilmo

    2014-04-01

    Spatial distribution of paleolandslides coincides with postglacial faults (PGFs) in northern Fennoscandia, yet the timing of the seismic events and associated paleoslides is insufficiently known. We applied airborne LiDAR (Light Detection And Ranging) to record distribution of paleolandslides possibly associated with the known Suasselkä postglacial fault in Kittilä, western Finnish Lapland. Landslide-buried organic material was radiocarbon dated to bring insight if the landslides were generated by single high-magnitude (Mw > 7) earthquake or if seismic activity has continued through the Holocene. We found a total of four sets of previously unrecognized paleolandslides to indicate fault-activity in the Kittilä area. The size of the landslide scarps exceeded up to 250-300 m in width, up to 600 m in length and 10 m in the height of the back-wall. A previously unrecognized (tentatively PGF) scarp, 6 m in height and in conjunction with paleolandslides, was found in granite rock west of the Kittilä village. The slide debris was similar to local tills, yet the maximum electrical conductivity (?a) anisotropy was incoherent to any of the ice-flow (morphological) stages recognized through LiDAR or known fabric-sedimentary stratigraphy. We found peat/gyttja beneath 6 m of slide debris which yielded radiocarbon (14C) age of 4400 ± 35 BP (cal. 5055 yr BP). Our previous finding of landslide-buried woody remnants of birch (Betula ssp.) yielded cal. 9730 yr BP in Kittilä. These ages suggest that seismic activity has continued at least 5 ky after deglaciation.

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

    NASA Technical Reports Server (NTRS)

    Simon, Dan; Simon, Donald L.

    2009-01-01

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

  16. Mine geophysics methods in studying the coal bearing rock mass condition in low magnitude tectonic fault zones

    NASA Astrophysics Data System (ADS)

    Alexeev, A. D.; Zhitlyonok, D. M.; Pitalenko, E. I.

    2003-04-01

    Disjunctive type tectonic faults are quite serious problem at underground coal winning. In the fault adjacent areas both coal seam and coal bearing rocks are usually essentially fractured that makes them less stable in coalfaces at underground mining. Some researchers have pointed out to enhanced stress state in these areas as well provided that loosening zones are absent. Coal seams are mostly inclined to disjunctive faults in Central region of Donets Coal Basin where tectonic processes were very intense. There are a lot of small faults with magnitudes close to seam thickness about 2 m in this region along with large thrust or fault disjunctives with stratigraphic magnitudes over 10 m (Dyleyev, Northern, Brunvald, Bulavin faults and others). Highest disjunctive dislocation is typical for coalfields near mines "Toretskaya" and "Novodzerzhinskaya", Coal Production Co. "Dzerzhinskugol", where dislocation density reaches about 8.5 faults per 1 km across the field. Small disjunctive faults often coincide with sites of sudden coal and gas outbursts, longwall inrushes, and poor support condition in development workings. It is known that affected zones on either side accommodate each disjunctive fault, these zones being distinctive for increased fissuring, higher stresses, coal and rocks differing strength. Affected zone width dependence on the fault parameters was determined using geological approach. Mine electrical survey and acoustical probing methods were used to study rock mass faulted condition in the vicinity of development workings and stopes intercepting low magnitude (below 5 m) disjunctive faults in coal field of mine "Toretskaya". These findings have allowed to establish a new fault magnitude dependence of rupture tectonic dislocation's affected zone width in the form of B = 3.2 H, where B is dislocation's affected zone width (m); H is the dislocation's stratigraphic magnitude (m). It was established as well that stress level in rock mass near disjunctive fault is 2 to 2.5 times higher than in undisturbed rock mass.

  17. Method for detecting toxic gases

    DOEpatents

    Stetter, Joseph R. (Naperville, IL); Zaromb, Solomon (Hinsdale, IL); Findlay, Jr., Melvin W. (Bolingbrook, IL)

    1991-01-01

    A method capable of detecting low concentrations of a pollutant or other component in air or other gas, utilizing a combination of a heating filament having a catalytic surface of a noble metal for exposure to the gas and producing a derivative chemical product from the component, and an electrochemical sensor responsive to the derivative chemical product for providing a signal indicative of the product. At concentrations in the order of about 1-100 ppm of tetrachloroethylene, neither the heating filament nor the electrochemical sensor is individually capable of sensing the pollutant. In the combination, the heating filament converts the benzyl chloride to one or more derivative chemical products which may be detected by the electrochemical sensor.

  18. A weighted multi-scale morphological gradient filter for rolling element bearing fault detection.

    PubMed

    Li, Bing; Zhang, Pei-lin; Wang, Zheng-jun; Mi, Shuang-shan; Liu, Dong-sheng

    2011-10-01

    This paper presents a novel signal processing scheme, named the weighted multi-scale morphological gradient filter (WMMG), for rolling element bearing fault detection. The WMMG can depress the noise at large scale and preserve the impulsive shape details at small scale. Both a simulated signal and vibration signals from a bearing test rig are employed to evaluate the performance of the proposed technique. The traditional envelope analysis and a multi-scale enveloping spectrogram algorithm combining continuous wavelet transform and envelope analysis (WT-EA) are also studied and compared with the presented WMMG. Experimental results have demonstrated the effectiveness of the WMMG to extract the impulsive components from the raw vibration signal with strong background noise. We also investigated the classification performance on identifying bearing faults based on the WMMG and statistical parameters with varied noise levels. Application results reveal that the WMMG achieves the same or better performance as EA and WT-EA. Meanwhile, the WMMG requires low computation cost and is very suitable for on-line condition monitoring of bearing operating states. PMID:21723552

  19. Fault detection of a roller-bearing system through the EMD of a wavelet denoised signal.

    PubMed

    Ahn, Jong-Hyo; Kwak, Dae-Ho; Koh, Bong-Hwan

    2014-01-01

    This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matrices from healthy and damaged bearings exhibit different POV profiles, which can be a damage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility of wavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault. PMID:25196008

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

    NASA Technical Reports Server (NTRS)

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

    2003-01-01

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

  1. Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal

    PubMed Central

    Ahn, Jong-Hyo; Kwak, Dae-Ho; Koh, Bong-Hwan

    2014-01-01

    This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matrices from healthy and damaged bearings exhibit different POV profiles, which can be a damage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility of wavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault. PMID:25196008

  2. Bacteria detection instrument and method

    NASA Technical Reports Server (NTRS)

    Renner, W.; Fealey, R. D. (inventors)

    1972-01-01

    A method and apparatus for screening a sample fluid for bacterial presence are disclosed wherein the fluid sample is mixed with culture media of sufficient quantity to permit bacterial growth in order to obtain a test solution. The concentration of oxygen dissolved in the test solution is then monitored using the potential difference between a reference electrode and a noble metal electrode which are in contact with the test solution. The change in oxygen concentration which occurs during a period of time as indicated by the electrode potential difference is compared with a detection criterion which exceeds the change which would occur absent bacteria.

  3. In-process fault detection for textile fabric production: onloom imaging

    NASA Astrophysics Data System (ADS)

    Neumann, Florian; Holtermann, Timm; Schneider, Dorian; Kulczycki, Ashley; Gries, Thomas; Aach, Til

    2011-05-01

    Constant and traceable high fabric quality is of high importance both for technical and for high-quality conventional fabrics. Usually, quality inspection is carried out by trained personal, whose detection rate and maximum period of concentration are limited. Low resolution automated fabric inspection machines using texture analysis were developed. Since 2003, systems for the in-process inspection on weaving machines ("onloom") are commercially available. With these defects can be detected, but not measured quantitative precisely. Most systems are also prone to inevitable machine vibrations. Feedback loops for fault prevention are not established. Technology has evolved since 2003: Camera and computer prices dropped, resolutions were enhanced, recording speeds increased. These are the preconditions for real-time processing of high-resolution images. So far, these new technological achievements are not used in textile fabric production. For efficient use, a measurement system must be integrated into the weaving process; new algorithms for defect detection and measurement must be developed. The goal of the joint project is the development of a modern machine vision system for nondestructive onloom fabric inspection. The system consists of a vibration-resistant machine integration, a high-resolution machine vision system, and new, reliable, and robust algorithms with quality database for defect documentation. The system is meant to detect, measure, and classify at least 80 % of economically relevant defects. Concepts for feedback loops into the weaving process will be pointed out.

  4. Explosives detection system and method

    DOEpatents

    Reber, Edward L. (Idaho Falls, ID); Jewell, James K. (Idaho Falls, ID); Rohde, Kenneth W. (Idaho Falls, ID); Seabury, Edward H. (Idaho Falls, ID); Blackwood, Larry G. (Idaho Falls, ID); Edwards, Andrew J. (Idaho Falls, ID); Derr, Kurt W. (Idaho Falls, ID)

    2007-12-11

    A method of detecting explosives in a vehicle includes providing a first rack on one side of the vehicle, the rack including a neutron generator and a plurality of gamma ray detectors; providing a second rack on another side of the vehicle, the second rack including a neutron generator and a plurality of gamma ray detectors; providing a control system, remote from the first and second racks, coupled to the neutron generators and gamma ray detectors; using the control system, causing the neutron generators to generate neutrons; and performing gamma ray spectroscopy on spectra read by the gamma ray detectors to look for a signature indicative of presence of an explosive. Various apparatus and other methods are also provided.

  5. Fault detection in heavy duty wheels by advanced vibration processing techniques and lumped parameter modeling

    NASA Astrophysics Data System (ADS)

    Malago`, M.; Mucchi, E.; Dalpiaz, G.

    2016-03-01

    Heavy duty wheels are used in applications such as automatic vehicles and are mainly composed of a polyurethane tread glued to a cast iron hub. In the manufacturing process, the adhesive application between tread and hub is a critical assembly phase, since it is completely made by an operator and a contamination of the bond area may happen. Furthermore, the presence of rust on the hub surface can contribute to worsen the adherence interface, reducing the operating life. In this scenario, a quality control procedure for fault detection to be used at the end of the manufacturing process has been developed. This procedure is based on vibration processing techniques and takes advantages of the results of a lumped parameter model. Indicators based on cyclostationarity can be considered as key parameters to be adopted in a monitoring test station at the end of the production line due to their not deterministic characteristics.

  6. Evaluation of an expert system for fault detection, isolation, and recovery in the manned maneuvering unit

    NASA Technical Reports Server (NTRS)

    Rushby, John; Crow, Judith

    1990-01-01

    The authors explore issues in the specification, verification, and validation of artificial intelligence (AI) based software, using a prototype fault detection, isolation and recovery (FDIR) system for the Manned Maneuvering Unit (MMU). They use this system as a vehicle for exploring issues in the semantics of C-Language Integrated Production System (CLIPS)-style rule-based languages, the verification of properties relating to safety and reliability, and the static and dynamic analysis of knowledge based systems. This analysis reveals errors and shortcomings in the MMU FDIR system and raises a number of issues concerning software engineering in CLIPs. The authors came to realize that the MMU FDIR system does not conform to conventional definitions of AI software, despite the fact that it was intended and indeed presented as an AI system. The authors discuss this apparent disparity and related questions such as the role of AI techniques in space and aircraft operations and the suitability of CLIPS for critical applications.

  7. Procedures for on-line detection of sensor and operating faults in solar energy systems

    SciTech Connect

    Duff, W.S.; Millard, R.A.

    1980-01-01

    Every 10 minutes, 100 channels of directly measured quantities, integrated and counted flows and fluxes, and calculated energy quantities from Colorado State University Solar House I are recorded on magnetic tape and passed on to a minicomputer with dual floppy disc, printer, and plotter. The magnetic tape output is processed in monthly blocks to provide detailed monthly, daily, and hourly reduced data summaries. The minicomputer is used to display real time operating data, print out hourly performance summaries, and collect statistics on sensor and operating system status. These statistics are then analyzed to detect possible sensor and operating faults in the system so they may be quickly recognized and corrected. The procedures that have been developed to perform these analyses are discussed, and their use and effectiveness under actual operating conditions is illustrated.

  8. Fault Detection and Safety in Closed-Loop Artificial Pancreas Systems

    PubMed Central

    2014-01-01

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

  9. Particle Filters for Real-Time Fault Detection in Planetary Rovers

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

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

    PubMed

    Amozegar, M; Khorasani, K

    2016-04-01

    In this paper, a new approach for Fault Detection and Isolation (FDI) of gas turbine engines is proposed by developing an ensemble of dynamic neural network identifiers. For health monitoring of the gas turbine engine, its dynamics is first identified by constructing three separate or individual dynamic neural network architectures. Specifically, a dynamic multi-layer perceptron (MLP), a dynamic radial-basis function (RBF) neural network, and a dynamic support vector machine (SVM) are trained to individually identify and represent the gas turbine engine dynamics. Next, three ensemble-based techniques are developed to represent the gas turbine engine dynamics, namely, two heterogeneous ensemble models and one homogeneous ensemble model. It is first shown that all ensemble approaches do significantly improve the overall performance and accuracy of the developed system identification scheme when compared to each of the stand-alone solutions. The best selected stand-alone model (i.e., the dynamic RBF network) and the best selected ensemble architecture (i.e., the heterogeneous ensemble) in terms of their performances in achieving an accurate system identification are then selected for solving the FDI task. The required residual signals are generated by using both a single model-based solution and an ensemble-based solution under various gas turbine engine health conditions. Our extensive simulation studies demonstrate that the fault detection and isolation task achieved by using the residuals that are obtained from the dynamic ensemble scheme results in a significantly more accurate and reliable performance as illustrated through detailed quantitative confusion matrix analysis and comparative studies. PMID:26881999

  11. Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for Formation Flying of Satellites

    NASA Astrophysics Data System (ADS)

    Valdes, A.; Khorasani, K.

    The main objective of this paper is to develop a dynamic neural network-based fault detection and isolation (FDI) scheme for the Pulsed Plasma Thrusters (PPTs) that are used in the Attitude Control Subsystem (ACS) of satellites that are tasked to perform a formation flying mission. By using data collected from the relative attitudes of the formation flying satellites our proposed "High Level" FDI scheme can detect the pair of thrusters which is faulty, however fault isolation cannot be accomplished. Based on the "High Level" FDI scheme and the DNN-based "Low Level" FDI scheme developed earlier by the authors, an "Integrated" DNN-based FDI scheme is then proposed. To demonstrate the FDI capabilities of the proposed schemes various fault scenarios are simulated.

  12. Fault-tolerant quantum computation with a soft-decision decoder for error correction and detection by teleportation

    PubMed Central

    Goto, Hayato; Uchikawa, Hironori

    2013-01-01

    Fault-tolerant quantum computation with quantum error-correcting codes has been considerably developed over the past decade. However, there are still difficult issues, particularly on the resource requirement. For further improvement of fault-tolerant quantum computation, here we propose a soft-decision decoder for quantum error correction and detection by teleportation. This decoder can achieve almost optimal performance for the depolarizing channel. Applying this decoder to Knill's C4/C6 scheme for fault-tolerant quantum computation, which is one of the best schemes so far and relies heavily on error correction and detection by teleportation, we dramatically improve its performance. This leads to substantial reduction of resources. PMID:23784512

  13. Fault-tolerant quantum computation with a soft-decision decoder for error correction and detection by teleportation.

    PubMed

    Goto, Hayato; Uchikawa, Hironori

    2013-01-01

    Fault-tolerant quantum computation with quantum error-correcting codes has been considerably developed over the past decade. However, there are still difficult issues, particularly on the resource requirement. For further improvement of fault-tolerant quantum computation, here we propose a soft-decision decoder for quantum error correction and detection by teleportation. This decoder can achieve almost optimal performance for the depolarizing channel. Applying this decoder to Knill's C4/C6 scheme for fault-tolerant quantum computation, which is one of the best schemes so far and relies heavily on error correction and detection by teleportation, we dramatically improve its performance. This leads to substantial reduction of resources. PMID:23784512

  14. Methods for locating ground faults and insulation degradation condition in energy conversion systems

    DOEpatents

    Agamy, Mohamed; Elasser, Ahmed; Galbraith, Anthony William; Harfman Todorovic, Maja

    2015-08-11

    Methods for determining a ground fault or insulation degradation condition within energy conversion systems are described. A method for determining a ground fault within an energy conversion system may include, in part, a comparison of baseline waveform of differential current to a waveform of differential current during operation for a plurality of DC current carrying conductors in an energy conversion system. A method for determining insulation degradation within an energy conversion system may include, in part, a comparison of baseline frequency spectra of differential current to a frequency spectra of differential current transient at start-up for a plurality of DC current carrying conductors in an energy conversion system. In one embodiment, the energy conversion system may be a photovoltaic system.

  15. 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.

  16. Wire detecting apparatus and method

    SciTech Connect

    Kronberg, J.W.

    1991-12-31

    This invention is comprised of an apparatus and method that combines a signal generator and a passive signal receiver to detect and record the path of partially or completely concealed electrical wiring without disturbing the concealing surface. The signal generator applies a series of electrical pulses to the selected wiring of interest. The applied pulses create a magnetic field about the wiring that can be detected by a coil contained within the signal receiver. An audible output connected to the receiver and driven by the coil reflects the receiver`s position with respect to the wiring. The receiver`s audible signal is strongest when the receiver is directly above the wiring and the long axis of the receiver`s coil is parallel to the wiring. A marking means is mounted on the receiver to mark the location of the wiring as the receiver is directed over the wiring`s concealing surface. Numerous marks made on various locations of the concealing surface will trace the path of the wiring of interest.

  17. Immunofluorescence detection methods using microspheres

    NASA Astrophysics Data System (ADS)

    Szurdoki, Ferenc; Michael, Karri L.; Agrawal, Divya; Taylor, Laura C.; Schultz, Sandra L.; Walt, David R.

    1999-01-01

    Microsphere-based immunoassays were devised for compounds of agricultural and biomedical interest (e.g., digoxin, theophylline, and zearalenone). Commercially available microspheres with surface functional groups for chemical derivatization were used as solid carriers. After immobilizing the target substances, the surface of the haptenized microspheres was blocked by a protein to reduce aspecific binding. Competitive immunoassays were performed using the functionalized microspheres and antibodies labeled with horseradish peroxidase. Immunofluorescence signal amplification was achieved by enzyme-catalyzed reporter deposition (CARD). An epifluorescence microscope, a CCD camera interfaced with a computer, and microscopy image analysis software were employed for quantitative detection of fluorescent light emitted from individual microspheres. Integration of several such immunoassays and application of an optical encoding method enabled multianalyte determination. These immunoassays can also be utilized in an immunosensor array format. This immunoarray format could facilitate miniaturization and automation of multianalyte immunoassays.

  18. A decentralized fault detection and isolation scheme for spacecraft: bridging the gap between model-based fault detection and isolation research and practice

    NASA Astrophysics Data System (ADS)

    Indra, S.; Travé-Massuyès, L.; Chanthery, E.

    2013-12-01

    This paper introduces a decentralized fault diagnosis and isolation (FDI) architecture for spacecraft and applies it to the attitude determination and control system (ADCS) of a satellite. A system is decomposed into functional subsystems. The architecture is composed of local diagnosers for subsystems which work with local models. Fault ambiguities due to interactions between subsystems are resolved at a higher level by a supervisor, which combines the partial view of the local diagnosers and performs isolation on request. The architecture is hierarchically scalable. The structure of the ADCS is modeled as constraints and variables and used to demonstrate the decentralized architecture.

  19. 3-D InSAR Phase Unwrapping with Extended Kalman Filter: Applications to interseismic deformation detection across the North Anatolian and San Andreas Fault zones

    NASA Astrophysics Data System (ADS)

    Havazli, E.; Wdowinski, S.; Osmanoglu, B.

    2014-12-01

    Interferometric Synthetic Aperture Radar (InSAR) is a method that allows researchers to map elevations, analyze surface deformation and even detect ground water level changes. The InSAR phase measurements are wrapped between 0 and 2? and therefore have to be unwrapped to reveal the full scale of the observations. Even though there are algorithms for finding discrete irrotational fields among neighboring pixels in two-dimensions, a three dimensional unwrapping approach is important as it can constrain the solution of our data to a more robust and accurate state. We developed a 3-D unwrapping algorithm based on an Extended Kalman Filter (EKF) that is capable of simultaneously filtering, unwrapping and inverting multiple interferograms to obtain a DEM or deformation map. The method is based on a path-following algorithm that unwraps the dataset starting from a reference point and moves to the next-highest quality neighboring point. The EKF algorithm allows us to better resolve unwrapping problems, especially in vegetated areas, which tend to be decorrelated, and hence obtain more accurate results. In this study we apply our 3-D EKF unwrapping algorithm to North Anatolian and San Andreas fault zones in order to detect interseismic crustal movement across these two major fault systems. For the North Anatolian Fault we processed 37 Envisat scenes that covers the Ismetpasa segment of the fault, and generated 237 interferograms. The generated interferograms are used with both EKF and SBAS algorithms to estimate the deformation in the area. Our previous study of this segment based on the SBAS technique revealed that the Ismetpasa segment creeps at a rate of 8 mm/yr. For the San Andreas Fault (SAF) we processed 37 descending Envisat ASAR scenes acquired between November 2005 and October 2010. Our area of interest includes the central SAF near its intersection with the Garlock Fault. Initial results show deformation across the fault but the results have low fit to the data and further work is needed. In this presentation, we will also present the results from the SAF using SBAS and EKF.

  20. A wavelet decomposition analysis of vibration signal for bearing fault detection

    NASA Astrophysics Data System (ADS)

    Nizwan, C. K. E.; Ong, S. A.; Yusof, M. F. M.; Baharom, M. Z.

    2013-12-01

    This paper presents a study of vibrational signal analysis for bearing fault detection using Discrete Wavelet Transform (DWT). In this study, the vibration data was acquired from three different types of bearing defect i.e. corroded, outer race defect and point defect. The experiments were carried out at three different speeds which are 10%, 50% and 90% of the maximum motor speed. The time domain vibration data measured from accelerometer was then transformed into frequency domain using a frequency analyzer in order to study the frequency characteristics of the signal. The DWT was utilized to decomposed signal at different frequency scale. Then, root mean square (RMS) for every decomposition level was calculated to detect the defect features in vibration signals by referring to the trend of vibrational energy retention at every decomposition. Based on the result, the defective bearings show significant deviation in retaining RMS value after a few levels of decomposition. The findings indicate that Wavelet decomposition analysis can be used to develop an effective bearing condition monitoring tool. This signal processing analysis is recommended in on-line monitoring while the machine is on operation.

  1. Application of an inverse method for calculating three-dimensional fault geometries and clip vectors, Nun River Field, Nigeria

    SciTech Connect

    Kerr, H.G.; White, N.

    1996-03-01

    A general, automatic method for determining the three-dimensional geometry of a normal fault of any shape and size is applied to a three-dimensional seismic reflection data set from the Nun River field, Nigeria. In addition to calculating fault geometry, the method also automatically retrieves the extension direction without requiring any previous information about either the fault shape or the extension direction. Solutions are found by minimizing the misfit between sets of faults that are calculated from the observed geometries of two or more hanging-wall beds. In the example discussed here, the predicted fault surface is in excellent agreement with the shape of the seismically imaged fault. Although the calculated extension direction is oblique to the average strike of the fault, the value of this parameter is not well resolved. Our approach differs markedly from standard section-balancing models in two important ways. First, we do not assume that the extension direction is known, and second, the use of inverse theory ensures that formal confidence bounds can be determined for calculated fault geometries. This ability has important implications for a range of geological problems encountered at both exploration and production scales. In particular, once the three-dimensional displacement field has been constrained, the difficult but important problem of three-dimensional palinspastic restoration of hanging-wall structures becomes tractable.

  2. Non-adaptive fault diagnosis for low-degree networks via lightpath probing method

    NASA Astrophysics Data System (ADS)

    Zhang, Hang; Li, Yanhe; Guo, Yili; Zhang, Hanyi; Zheng, Xiaoping

    2008-11-01

    This paper considers the fault-diagnosis problem for low-degree all-optical networks. Failure-detection schemes which subject to topological constraints are proposed. Optical probe signals are sent in parallel along a set of designed lightpaths. The network health state is derived from the results of this end-to-end test (i.e., probe syndromes). The design objectives are to minimize the diagnosis cost which is represented by the number of probes. We obtain a lower bound of the number of the probes to identify any single link failure and implement this scheme in several networks as well.

  3. The correlation of 2D-resistivity and magnetic methods in fault verification at northern Sumatra, Indonesia

    NASA Astrophysics Data System (ADS)

    Kamaruddin, Nur Aminuda; Saad, Rosli; Nordiana, M. M.; Azwin, I. N.

    2015-04-01

    The Great Sumatra Fault system was split into two sub-parallel lines or segments at the Northern Sumatra. This event is one of the impacts of powerful earthquakes that hit Sumatra Island especially one that occurred in 2004. These two sub-parallel segments known as Aceh and Seulimeum fault. The study is focused on the Seulimeum fault and two geophysical methods chosen aimed to compare and verified the result obtained respectively. 2-D resistivity method is a common geophysical method used in determination of near surface structures such as faults, cavities, voids and sinkholes. Meanwhile, the magnetic method often chosen to delineate subsurface structures, determine depth of magnetic source bodies and possibly sediment thickness. Three survey lines of resistivity method and randomly magnetic stations were carried out covering Krueng district. The resistivity data processed using Res2Dinv and result presented using Surfer software. The fault identified by the contrast of low and high resistivity value. Meanwhile, the magnetic data were presented in magnetic residual contour map and the extended fault system is suspected represent by the contrast value of the magnetic anomalies. Within suspected fault zone, the results of resistivity are tally with magnetic result.

  4. Particle detection systems and methods

    DOEpatents

    Morris, Christopher L.; Makela, Mark F.

    2010-05-11

    Techniques, apparatus and systems for detecting particles such as muons and neutrons. In one implementation, a particle detection system employs a plurality of drift cells, which can be for example sealed gas-filled drift tubes, arranged on sides of a volume to be scanned to track incoming and outgoing charged particles, such as cosmic ray-produced muons. The drift cells can include a neutron sensitive medium to enable concurrent counting of neutrons. The system can selectively detect devices or materials, such as iron, lead, gold, uranium, plutonium, and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can concurrently detect any unshielded neutron sources occupying the volume from neutrons emitted therefrom. If necessary, the drift cells can be used to also detect gamma rays. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

  5. Revised and Improved Fault Maps of Washoe County, Nevada using Light Detecting and Ranging (LiDAR) Imagery

    NASA Astrophysics Data System (ADS)

    Brailo, C.; Kent, G.; Wesnousky, S. G.; Kell, A. M.; Pierce, I.; Ruhl, C. J.; Smith, K. D.

    2014-12-01

    A new Light Detection and Ranging (LiDAR) survey images the fault network of Truckee Meadows region of western Nevada, including the Reno/Sparks metropolitan area in Washoe County. The airborne LiDAR imagery (1485 sq. km) is being used to create high quality bare-earth digital elevation models that were previously unattainable in vegetated, populated or alpine terrain. LiDAR gives us an opportunity to improve fault maps that may be outdated or incomplete in the area. Here we show LiDAR imagery of a large section of Washoe County and highlight areas where this imagery may be useful in revising current fault maps. Conflicting stress regimes, with strike-slip regions overlapping extensional domains in the Walker Lane Deformation Belt, complicate regional tectonics of Washoe County. In this region east of the Sierra Nevada batholith, approximately 20-25% of Pacific-North American plate motion (mostly right-lateral shear) is accommodated along the Walker Lane. There is ample evidence of Magnitude 6-7 earthquakes in or surrounding the Truckee Meadows region as recently as the late 1800s and it is possible that earthquakes of this size may occur here in the near future. Accurate mapping of faults and associated earthquake hazards in populated areas is critically important for earthquake mitigation and preparedness, and furthers our understanding of regional tectonics. The new LiDAR data confirms the presence of many previously mapped faults, simplifies areas that may be presently over-complicated by current maps, and identifies faults that were previously unmapped. Current and future research will also focus on dating of glacial outwash terraces and alluvial fans, particularly in the Mogul area and Mt. Rose pediment. Coupled with comprehensive fault maps and displacement measurements improved by this new LiDAR dataset, these data may allow researchers to get more accurate slip rate estimates on faults in this region, and may support the hypothesis that some faults in the Washoe County region are more active than previously reported.

  6. Detection of active faults using EMR-Technique and Cerescope at Landau area in central Upper Rhine Graben, SW Germany

    NASA Astrophysics Data System (ADS)

    Hagag, Wael; Obermeyer, Hennes

    2016-01-01

    Two conjugate sets of active faults oriented NNE-SSW and NNW-SSE have been detected at Landau area in SW Germany. These faults follow the old trends of the rift-related structures predominating in the Upper Rhine Graben (URG), which originated during Late Eocene-Miocene time. Linear and horizontal measurements were performed by using the Cerescope device and interpreted, applying the Electromagnetic Radiation (EMR) Technique. Linear EMR-profiles were helpful for mapping active faults, while the main horizontal stress (σH, N to NNE) was easily identified with EMR-horizontal measurements. Reactivation of rift-related structures of the Upper Rhine Graben at Landau area produces a new system of active shallow fractures following old trends, and has been detected through the present study by Cerescope applying the EMR-Technique. The present results imply that the Enhanced Geothermal System (EGS) to the south of Landau has a great impact on reactivation of the pre-existing rift-related faults by mechanical hydro-fracturing occurring within the reservoir rocks underneath the area.

  7. Method For Detecting Biological Agents

    DOEpatents

    Chen, Liaohai (Los Alamos, NM); McBranch, Duncan W. (Santa Fe, NM); Wang, Hsing-Lin (Los Alamos, NM); Whitten, David G. (Santa Fe, NM)

    2005-12-27

    A sensor is provided including a polymer capable of having an alterable measurable property from the group of luminescence and electrical conductivity, the polymer having an intermediate combination of a recognition element, a tethering element and a property-altering element bound thereto and capable of altering the measurable property, the intermediate combination adapted for subsequent separation from the polymer upon exposure to an agent having an affinity for binding to the recognition element whereupon the separation of the intermediate combination from the polymer results in a detectable change in the alterable measurable property, and, detecting said detectable change in the alterable measurable property.

  8. Accurate Monitoring and Fault Detection in Wind Measuring Devices through Wireless Sensor Networks

    PubMed Central

    Khan, Komal Saifullah; Tariq, Muhammad

    2014-01-01

    Many wind energy projects report poor performance as low as 60% of the predicted performance. The reason for this is poor resource assessment and the use of new untested technologies and systems in remote locations. Predictions about the potential of an area for wind energy projects (through simulated models) may vary from the actual potential of the area. Hence, introducing accurate site assessment techniques will lead to accurate predictions of energy production from a particular area. We solve this problem by installing a Wireless Sensor Network (WSN) to periodically analyze the data from anemometers installed in that area. After comparative analysis of the acquired data, the anemometers transmit their readings through a WSN to the sink node for analysis. The sink node uses an iterative algorithm which sequentially detects any faulty anemometer and passes the details of the fault to the central system or main station. We apply the proposed technique in simulation as well as in practical implementation and study its accuracy by comparing the simulation results with experimental results to analyze the variation in the results obtained from both simulation model and implemented model. Simulation results show that the algorithm indicates faulty anemometers with high accuracy and low false alarm rate when as many as 25% of the anemometers become faulty. Experimental analysis shows that anemometers incorporating this solution are better assessed and performance level of implemented projects is increased above 86% of the simulated models. PMID:25421739

  9. Accurate monitoring and fault detection in wind measuring devices through wireless sensor networks.

    PubMed

    Khan, Komal Saifullah; Tariq, Muhammad

    2014-01-01

    Many wind energy projects report poor performance as low as 60% of the predicted performance. The reason for this is poor resource assessment and the use of new untested technologies and systems in remote locations. Predictions about the potential of an area for wind energy projects (through simulated models) may vary from the actual potential of the area. Hence, introducing accurate site assessment techniques will lead to accurate predictions of energy production from a particular area. We solve this problem by installing a Wireless Sensor Network (WSN) to periodically analyze the data from anemometers installed in that area. After comparative analysis of the acquired data, the anemometers transmit their readings through a WSN to the sink node for analysis. The sink node uses an iterative algorithm which sequentially detects any faulty anemometer and passes the details of the fault to the central system or main station. We apply the proposed technique in simulation as well as in practical implementation and study its accuracy by comparing the simulation results with experimental results to analyze the variation in the results obtained from both simulation model and implemented model. Simulation results show that the algorithm indicates faulty anemometers with high accuracy and low false alarm rate when as many as 25% of the anemometers become faulty. Experimental analysis shows that anemometers incorporating this solution are better assessed and performance level of implemented projects is increased above 86% of the simulated models. PMID:25421739

  10. User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program

    NASA Technical Reports Server (NTRS)

    Caglayan, A. K.; Godiwala, P. M.; Satz, H. S.

    1988-01-01

    Described are the operation and internal structure of the computer program FINDS (Fault Inferring Nonlinear Detection System). The FINDS algorithm is designed to provide reliable estimates for aircraft position, velocity, attitude, and horizontal winds to be used for guidance and control laws in the presence of possible failures in the avionics sensors. The FINDS algorithm was developed with the use of a digital simulation of a commercial transport aircraft and tested with flight recorded data. The algorithm was then modified to meet the size constraints and real-time execution requirements on a flight computer. For the real-time operation, a multi-rate implementation of the FINDS algorithm has been partitioned to execute on a dual parallel processor configuration: one based on the translational dynamics and the other on the rotational kinematics. The report presents an overview of the FINDS algorithm, the implemented equations, the flow charts for the key subprograms, the input and output files, program variable indexing convention, subprogram descriptions, and the common block descriptions used in the program.

  11. Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System.

    PubMed

    Ouyang, Kesai; Lu, Siliang; Zhang, Shangbin; Zhang, Haibin; He, Qingbo; Kong, Fanrang

    2015-01-01

    The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy. PMID:26343657

  12. Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System

    PubMed Central

    Ouyang, Kesai; Lu, Siliang; Zhang, Shangbin; Zhang, Haibin; He, Qingbo; Kong, Fanrang

    2015-01-01

    The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy. PMID:26343657

  13. An adaptive disturbance accommodation approach for robust control and fault detection in uncertain stochastic systems

    NASA Astrophysics Data System (ADS)

    George, Jemin

    The detailed formulation and analysis of a robust control scheme for multi-input multi-output uncertain stochastic systems known as the disturbance accommodating controller is presented. Instead of dealing with system uncertainties and external disturbances separately, the disturbance accommodating control scheme lumps the overall effects of these errors in a to-be-determined model-error vector, and then utilizes a Kalman filter in the feedback loop for simultaneously estimating the system states and the model-error vector from noisy measurements. The estimated states are then used to develop a nominal control law while the estimated model-error vector is used as a signal synthesis adaptive correction to the nominal control input to minimize the adverse effects of system uncertainties and the external disturbances. Since the model-error dynamics is unknown, the process noise covariance associated with the model-error dynamics is used to empirically tune the Kalman filter to yield accurate estimates. This dissertation presents a detailed stability analysis, which examines the explicit dependency of the controlled system's closed-loop performance on the assumed process noise covariance. Development of a robust adaptive disturbance accommodating controller for multi-input multi-output uncertain stochastic systems based on a stochastic adaptive scheme for selecting the appropriate process noise covariance that would guarantee closed-loop stability is presented. The presented approach concurrently tackles the problem of designing robust controllers and estimators for uncertain stochastic systems by indirectly adapting for the estimator gain though updating the estimator parameters such as the process noise covariance matrix in real-time. As presented here, the proposed adaptive disturbance accommodating control scheme can be easily extended to develop robust controllers for saturating systems by adapting for the controller gains along with the estimator parameters. In nonlinear stochastic systems, the proposed adaptive disturbance accommodating control scheme can be exploited for complexity mitigation as well as disturbance attenuation. Since the disturbance accommodating controller is extensively used for fault accommodation, robust fault detection and identification scheme based on the disturbance accommodating control theory is also presented. Though the results presented in this dissertation are supported by detailed mathematical proofs, several numerical simulations are also presented here to further validate the efficiency and applicability of the proposed approaches.

  14. LMD method and multi-class RWSVM of fault diagnosis for rotating machinery using condition monitoring information.

    PubMed

    Liu, Zhiwen; Chen, Xuefeng; He, Zhengjia; Shen, Zhongjie

    2013-01-01

    Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM. PMID:23881133

  15. An Investigation of a Control Method for Fault-Mode Inverters to Drive Induction Motors

    NASA Astrophysics Data System (ADS)

    Yamato, Masanori; Sato, Yukihiko

    In this paper, a control method in fault-mode for three-phase voltage source inverters to drive AC motors is proposed. The proposed method does not require any additional switching devices and specially constructed motors with an external terminal of the neutral point. Thus, this method can be easily applied to conventional three-phase voltage source inverters. When the one leg of the inverters is lost by a trouble in the switching devices, the turn-on signals for the switching devices are removed. Then, the corresponding phase of the motor is connected to the neutral point of the DC link divided by the two series-connected DC smoothing capacitors. In this connection, the switching devices in the remaining two phases of the inverter can apply sinusoidal line-to-line voltage to produce the three-phase balanced currentin the motor. To realize the proposed method, a method to compensate the voltage fluctuation of the DC neutral point must be developed. The effectiveness of the proposed control strategy for the fault-mode three-phase voltage source inverters has been confirmed by the experimental results employing a test system.

  16. TR method (TRM): A separation and stress inversion method for heterogeneous fault-slip data driven by Andersonian extensional and compressional stress regimes

    NASA Astrophysics Data System (ADS)

    Tranos, Markos D.

    2015-10-01

    The recently proposed TR method (TRM) which uses the slip preference of the faults to separate heterogeneous fault-slip data in extensional and compressional Andersonian stress regimes, is enhanced so as to determine stress tensors with the use of the Wallace-Bott slip criterion. Published natural fault-slip data from the extensional region of Tympaki, Crete, Greece and artificial fault-slip data modeled from the Chelungpu thrust, activated during the 1999 Chi-Chi earthquake in Taiwan, have been used as case studies. In the first case, the fault-slip data previously considered as homogeneous might actually be of heterogeneous origin as they determine two distinct stress tensors that both fit well with the neotectonic faulting deformation of the region. In the second case, where the fault-slip data belonging to three different subsets are of low diversity, the TRM succeeds in defining the driving stress tensors. The Misfit Angle minimization criterion can adequately separate the fault-slip data between two subsets when the percentage of the "Stress Tensor Discriminator Faults" is higher than approximately 70%.

  17. Fault model development for fault tolerant VLSI design

    NASA Astrophysics Data System (ADS)

    Hartmann, C. R.; Lala, P. K.; Ali, A. M.; Visweswaran, G. S.; Ganguly, S.

    1988-05-01

    Fault models provide systematic and precise representations of physical defects in microcircuits in a form suitable for simulation and test generation. The current difficulty in testing VLSI circuits can be attributed to the tremendous increase in design complexity and the inappropriateness of traditional stuck-at fault models. This report develops fault models for three different types of common defects that are not accurately represented by the stuck-at fault model. The faults examined in this report are: bridging faults, transistor stuck-open faults, and transient faults caused by alpha particle radiation. A generalized fault model could not be developed for the three fault types. However, microcircuit behavior and fault detection strategies are described for the bridging, transistor stuck-open, and transient (alpha particle strike) faults. The results of this study can be applied to the simulation and analysis of faults in fault tolerant VLSI circuits.

  18. 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.

  19. High-resolution fault detection at the CO2CRC Otway Project site using shear-wave reflection seismics

    NASA Astrophysics Data System (ADS)

    Beilecke, Thies; Krawczyk, Charlotte M.; Ziesch, Jennifer; Tanner, David C.

    2015-04-01

    In the framework of the project PROTECT (PRedition Of deformation To Ensure Carbon Traps) shear (SH) wave reflection data were processed and interpreted that had been collected at the CO2CRC Otway Project Site, Australia in November 2013. The aim was to supplement the existing 3-D exploration seismic data set in the uppermost 400 m targeting the upward continuation of interpreted faults at larger depth, and the detection of possible small-scale faults. Using LIAG's equipment for SH-wave seismic acquisition (hydraulic vibrator MHV4S with source point spacing of 4 m, and SH-geophones mounted on a 240-m-long land streamer with 1 m spacing), three profiles were acquired. Data processing comprised geometry setup, elevation statics, surface-wave noise suppression via fk-filtering, velocity determination after DMO-correction, pre-stack time migration, and 1-D depth conversion. Fundamental steps of data pre-processing turned out to be spectral balancing and fk-filtering. Surprisingly, the application of refraction statics partly improved the data quality on one of three profiles drastically, even though the survey had been carried out on road pavement. However, this probably can be attributed to the relatively high refractor velocity of more than 700 m/s in the survey area. The interpretation of profiles PROTECT 1 and PROTECT 2 shows that the deep fault zones do in fact reach the surface. In the case of PROTECT 1, a reverse fault structure is evidenced, that can be linked to the expected normal fault structure at ca. 400 m depth. This reverse fault seems to correlate with a step in surface topography. In the case of PROTECT 2 the expected near-surface extrapolation of the interpreted deep normal fault could be imaged. PROTECT 3 reveals an unexpected fault zone above 400 m depth, which does not seem to be linked to greater depth. Pre-stack time migration and subsequent depth conversion yielded the best images so far, with a vertical resolution of ca. 5 m in the upper part. Nonetheless, pre-stack depth migration including reflection tomography will be tested in the future since tomographic velocity models are more horizon-oriented than DMO-/NMO-based ones, which may lead to better resolved depth sections.

  20. Lunar thrust faults in the Taurus-Littrow region. [detected by Apollo 17

    NASA Technical Reports Server (NTRS)

    Howard, K. A.; Muehlberger, W. R.

    1973-01-01

    Evidence, suggesting that wrinkle ridges and similar looking one-sided scrapes in the Taurus-Littrow region are caused by anticlines and thrust faults resulting from sliding on a flowing surface, is given.