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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. Battery control and fault detection method

    SciTech Connect

    Bishop, W.S.

    1984-07-11

    This is a method for control, fault detection, fault isolation, and state-of-health monitoring of batteries and battery arrays. The method consists of measuring all of the battery, well, or cell group voltages, using statistics to determine a mean voltage and a standard deviation voltage, then comparing all of the measured voltages to the mean voltage. If the measured voltage deviates from the mean voltage by an arbitrary amount (number of standard deviations) corrective action can be implemented or an alarm signal given. The measurements need to be made rapidly enough to eliminate battery or cell voltage changes due to state of charge or temperature changes and, in most cases, require a computerized data collection/reduction system. Absolute high and/or low voltage limits can be included to prevent catastrophic events. The concept can be expanded to include similar temperature, pressure and/or battery current measurements in an array.

  3. Performance evaluation of fault detection methods for wastewater treatment processes.

    PubMed

    Corominas, Lluís; Villez, Kris; Aguado, Daniel; Rieger, Leiv; Rosén, Christian; Vanrolleghem, Peter A

    2011-02-01

    Several methods to detect faults have been developed in various fields, mainly in chemical and process engineering. However, minimal practical guidelines exist for their selection and application. This work presents an index that allows for evaluating monitoring and diagnosis performance of fault detection methods, which takes into account several characteristics, such as false alarms, false acceptance, and undesirable switching from correct detection to non-detection during a fault event. The usefulness of the index to process engineering is demonstrated first by application to a simple example. Then, it is used to compare five univariate fault detection methods (Shewhart, EWMA, and residuals of EWMA) applied to the simulated results of the Benchmark Simulation Model No. 1 long-term (BSM1_LT). The BSM1_LT, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor and actuator faults and process disturbances in a wastewater treatment plant. The results from the method comparison using BSM1_LT show better performance to detect a sensor measurement shift for adaptive methods (residuals of EWMA) and when monitoring the actuator signals in a control loop (e.g., airflow). Overall, the proposed index is able to screen fault detection methods.

  4. 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.

  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. A Generalized Machine Fault Detection Method Using Unified Change Detection

    DTIC Science & Technology

    2014-10-02

    for gear diagnosis in mid to late 1970’s by Braun (1975) and Stewart (1977), the field of machine diagnostics has had enormous advancement. Over...visual inspection from side , about 3.15mm by fractography analysis). Note that the total length of the projected crack path was 5.82mm for this gear ...fault types. Vibration data from machines with several types of faults are used for the demonstration. The fault types include gear tooth cracks in a

  7. Fault detection and diagnosis based on modeling and estimation methods.

    PubMed

    Huang, Sunan; Tan, Kok Kiong

    2009-05-01

    This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the fault function. The fault model obtained by the second neural network (NN) can be used for identifying the failure mode by comparing it with any known failure modes. Finally, a simulation example is presented to illustrate the effectiveness of the proposed scheme.

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  11. 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.

  12. Solar system fault detection

    DOEpatents

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

    1986-01-01

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

  13. 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.

  14. A correlation based fault detection method for short circuits in battery packs

    NASA Astrophysics Data System (ADS)

    Xia, Bing; Shang, Yunlong; Nguyen, Truong; Mi, Chris

    2017-01-01

    This paper presents a fault detection method for short circuits based on the correlation coefficient of voltage curves. The proposed method utilizes the direct voltage measurements from the battery cells, and does not require any additional hardware or effort in modeling during fault detection. Moreover, the inherent mathematical properties of the correlation coefficient ensure the robustness of this method as the battery pack ages or is imbalanced in real applications. In order to apply this method online, the recursive moving window correlation coefficient calculation is adopted to maintain the detection sensitivity to faults during operation. An additive square wave is designed to prevent false positive detections when the batteries are at rest. The fault isolation can be achieved by identifying the overlapped cell in the correlation coefficients with fault flags. Simulation and experimental results validated the feasibility and demonstrated the advantages of this method.

  15. Compound faults detection in gearbox via meshing resonance and spectral kurtosis methods

    NASA Astrophysics Data System (ADS)

    Wang, Tianyang; Chu, Fulei; Han, Qinkai; Kong, Yun

    2017-03-01

    Kurtosis-based impulsive component identification is one of the most effective algorithms in detecting localized faults in both gearboxes and rolling bearings. However, if localized faults exist in both gear tooth and rolling bearing simultaneously it is difficult to tell the differences between the two types of defects. As such, this study proposes a new method to solve the problem by using the meshing resonance and spectral kurtosis (SK) algorithms together. In specific, the raw signal is first decomposed into different frequency bands and levels, and then the corresponding Kurtogram and MRgram are calculated via the fault SK analysis and the meshing index. Furthermore, the resonance frequency bands induced by localized faults of the gear tooth and rolling bearing are separately identified by comparing the Kurtogram and the MRgram. Finally, the compound faults are respectively detected using envelope analysis. The effectiveness of the proposed method has been validated via both simulated and experimental gearboxes vibration signals with compound faults.

  16. 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.

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

    DOEpatents

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

    2010-08-17

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

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

    DOEpatents

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

    2010-12-07

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

  19. 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.

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

    DOEpatents

    Almasi, Gheorghe [Ardsley, NY; Blumrich, Matthias Augustin [Ridgefield, CT; Chen, Dong [Croton-On-Hudson, NY; Coteus, Paul [Yorktown, NY; Gara, Alan [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Hoenicke, Dirk I [Ossining, NY; Singh, Sarabjeet [Mississauga, CA; Steinmacher-Burow, Burkhard D [Wernau, DE; Takken, Todd [Brewster, NY; Vranas, Pavlos [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. Real-time fault detection of braiding ropes using recognition methods

    NASA Astrophysics Data System (ADS)

    Matela, Lukas

    2004-10-01

    Formation of this paper is evoked by solving of device that is able to detect faults of braiding ropes in real-time. Many various inspection devices for textile industry were developed. However, rope-producing textile company has come with demand of intelligent inspection device that is able to detect faults in finishing process. The winding speeds are 50 - 200 m/min. Nowadays commercial devices are focused on textile fabrics (weaving or knitting) and they are only able to detect basic faults (holes, dirty and oil spots). Considering textile structure faults are possible to find in several research papers, however, for specific types of textiles or for slow processes only. The inspection device, which has been developed in our laboratory, is able to work with high winding speeds of rope. The device is based on fast line-scan camera with Camera-Link interface. The goal of the project was to search three basic structure faults: missing strand, strands pulled tight and stitch irregularity. The principle of fault detection is based on gathering the most suitable symptoms that are used for recognition methods. These methods are very successful for speech recognition and using them even bring us better results than using neural networks. This paper shows the way of finding the most suitable symptoms, their statistical evaluation and decision making algorithms. The most important step is reducing the problem from time-consuming image processing to one-dimensional signal processing.

  2. 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.

  3. Set-theoretic methods in robust detection and isolation of sensor faults

    NASA Astrophysics Data System (ADS)

    Xu, Feng; Puig, Vicenç; Ocampo-Martinez, Carlos; Olaru, Sorin; Stoican, Florin

    2015-10-01

    This paper proposes a sensorfault detection and isolation (FDI) approach based on interval observers and invariant sets. In fault detection (FD), both interval observer-based and invariant set-based mechanisms are used to provide real-time fault alarms. In fault isolation (FI), the proposed approach also uses these two different mechanisms. The former, based on interval observers, aims to isolate faults during the transient-state operation induced by faults. If the former does not succeed, the latter, based on both interval observers and invariant sets, is started to guarantee FI after the system enters into steady state. Besides, a collection of invariant set-based FDI conditions are established by using all available system-operating information provided by all interval observers. In order to reduce computational complexity, a method to remove all available but redundant/unnecessary system-operating information is incorporated into this approach. If the considered faults satisfy the proposed FDI conditions, it can be guaranteed that they are detectable and isolable after their occurrences. This paper concludes with a case study based on a subsystem of a wind turbine benchmark, which can illustrate the effectiveness of this FDI technique.

  4. 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.

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

    PubMed

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

    2016-07-12

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  7. 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.

  8. 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

  9. 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.

  10. 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.

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

    PubMed

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

    2015-07-17

    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.

  12. 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

  13. 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.

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

    NASA Astrophysics Data System (ADS)

    Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

    2016-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

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

  16. A Method of Fault Detection in Power System by using Multi-agent Approach

    NASA Astrophysics Data System (ADS)

    Fukunaga, Shinnosuke; Nagata, Takeshi; Tani, Kazuhiro; Shimada, Ikuhiko

    In this paper, we propose a new decentralized multi-agent approach for detecting fault equipment in power system. The proposed method consists of several Substation agents (SSAGs), Distribution substations (DSAGs) and Line agents (LAGs). SSAG is installed each substation and controls its substation. DSAG is installed each distribution substation and receives instruction from SSAG or LAG. LAG is installed any substation and controls the transmission line. In order to demonstrate the capability of proposed multi-agent system, it has been applied to a model power system that has 7 substations and 8 distribution substations. The simulation results show that the proposed multi-agent approach is effective and promising.

  17. Monitoring, fault detection and operation prediction of MSW incinerators using multivariate statistical methods.

    PubMed

    Tavares, Gilberto; Zsigraiová, Zdena; Semiao, Viriato; Carvalho, Maria da Graca

    2011-07-01

    This work proposes the application of two multivariate statistical methods, principal component analysis (PCA) and partial least square (PLS), to a continuous process of a municipal solid waste (MSW) moving grate-type incinerator for process control--monitoring, fault detection and diagnosis--through the extraction of information from historical data. PCA model is built for process monitoring capable of detecting abnormal situations and the original 16-variable process dimension is reduced to eight, the first 4 being able to capture together 86% of the total process variation. PLS model is constructed to predict the generated superheated steam flow rate allowing for control of its set points. The model retained six of the original 13 variables, explaining together 90% of the input variation and almost 98% of the output variation. The proposed methodology is demonstrated by applying those multivariate statistical methods to process data continuously measured in an actual incinerator. Both models exhibited very good performance in fault detection and isolation. In predicting the generated superheated steam flow rate for its set point control the PLS model performed very well with low prediction errors (RMSE of 3.1 and 4.1).

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

    NASA Astrophysics Data System (ADS)

    Pourarian, Shokouh

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

  19. 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.

  20. Fault detection and isolation for discrete-time switched linear systems based on average dwell-time method

    NASA Astrophysics Data System (ADS)

    Li, Jian; Yang, Guang-Hong

    2013-12-01

    This article is concerned with the problem of fault detection and isolation (FDI) for discrete-time switched linear systems based on the average dwell-time method. The proposed FDI framework consists of a bank of FDI filters, which are divided into N groups for N subsystems. The FDI filters belonging to one group correspond to the faults for a subsystem, and generate a residual signal to guarantee the fault sensitivity performance for the subsystem, the fault attenuation performance for other subsystems and the disturbance attenuation performance for all subsystems. Different form employing the weighting matrices to restrict the frequency ranges of faults for each subsystem, the finite-frequency H - performance for switched systems is first defined. Sufficient conditions are established by linear matrix inequalities (LMIs), and the filter gains are characterised in terms of the solution of a convex optimisation problem. Two examples are used to demonstrate the effectiveness of the proposed design method.

  1. 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.

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

    PubMed

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

    2016-08-19

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

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

    PubMed Central

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

    2016-01-01

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

  4. 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.

  5. Row fault detection system

    DOEpatents

    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.

  6. 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.

  7. Gear-box fault detection using time-frequency based methods

    SciTech Connect

    Odgaard, Peter Fogh; Stoustrup, Jakob

    2015-01-01

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

  8. Method of locating ground faults

    NASA Astrophysics Data System (ADS)

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

    1994-11-01

    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.

  9. 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.

  10. An optimized ensemble local mean decomposition method for fault detection of mechanical components

    NASA Astrophysics Data System (ADS)

    Zhang, Chao; Li, Zhixiong; Hu, Chao; Chen, Shuai; Wang, Jianguo; Zhang, Xiaogang

    2017-03-01

    Mechanical transmission systems have been widely adopted in most of industrial applications, and issues related to the maintenance of these systems have attracted considerable attention in the past few decades. The recently developed ensemble local mean decomposition (ELMD) method shows satisfactory performance in fault detection of mechanical components for preventing catastrophic failures and reducing maintenance costs. However, the performance of ELMD often heavily depends on proper selection of its model parameters. To this end, this paper proposes an optimized ensemble local mean decomposition (OELMD) method to determinate an optimum set of ELMD parameters for vibration signal analysis. In OELMD, an error index termed the relative root-mean-square error (Relative RMSE) is used to evaluate the decomposition performance of ELMD with a certain amplitude of the added white noise. Once a maximum Relative RMSE, corresponding to an optimal noise amplitude, is determined, OELMD then identifies optimal noise bandwidth and ensemble number based on the Relative RMSE and signal-to-noise ratio (SNR), respectively. Thus, all three critical parameters of ELMD (i.e. noise amplitude and bandwidth, and ensemble number) are optimized by OELMD. The effectiveness of OELMD was evaluated using experimental vibration signals measured from three different mechanical components (i.e. the rolling bearing, gear and diesel engine) under faulty operation conditions.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Row fault detection system

    DOEpatents

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

    2010-02-23

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

  1. Cell boundary fault detection system

    DOEpatents

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

    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.

  2. 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.

  3. 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.

  4. 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.

  5. HVAC Fault Detection and Diagnosis Toolkit

    SciTech Connect

    Haves, Philip; Xu, Peng; Kim, Moosung

    2004-12-31

    This toolkit supports component-level model-based fault detection methods in commercial building HVAC systems. The toolbox consists of five basic modules: a parameter estimator for model calibration, a preprocessor, an AHU model simulator, a steady-state detector, and a comparator. Each of these modules and the fuzzy logic rules for fault diagnosis are described in detail. The toolbox is written in C++ and also invokes the SPARK simulation program.

  6. 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.

  7. Bisectional fault detection system

    DOEpatents

    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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. High Resolution Seismic Reflection Survey for Coal Mine: fault detection

    NASA Astrophysics Data System (ADS)

    Khukhuudei, M.; Khukhuudei, U.

    2014-12-01

    High Resolution Seismic Reflection (HRSR) methods will become a more important tool to help unravel structures hosting mineral deposits at great depth for mine planning and exploration. Modern coal mining requires certainly about geological faults and structural features. This paper focuses on 2D Seismic section mapping results from an "Zeegt" lignite coal mine in the "Mongol Altai" coal basin, which required the establishment of major structure for faults and basement. HRSR method was able to detect subsurface faults associated with the major fault system. We have used numerical modeling in an ideal, noise free environment with homogenous layering to detect of faults. In a coal mining setting where the seismic velocity of the high ranges from 3000m/s to 3600m/s and the dominant seismic frequency is 100Hz, available to locate faults with a throw of 4-5m. Faults with displacements as seam thickness detected down to several hundred meter beneath the surface.

  13. 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.

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

    SciTech Connect

    Ghazali, Abu Bakar; Ibrahim, Maslina Mohd

    2016-01-22

    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.

  15. 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.

  16. 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

  17. Signal injection as a fault detection technique.

    PubMed

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

    2011-01-01

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

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

    DOEpatents

    Linehan, Daniel J.; Bunch, Stanley L.; Lyster, Carl T.

    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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Fault detection in CMOS manufacturing using MBPCA

    NASA Astrophysics Data System (ADS)

    Lachman-Shalem, Sivan; Haimovitch, Nir; Shauly, Eitan N.; Lewin, Daniel R.

    2000-08-01

    This paper describes the application of model-based principal component analysis (MBPCA) to the identification and isolation of faults in CMOS manufacture. Some of the CMOS fabrication processing steps are well understood, with first principles mathematical models available which can describe the physical and chemical phenomena that takes place. The fabrication of the device using a known industrial process is therefore first modeled 'ideally', using ATHENA and MATLAB. Detailed furnace models are used to investigate the effect of errors in furnace control on the device fabrication and the subsequent effect on the device electrical properties. This models the distribution of device properties resulting from processing a stack of wafers in a furnace, and allows faults and production errors to be simulated for analysis. The analysis is performed using MBPCA. which has been shown to improve fault-detection resolution for batch processes. The diagnosis method is demonstrated on an industrial NMOS transistor fabrication process with faults introduced in places where they might realistically occur.

  5. Fault detection and diagnosis of diesel engine valve trains

    NASA Astrophysics Data System (ADS)

    Flett, Justin; Bone, Gary M.

    2016-05-01

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

  6. A comparison of cepstral editing methods as signal pre-processing techniques for vibration-based bearing fault detection

    NASA Astrophysics Data System (ADS)

    Peeters, Cédric; Guillaume, Patrick; Helsen, Jan

    2017-07-01

    The detection and diagnosis of incipient rolling element bearing faults is not an undemanding task and signal analysis of vibration measurements therefore often incorporates the use of various complex processing techniques. One of the key steps in the processing procedure is the proper separation of the bearing signal from other influencing sources like shafts or gears. The latter sources produce deterministic signal components showing up as discrete frequencies in the amplitude spectrum, while bearing signals are assumed to be (quasi-) cyclostationary resulting in a smearing of the bearing frequencies in the spectrum. This property gave rise to the idea of using the cepstrum for the purpose of separating the deterministic signal content from the second-order cyclostationary bearing signal. The cepstrum essentially groups the deterministic multi-harmonic signal content in a cepstral peak at the corresponding quefrency, making it more suitable for easy removal of the discrete frequency peaks. Even though initially there was a tendency to only remove or 'lifter' the selected cepstral peaks, nowadays the full real cepstrum is set to zero instead of only certain quefrency bands. This technique, called cepstrum pre-whitening, is easy to implement, can be performed quickly without the need for additional input parameters or fine-tuning and would be well-suited for practical applications. However, these advantages do come at the cost of some control and insight over the editing procedure of the signal. In order to assess the performance of this cepstrum pre-whitening technique, it is compared to an automated cepstrum editing procedure. It automatically selects certain peaks in the real cepstrum and only sets them to zero instead of the full real cepstrum. Both methods perform quite well in separating deterministic signal content from more random content, but there are some differences to observe when using them for diagnosis purposes. An analysis of the methods is made

  7. 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.

  8. Approximate active fault detection and control

    NASA Astrophysics Data System (ADS)

    Škach, Jan; Punčochář, Ivo; Šimandl, Miroslav

    2014-12-01

    This paper deals with approximate active fault detection and control for nonlinear discrete-time stochastic systems over an infinite time horizon. Multiple model framework is used to represent fault-free and finitely many faulty models. An imperfect state information problem is reformulated using a hyper-state and dynamic programming is applied to solve the problem numerically. The proposed active fault detector and controller is illustrated in a numerical example of an air handling unit.

  9. Research of Gear Fault Detection in Morphological Wavelet Domain

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

    For extracting mutation information from gear fault signal and achieving a valid fault diagnosis, a gear fault diagnosis method based on morphological mean wavelet transform was designed. Morphological mean wavelet transform is a linear wavelet in the framework of morphological wavelet. Decomposing gear fault signal by this morphological mean wavelet transform could produce signal synthesis operators and detailed synthesis operators. For signal synthesis operators, it was just close to orginal signal, and for detailed synthesis operators, it contained fault impact signal or interference signal and could be catched. The simulation experiment result indicates that, compared with Fourier transform, the morphological mean wavelet transform method can do time-frequency analysis for original signal, effectively catch impact signal appears position; and compared with traditional linear wavelet transform, it has simple structure, easy realization, signal local extremum sensitivity and high denoising ability, so it is more adapted to gear fault real-time detection.

  10. 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.

  11. 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.

  12. Fault detection and isolation for linear time-invariant systems

    SciTech Connect

    Liu, B.; Si, J.

    1994-12-31

    This paper is concerned with the problem of detecting and isolating faults by an observer. If the initial error of state estimation is zero, we show that faults can be isolated if and only if the system has a left-invertible detectability matrix which is defined in the present paper. For the more realistic case of nonzero initial error of state estimation, we develop fault isolation filters such that failures to be detected can be asymptotically isolated. We give necessary and sufficient conditions for the existence of such filters. We provide a method to design a fault isolation filter. This design procedure guarantees to isolate up to n faults, where n is the dimension of the system.

  13. 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.

  14. 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.

  15. 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.

  16. Data fault detection in medical sensor networks.

    PubMed

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

    2015-03-12

    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.

  17. Cell boundary fault detection system

    DOEpatents

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

    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.

  18. Robust fault detection observer design for linear uncertain systems

    NASA Astrophysics Data System (ADS)

    Wei, Xiukun; Verhaegen, Michel

    2011-01-01

    This article addresses the fault detection observer design issue for linear time invariant (LTI) systems with additive or multiplicative uncertainties, which are also subject to unknown disturbances. The observer design is investigated under the ℋ∞/ℋ- index framework using the generalised KYP lemma in the finite-frequency domain. Sufficient conditions for the existence of such a fault detection observer are given in terms of linear matrix inequalities (LMIs). The threshold design issue is discussed and a method for estimating the worst undetectable fault size is proposed. The effectiveness of the proposed algorithms is illustrated by numerical simulation examples.

  19. Networked-based generalised H∞ fault detection filtering for sensor faults

    NASA Astrophysics Data System (ADS)

    Li, Tao; Zheng, Wei Xing

    2015-04-01

    This paper is concerned with designing fault detection filters for networked control systems subject to limited communication capacity and a class of sensor stuck faults. By taking into account the communication limitations (e.g. measurement quantisation, signal transmission delays and data packet dropouts) and all possible sensor stuck faults, a unified mathematical model is first presented. Based on this framework, a full-order fault detection filter is designed such that the residual system is asymptotically stable with the prescribed attenuation level in the generalised H∞ sense. In order to further improve the detection performance, an optimisation algorithm is proposed to minimise the threshold. Finally, a spring-mass-damper system is utilised to show the effectiveness of the proposed method.

  20. Tracy-Widom distribution based fault detection approach: application to aircraft sensor/actuator fault detection.

    PubMed

    Hajiyev, Ch

    2012-01-01

    The fault detection approach based on the Tracy-Widom distribution is presented and applied to the aircraft flight control system. An operative method of testing the innovation covariance of the Kalman filter is proposed. The maximal eigenvalue of the random Wishart matrix is used as the monitoring statistic, and the testing problem is reduced to determine the asymptotics for the largest eigenvalue of the Wishart matrix. As a result, an algorithm for testing the innovation covariance based on the Tracy-Widom distribution is proposed. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model is considered, and detection of sensor and control surface faults in the flight control system which affect the innovation covariance, are examined.

  1. 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.

  2. A Hybrid Approach for Fault Detection in Autonomous Physical Agents

    DTIC Science & Technology

    2014-05-01

    A Hybrid Approach for Fault Detection in Autonomous Physical Agents Eliahu Khalastchi, Meir Kalech, Lior Rokach Information Systems Engineering...Experimentation Keywords Fault detection, Model-Based Diagnosis , Robotics, UAV. 1. INTRODUCTION Autonomous physical agents such as Unmanned Vehicles (UVs...then a crash. To continue operate autonomously, the agent must have an accurate fault detection mechanism. Upon fault detection a diagnosis process

  3. 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.

  4. 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

  5. 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.

  6. Fault detection in HVAC systems using fuzzy models

    NASA Astrophysics Data System (ADS)

    Dexter, A. L.; Mok, B. K. K.

    1993-12-01

    A fault detection scheme which uses qualitative models, consisting of sets of fuzzy rules, to describe the generic behavior of both fault free and faulty operation of plant is described. It is applied to Heating, Ventilating and Air Conditioning (HVAC) systems. These fuzzy reference models are generated off line from training data produced by computer simulation of a typical plant, with and without the faults, using a fuzzy identification scheme. A computationally efficient, fuzzy identification algorithm, that is suitable for implementation in packaged controls, is used to estimate the credibility of each of the rules. Faults are detected by comparing the behavior of the plant with the behavior predicted by the fuzzy reference models. Two methods of comparing the actual and predicted behavior are examined: a prediction based method in which faults are detected by comparing measurements, available from the building energy management system connected to the plant, with the predictions of the fuzzy reference models; and a rule similarity method in which data collected on line from the real plant are used to identify a partial fuzzy model. The degree to which faulty or correct operation is present, is determined by comparing the rules of the partial fuzzy model with the rules of the fuzzy reference models, using a fuzzy measure of similarity. Results which demonstrate the ability of both schemes to detect faults in the mixing box of the air handling unit of an air conditioning system are presented.

  7. Sparsity-based algorithm for detecting faults in rotating machines

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

    This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect.

  8. Gear fault detection using customized multiwavelet lifting schemes

    NASA Astrophysics Data System (ADS)

    Yuan, Jing; He, Zhengjia; Zi, Yanyang

    2010-07-01

    Fault symptoms of running gearboxes must be detected as early as possible to avoid serious accidents. Diverse advanced methods are developed for this challenging task. However, for multiwavelet transforms, the fixed basis functions independent of the input dynamic response signals will possibly reduce the accuracy of fault diagnosis. Meanwhile, for multiwavelet denoising technique, the universal threshold denoising tends to overkill important but weak features in gear fault diagnosis. To overcome the shortcoming, a novel method incorporating customized (i.e., signal-based) multiwavelet lifting schemes with sliding window denoising is proposed in this paper. On the basis of Hermite spline interpolation, various vector prediction and update operators with the desirable properties of biorthogonality, symmetry, short support and vanishing moments are constructed. The customized lifting-based multiwavelets for feature matching are chosen by the minimum entropy principle. Due to the periodic characteristics of gearbox vibration signals, sliding window denoising favorable to retain valuable information as much as possible is employed to extract and identify the fault features in gearbox signals. The proposed method is applied to simulation experiments, gear fault diagnosis and normal gear detection to testify the efficiency and reliability. The results show that the method involving the selection of appropriate basis functions and the proper feature extraction technique could act as an effective and promising tool for gear fault detection.

  9. 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.

  10. ESR Detection of Frictional Heat in Seismic Fault Slip

    NASA Astrophysics Data System (ADS)

    Fukuchi, T.; Mizoguchi, K.; Shimamoto, T.

    2001-12-01

    Electron spin resonance (ESR) is a spectroscopic method to detect paramagnetic defect centers or impurities in materials. Fukuchi (2001) measured ESR spectra of the Nojima pseudotachylyte found along the Nojima fault that caused the 1995 Kobe Earthquake (M 7.2) in Japan. There has been controversy as to whether frictional melting or crushing is more essential for the origin of the Nojima pseudotachylyte. As a result of the ESR measurement, the Nojima pseudotachylyte gave a huge ESR signal of bulky trivalent iron (Fe3+) ions derived from ferrimagnetic iron oxides (γ -Fe2O3). It has turned out that the bulky Fe3+ ion signal is produced by heating the Nojima fault gouge and that the fault gouge changes from an ocherous paramagnetic material to a black ferrimagnetic one with heating. We carry out high-speed frictional experiments using a new rotary-shear high-speed frictional testing machine to confirm that frictional heat in faulting can produce the black material like pseudotachylyte. By shearing under the condition that the axial stress is 0.61 MPa, the equivalent speed is 1.74 m/s and the average displacement is about 15m, the fault gouge changes into the pseudotachylyte-like material with a strong bulky Fe3+ signal. From the fault gouge after the shearing, the traces of dehydration are observed along the shear plane. This means that hot fluids dehydrated by frictional heating pass through the fault gouge along the fault plane in natural faulting. Heat and mass transfer may occur with the hot fluid flow. Furthermore, we discuss how to estimate the temperature of frictional heat using the bulky Fe3+ signal. If we use the bulky Fe3+ signal detected from the Nojima pseudotachylyte, the maximum temperature in faulting is estimated as about 600° C. This result supports that the Nojima pseudotachylyte is a crushing-originated one. >http://www.cc.yamaguchi-u.ac.jp/~fukuchi/index.html

  11. 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.

  12. Fault Detection and Isolation using Viability Theory and Interval Observers

    NASA Astrophysics Data System (ADS)

    Ghaniee Zarch, Majid; Puig, Vicenç; Poshtan, Javad

    2017-01-01

    This paper proposes the use of interval observers and viability theory in fault detection and isolation (FDI). Viability theory develops mathematical and algorithmic methods for investigating the adaptation to viability constraints of evolutions governed by complex systems under uncertainty. These methods can be used for checking the consistency between observed and predicted behavior by using simple sets that approximate the exact set of possible behavior (in the parameter or state space). In this paper, fault detection is based on checking for an inconsistency between the measured and predicted behaviors using viability theory concepts and sets. Finally, an example is provided in order to show the usefulness of the proposed approach.

  13. 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.

  14. Double fault detection of cone-shaped redundant IMUs using wavelet transformation and EPSA.

    PubMed

    Lee, Wonhee; Park, Chan Gook

    2014-02-19

    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).

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. Multi-directional fault detection system

    DOEpatents

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

    2010-11-23

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

  1. Multi-directional fault detection system

    DOEpatents

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

    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.

  2. 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.

  3. 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.

  4. Statistical Fault Detection & Diagnosis Expert System

    SciTech Connect

    Wegerich, Stephan

    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.

  5. Sinusoidal synthesis based adaptive tracking for rotating machinery fault detection

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  6. Induction motor inter turn fault detection using infrared thermographic analysis

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

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

    PubMed

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

    2017-03-01

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

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

    NASA Technical Reports Server (NTRS)

    Ijaz, Nabeel; Frenzel, James F.

    1993-01-01

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

  9. 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.

  10. 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.

  11. Robust fault detection and isolation in stochastic systems

    NASA Astrophysics Data System (ADS)

    George, Jemin

    2012-07-01

    This article outlines the formulation of a robust fault detection and isolation (FDI) 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 estimating 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 proposed robust FDI system.

  12. UIO design for singular delayed LPV systems with application to actuator fault detection and isolation

    NASA Astrophysics Data System (ADS)

    Hassanabadi, Amir Hossein; Shafiee, Masoud; Puig, Vicenc

    2016-01-01

    In this paper, the unknown input observer (UIO) design for singular delayed linear parameter varying (LPV) systems is considered regarding its application to actuator fault detection and isolation. The design procedure assumes that the LPV system is represented in the polytopic framework. Existence and convergence conditions for the UIO are established. The design procedure is formulated by means of linear matrix inequalities (LMIs). Actuator fault detection and isolation is based on using the UIO approach for designing a residual generator that is completely decoupled from unknown inputs and exclusively sensitive to faults. Fault isolation is addressed considering two different strategies: dedicated and generalised bank of observers' schemes. The applicability of these two schemes for the fault isolation is discussed. An open flow canal system is considered as a case study to illustrate the performance and usefulness of the proposed fault detection and isolation method in different fault scenarios.

  13. Automated Monitoring with a BSP Fault-Detection Test

    NASA Technical Reports Server (NTRS)

    Bickford, Randall L.; Herzog, James P.

    2003-01-01

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

  14. 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.

  15. Input-output method to fault detection for discrete-time fuzzy networked systems with time-varying delay and multiple packet losses

    NASA Astrophysics Data System (ADS)

    Wang, Shenquan; Feng, Jian; Jiang, Yulian

    2016-05-01

    The fault detection (FD) problem for discrete-time fuzzy networked systems with time-varying delay and multiple packet losses is investigated in this paper. The communication links between the plant and the FD filter (FDF) are assumed to be imperfect, and the missing probability is governed by an individual random variable satisfying a certain probabilistic distribution over the interval [0 1]. The discrete-time delayed fuzzy networked system is first transformed into the form of interconnect ion of two subsystems by applying an input-output method and a two-term approximation approach, which are employed to approximate the time-varying delay. Our attention is focused on the design of fuzzy FDF (FFDF) such that, for all data missing conditions, the overall FD dynamics are input-output stable in mean square and preserves a guaranteed performance. Sufficient conditions are first established via H∞ performance analysis for the existence of the desired FFDF; meanwhile, the corresponding solvability conditions for the desired FFDF gains are characterised in terms of the feasibility of a convex optimisation problem. Moreover, we show that the obtained criteria based on the input-output approach can also be established by applying the direct Lyapunov method to the original time-delay systems. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed approaches.

  16. 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.

  17. Fault detection and isolation in manufacturing systems with an identified discrete event model

    NASA Astrophysics Data System (ADS)

    Roth, Matthias; Schneider, Stefan; Lesage, Jean-Jacques; Litz, Lothar

    2012-10-01

    In this article a generic method for fault detection and isolation (FDI) in manufacturing systems considered as discrete event systems (DES) is presented. The method uses an identified model of the closed-loop of plant and controller built on the basis of observed fault-free system behaviour. An identification algorithm known from literature is used to determine the fault detection model in form of a non-deterministic automaton. New results of how to parameterise this algorithm are reported. To assess the fault detection capability of an identified automaton, probabilistic measures are proposed. For fault isolation, the concept of residuals adapted for DES is used by defining appropriate set operations representing generic fault symptoms. The method is applied to a case study system.

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

    NASA Astrophysics Data System (ADS)

    Singh, Dheeraj Sharan; Zhao, Qing

    2016-12-01

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

  19. 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.

  20. Optical methods in fault dynamics

    NASA Astrophysics Data System (ADS)

    Uenishi, K.; Rossmanith, H. P.

    2003-10-01

    The Rayleigh pulse interaction with a pre-stressed, partially contacting interface between similar and dissimilar materials is investigated experimentally as well as numerically. This study is intended to obtain an improved understanding of the interface (fault) dynamics during the earthquake rupture process. Using dynamic photoelasticity in conjunction with high-speed cinematography, snapshots of time-dependent isochromatic fringe patterns associated with Rayleigh pulse-interface interaction are experimentally recorded. It is shown that interface slip (instability) can be triggered dynamically by a pulse which propagates along the interface at the Rayleigh wave speed. For the numerical investigation, the finite difference wave simulator SWIFD is used for solving the problem under different combinations of contacting materials. The effect of acoustic impedance ratio of the two contacting materials on the wave patterns is discussed. The results indicate that upon interface rupture, Mach (head) waves, which carry a relatively large amount of energy in a concentrated form, can be generated and propagated from the interface contact region (asperity) into the acoustically softer material. Such Mach waves can cause severe damage onto a particular region inside an adjacent acoustically softer area. This type of damage concentration might be a possible reason for the generation of the "damage belt" in Kobe, Japan, on the occasion of the 1995 Hyogo-ken Nanbu (Kobe) Earthquake.

  1. 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.

  2. Neuro-fuzzy identification applied to fault detection in nonlinear systems

    NASA Astrophysics Data System (ADS)

    Blázquez, L. Felipe; de Miguel, Luis J.; Aller, Fernando; Perán, José R.

    2011-10-01

    This article describes a fault detection method, based on the parity equations approach, to be applied to nonlinear systems. The input-output nonlinear model of the plant, used in the method, has been obtained by a neural fuzzy inference architecture and its learning algorithm. The proposed method is able to detect small abrupt faults, even in systems with unknown nonlinearities. This method has been applied to a real industrial pilot plant, and good performance has been obtained for the experimental case of fault detection in the level sensor of a level control process in the said industrial pilot plant.

  3. 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.

  4. Advanced fault diagnosis methods in molecular networks.

    PubMed

    Habibi, Iman; Emamian, Effat S; Abdi, Ali

    2014-01-01

    Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally.

  5. Rapid detection of small oscillation faults via deterministic learning.

    PubMed

    Wang, Cong; Chen, Tianrui

    2011-08-01

    Detection of small faults is one of the most important and challenging tasks in the area of fault diagnosis. In this paper, we present an approach for the rapid detection of small oscillation faults based on a recently proposed deterministic learning (DL) theory. The approach consists of two phases: the training phase and the test phase. In the training phase, the system dynamics underlying normal and fault oscillations are locally accurately approximated through DL. The obtained knowledge of system dynamics is stored in constant radial basis function (RBF) networks. In the diagnosis phase, rapid detection is implemented. Specially, a bank of estimators are constructed using the constant RBF neural networks to represent the training normal and fault modes. By comparing the set of estimators with the test monitored system, a set of residuals are generated, and the average L(1) norms of the residuals are taken as the measure of the differences between the dynamics of the monitored system and the dynamics of the training normal mode and oscillation faults. The occurrence of a test oscillation fault can be rapidly detected according to the smallest residual principle. A rigorous analysis of the performance of the detection scheme is also given. The novelty of the paper lies in that the modeling uncertainty and nonlinear fault functions are accurately approximated and then the knowledge is utilized to achieve rapid detection of small oscillation faults. Simulation studies are included to demonstrate the effectiveness of the approach.

  6. 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.

  7. Experimental studies on intelligent fault detection and diagnosis using sensor networks on mechanical pneumatic systems

    NASA Astrophysics Data System (ADS)

    Zhang, Kunbo; Kao, Imin; Kambli, Sachin; Boehm, Christian

    2008-03-01

    Fault is a undesirable factor in any mechanical/pneumatic system. It affects the efficiency of system operation and reduces economic benefit in industry. The early detection and diagnosis of faults in a mechanical system becomes important for preventing failure of equipment and loss of productivity and profits. In this paper, we present our ongoing research results on intelligent fault detections and diagnosis (FDD) on mechanical/ pneumatic systems. Using data from sensors and sensor network in an integrated industrial system, our proposed FDD methodology provides the analysis of necessary sensory information (for example, flow rates and pressure, as well as other digital sensor data) for the detection and diagnosis of system fault. In this experimental study, the leakage of pneumatic cylinder was the "fault." It was shown that the FDD analysis was able to make diagnosis of leakage both in location and size of the fault. In addition, the systematic fault and localized faults can be detected separately. The proposed wavelet method gives rise to the fingerprint analysis to recognize the patterns of the flow rate and pressure data - a very useful tool in intelligent fault detection and diagnosis.

  8. Multi-faults detection and estimation for nonlinear stochastic system based on particle filter and hypothesis test

    NASA Astrophysics Data System (ADS)

    Ding, Bo; Fang, Huajing

    2016-12-01

    This paper is concerned with the fault detection and estimation for nonlinear stochastic system with additive multi-faults. The states of system are estimated by the improved particle filter which composed of basic particle filter and preliminary fault estimation. Since the preliminary fault estimation contains noise, the faults are detected by the method of hypothesis testing, while the amplitude of each fault is estimated by the average of the sample of preliminary fault estimation. Meanwhile, the relationship of the sample size, the significance level of two types of error, the amplitude of fault and the variance of the error of preliminary fault estimation are also given. The effectiveness of the proposed method is verified by the simulation of three-vessel water tank system.

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

    PubMed

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

    2017-01-01

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

  10. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2009-01-01

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

  12. Sensor fault detection and isolation over wireless sensor network based on hardware redundancy

    NASA Astrophysics Data System (ADS)

    Hao, Jingjing; Kinnaert, Michel

    2017-01-01

    In order to diagnose sensor faults with small magnitude in wireless sensor networks, distinguishability measures are defined to indicate the performance for fault detection and isolation (FDI) at each node. A systematic method is then proposed to determine the information to be exchanged between nodes to achieve FDI specifications while limiting the computation complexity and communication cost.

  13. 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.

  14. Fiber Bragg Grating Sensor for Fault Detection in Radial and Network Transmission Lines

    PubMed Central

    Moghadas, Amin A.; Shadaram, Mehdi

    2010-01-01

    In this paper, a fiber optic based sensor capable of fault detection in both radial and network overhead transmission power line systems is investigated. Bragg wavelength shift is used to measure the fault current and detect fault in power systems. Magnetic fields generated by currents in the overhead transmission lines cause a strain in magnetostrictive material which is then detected by Fiber Bragg Grating (FBG). The Fiber Bragg interrogator senses the reflected FBG signals, and the Bragg wavelength shift is calculated and the signals are processed. A broadband light source in the control room scans the shift in the reflected signal. Any surge in the magnetic field relates to an increased fault current at a certain location. Also, fault location can be precisely defined with an artificial neural network (ANN) algorithm. This algorithm can be easily coordinated with other protective devices. It is shown that the faults in the overhead transmission line cause a detectable wavelength shift on the reflected signal of FBG and can be used to detect and classify different kind of faults. The proposed method has been extensively tested by simulation and results confirm that the proposed scheme is able to detect different kinds of fault in both radial and network system. PMID:22163416

  15. Fault Detection and Model Identification in Linear Dynamical Systems

    DTIC Science & Technology

    2001-02-01

    fault detection and isolation (FDI). One avenue of FDI is via the multi-model approach, in which the parameters of the nominal, unfailed model of the system are known, as well as the parameters of one or more fault models. The design goal is to obtain an indicator for when a fault has occurred, and, when more than one type is possible, which type of fault it is. A choice that must be made in tile system design is how to model noise. One way is as a bounded energy signal. This approach places very few restrictions on the types of noisy systems which

  16. 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 ʽstructured 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.

  17. A novel KFCM based fault diagnosis method for unknown faults in satellite reaction wheels.

    PubMed

    Hu, Di; Sarosh, Ali; Dong, Yun-Feng

    2012-03-01

    Reaction wheels are one of the most critical components of the satellite attitude control system, therefore correct diagnosis of their faults is quintessential for efficient operation of these spacecraft. The known faults in any of the subsystems are often diagnosed by supervised learning algorithms, however, this method fails to work correctly when a new or unknown fault occurs. In such cases an unsupervised learning algorithm becomes essential for obtaining the correct diagnosis. Kernel Fuzzy C-Means (KFCM) is one of the unsupervised algorithms, although it has its own limitations; however in this paper a novel method has been proposed for conditioning of KFCM method (C-KFCM) so that it can be effectively used for fault diagnosis of both known and unknown faults as in satellite reaction wheels. The C-KFCM approach involves determination of exact class centers from the data of known faults, in this way discrete number of fault classes are determined at the start. Similarity parameters are derived and determined for each of the fault data point. Thereafter depending on the similarity threshold each data point is issued with a class label. The high similarity points fall into one of the 'known-fault' classes while the low similarity points are labeled as 'unknown-faults'. Simulation results show that as compared to the supervised algorithm such as neural network, the C-KFCM method can effectively cluster historical fault data (as in reaction wheels) and diagnose the faults to an accuracy of more than 91%.

  18. 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.

  19. 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

  20. Health Monitoring System for the SSME-fault detection algorithms

    NASA Technical Reports Server (NTRS)

    Tulpule, S.; Galinaitis, W. S.

    1990-01-01

    A Health Monitoring System (HMS) Framework for the Space Shuttle Main Engine (SSME) has been developed by United Technologies Corporation (UTC) for the NASA Lewis Research Center. As part of this effort, fault detection algorithms have been developed to detect the SSME faults with sufficient time to shutdown the engine. These algorithms have been designed to provide monitoring coverage during the startup, mainstage and shutdown phases of the SSME operation. The algorithms have the capability to detect multiple SSME faults, and are based on time series, regression and clustering techniques. This paper presents a discussion of candidate algorithms suitable for fault detection followed by a description of the algorithms selected for implementation in the HMS and the results of testing these algorithms with the SSME test stand data.

  1. Model-based fault detection of blade pitch system in floating wind turbines

    NASA Astrophysics Data System (ADS)

    Cho, S.; Gao, Z.; Moan, T.

    2016-09-01

    This paper presents a model-based scheme for fault detection of a blade pitch system in floating wind turbines. A blade pitch system is one of the most critical components due to its effect on the operational safety and the dynamics of wind turbines. Faults in this system should be detected at the early stage to prevent failures. To detect faults of blade pitch actuators and sensors, an appropriate observer should be designed to estimate the states of the system. Residuals are generated by a Kalman filter and a threshold based on H optimization, and linear matrix inequality (LMI) is used for residual evaluation. The proposed method is demonstrated in a case study that bias and fixed output in pitch sensors and stuck in pitch actuators. The simulation results show that the proposed method detects different realistic fault scenarios of wind turbines under the stochastic external winds.

  2. Residual generation for fault detection and isolation in a class of uncertain nonlinear systems

    NASA Astrophysics Data System (ADS)

    Ma, Hong-Jun; Yang, Guang-Hong

    2013-02-01

    This article studies the problem of fault detection and isolation (FDI) for a class of uncertain nonlinear systems via a residual signal generated by a novel nonlinear adaptive observer. The considered faults are modelled by a set of time-varying vectors, in which a prescribed subset of faults are specially monitored and thus separable from the other faults. In the presence of Lipschitz-like nonlinearities and modelling uncertainties, the sensitivity of the residual signal to the monitored faults and its insensitivity to the other faults are rigorously analysed. Under a persistent excitation condition, the performances of the proposed fault diagnosis scheme, including the robustness to uncertainties, the quickness of estimation, the accuracy of estimation, the sensitivity to the monitored faults and the insensitivity to the complement faults, are quantified by a series of explicit design functions relevant to the observer parameters. It turns out that the number of faults which can be completely diagnosed is independent of the number of output sensors. A simulation example is given to illustrate the effectiveness of the proposed FDI method.

  3. Incipient Fault Detection and Isolation of Field Devices in Nuclear Power Systems Using Principal Component Analysis

    SciTech Connect

    Kaistha, Nitin; Upadhyaya, Belle R.

    2001-11-15

    An integrated method for the detection and isolation of incipient faults in common field devices, such as sensors and actuators, using plant operational data is presented. The approach is based on the premise that data for normal operation lie on a surface and abnormal situations lead to deviations from the surface in a particular way. Statistically significant deviations from the surface result in the detection of faults, and the characteristic directions of deviations are used for isolation of one or more faults from the set of typical faults. Principal component analysis (PCA), a multivariate data-driven technique, is used to capture the relationships in the data and fit a hyperplane to the data. The fault direction for each of the scenarios is obtained using the singular value decomposition on the state and control function prediction errors, and fault isolation is then accomplished from projections on the fault directions. This approach is demonstrated for a simulated pressurized water reactor steam generator system and for a laboratory process control system under single device fault conditions. Enhanced fault isolation capability is also illustrated by incorporating realistic nonlinear terms in the PCA data matrix.

  4. 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.

  5. Fault detection in rotor bearing systems using time frequency techniques

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

  6. Convolutional Neural Network Based Fault Detection for Rotating Machinery

    NASA Astrophysics Data System (ADS)

    Janssens, Olivier; Slavkovikj, Viktor; Vervisch, Bram; Stockman, Kurt; Loccufier, Mia; Verstockt, Steven; Van de Walle, Rik; Van Hoecke, Sofie

    2016-09-01

    Vibration analysis is a well-established technique for condition monitoring of rotating machines as the vibration patterns differ depending on the fault or machine condition. Currently, mainly manually-engineered features, such as the ball pass frequencies of the raceway, RMS, kurtosis an crest, are used for automatic fault detection. Unfortunately, engineering and interpreting such features requires a significant level of human expertise. To enable non-experts in vibration analysis to perform condition monitoring, the overhead of feature engineering for specific faults needs to be reduced as much as possible. Therefore, in this article we propose a feature learning model for condition monitoring based on convolutional neural networks. The goal of this approach is to autonomously learn useful features for bearing fault detection from the data itself. Several types of bearing faults such as outer-raceway faults and lubrication degradation are considered, but also healthy bearings and rotor imbalance are included. For each condition, several bearings are tested to ensure generalization of the fault-detection system. Furthermore, the feature-learning based approach is compared to a feature-engineering based approach using the same data to objectively quantify their performance. The results indicate that the feature-learning system, based on convolutional neural networks, significantly outperforms the classical feature-engineering based approach which uses manually engineered features and a random forest classifier. The former achieves an accuracy of 93.61 percent and the latter an accuracy of 87.25 percent.

  7. Distributed fault detection over sensor networks with Markovian switching topologies

    NASA Astrophysics Data System (ADS)

    Ge, Xiaohua; Han, Qing-Long

    2014-05-01

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

  8. 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

  9. 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.

  10. Fault detection and diagnosis for refrigerator from compressor sensor

    DOEpatents

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

    2016-12-06

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

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

    PubMed

    Elbouchikhi, Elhoussin; Choqueuse, Vincent; Benbouzid, Mohamed

    2016-07-01

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

  12. Similarity ratio analysis for early stage fault detection with optical emission spectrometer in plasma etching process.

    PubMed

    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.

  13. 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.

  14. 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.

  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. 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.

  17. Detection of signal transients based on wavelet and statistics for machine fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhu, Z. K.; Yan, Ruqiang; Luo, Liheng; Feng, Z. H.; Kong, F. R.

    2009-05-01

    This paper presents a transient detection method that combines continuous wavelet transform (CWT) and Kolmogorov-Smirnov (K-S) test for machine fault diagnosis. According to this method, the CWT represents the signal in the time-scale plane, and the proposed "step-by-step detection" based on K-S test identifies the transient coefficients. Simulation study shows that the transient feature can be effectively identified in the time-scale plane with the K-S test. Moreover, the transients can be further transformed back into the time domain through the inverse CWT. The proposed method is then utilized in the gearbox vibration transient detection for fault diagnosis, and the results show that the transient features both expressed in the time-scale plane and re-constructed in the time domain characterize the gearbox condition and fault severity development more clearly than the original time domain signal. The proposed method is also applied to the vibration signals of cone bearings with the localized fault in the inner race, outer race and the rolling elements, respectively. The detected transients indicate not only the existence of the bearing faults, but also the information about the fault severity to a certain degree.

  18. 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.

  19. 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.

  20. 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.

  1. A Dynamic Integrated Fault Diagnosis Method for Power Transformers

    PubMed Central

    Gao, Wensheng; Liu, Tong

    2015-01-01

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

  2. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis

    NASA Astrophysics Data System (ADS)

    Liu, Ruonan; Yang, Boyuan; Zhang, Xiaoli; Wang, Shibin; Chen, Xuefeng

    2016-06-01

    Bearing plays an essential role in the performance of mechanical system and fault diagnosis of mechanical system is inseparably related to the diagnosis of the bearings. However, it is a challenge to detect weak fault from the complex and non-stationary vibration signals with a large amount of noise, especially at the early stage. To improve the anti-noise ability and detect incipient fault, a novel fault detection method based on a short-time matching method and Support Vector Machine (SVM) is proposed. In this paper, the mechanism of roller bearing is discussed and the impact time frequency dictionary is constructed targeting the multi-component characteristics and fault feature of roller bearing fault vibration signals. Then, a short-time matching method is described and the simulation results show the excellent feature extraction effects in extremely low signal-to-noise ratio (SNR). After extracting the most relevance atoms as features, SVM was trained for fault recognition. Finally, the practical bearing experiments indicate that the proposed method is more effective and efficient than the traditional methods in weak impact signal oscillatory characters extraction and incipient fault diagnosis.

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

    PubMed Central

    Li, Ke; Chen, Peng

    2011-01-01

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

  4. 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

  5. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays.

    PubMed

    Yang, Jingli; Sun, Zhen; Chen, Yinsheng

    2016-12-06

    The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

  6. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

    PubMed Central

    Yang, Jingli; Sun, Zhen; Chen, Yinsheng

    2016-01-01

    The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays. PMID:27929412

  7. Capacitance and Inductance based Rotor Ground Fault Location Method for Synchronous Machines

    NASA Astrophysics Data System (ADS)

    Palanisamy, Ramanathan

    2017-02-01

    This paper presents a capacitance and inductance based rotor ground fault location method for synchronous machines, which can detect and locate the ground fault in the rotor. The main contribution of this technique is to find the location of the ground fault in the rotor winding and reduce the repair time. This detection method is based on the measurement of inductance and capacitance of the rotor winding. It is suitable for salient pole synchronous machines. This method has been validated through experimental tests at the site.

  8. 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

  9. 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.

  10. 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.

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

    NASA Astrophysics Data System (ADS)

    Yang, Jing-li; Chen, Yin-sheng; Zhang, Li-li; Sun, Zhen

    2016-06-01

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

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

    PubMed

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

    2016-06-01

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

  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. Condition based machinery maintenance (helicopter fault detection), phase 1

    NASA Astrophysics Data System (ADS)

    Brotherton, T. W.; Pollard, T. G.

    1993-01-01

    A problem of current interest to the Navy is the automatic detection and classification of faults in mechanical systems such as the transmissions, gearboxes, and bearings of helicopters. The problem is important for both economic and safety reasons. Using automated fault detection and classification, machinery repair can be undertaken as needed and can prevent catastrophic failure. This condition-based maintenance approach is more efficient and cost-effective than the use of predetermined maintenance schedules. Additionally, automated fault detection and classification systems can alert machinery operators, such as helicopter pilots, of the onset of a mechanical problem, thus allowing the pilot to take precautions, such as landing the aircraft, before a potentially catastrophic failure occurs.

  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.

  16. ARX model-based gearbox fault detection and localization under varying load conditions

    NASA Astrophysics Data System (ADS)

    Yang, Ming; Makis, Viliam

    2010-11-01

    The development of the fault detection schemes for gearbox systems has received considerable attention in recent years. Both time series modeling and feature extraction based on wavelet methods have been considered, mostly under constant load. Constant load assumption implies that changes in vibration data are caused only by deterioration of the gearbox. However, most real gearbox systems operate under varying load and speed which affect the vibration signature of the system and in general make it difficult to recognize the occurrence of an impending fault. This paper presents a novel approach to detect and localize the gear failure occurrence for a gearbox operating under varying load conditions. First, residual signal is calculated using an autoregressive model with exogenous variables (ARX) fitted to the time-synchronously averaged (TSA) vibration data and filtered TSA envelopes when the gearbox operated under various load conditions in the healthy state. The gear of interest is divided into several sections so that each section includes the same number of adjacent teeth. Then, the fault detection and localization indicator is calculated by applying F-test to the residual signal of the ARX model. The proposed fault detection scheme indicates not only when the gear fault occurs, but also in which section of the gear. Finally, the performance of the fault detection scheme is checked using full lifetime vibration data obtained from the gearbox operating from a new condition to a breakdown under varying load.

  17. 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.

  18. Model-based fault detection and diagnosis in ALMA subsystems

    NASA Astrophysics Data System (ADS)

    Ortiz, José; Carrasco, Rodrigo A.

    2016-07-01

    The Atacama Large Millimeter/submillimeter Array (ALMA) observatory, with its 66 individual telescopes and other central equipment, generates a massive set of monitoring data every day, collecting information on the performance of a variety of critical and complex electrical, electronic and mechanical components. This data is crucial for most troubleshooting efforts performed by engineering teams. More than 5 years of accumulated data and expertise allow for a more systematic approach to fault detection and diagnosis. This paper presents model-based fault detection and diagnosis techniques to support corrective and predictive maintenance in a 24/7 minimum-downtime observatory.

  19. 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).

  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. 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.

  2. 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.

  3. 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

    2015-10-09

    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.

  4. 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.

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

    PubMed

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

    2014-01-01

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

  6. 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

  7. 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.

  8. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  10. Fault Diagnostics Using Statistical Change Detection in the Bispectral Domain

    NASA Astrophysics Data System (ADS)

    Eugene Parker, B.; Ware, H. A.; Wipf, D. P.; Tompkins, W. R.; Clark, B. R.; Larson, E. C.; Vincent Poor, H.

    2000-07-01

    It is widely accepted that structural defects in rotating machinery components (e.g. bearings and gears) can be detected through monitoring of vibration and/or sound emissions. Traditional diagnostic vibration analysis attempts to match spectral lines with a priori -known defect frequencies that are characteristic of the affected machinery components. Emphasis herein is on use of bispectral-based statistical change detection algorithms for machinery health monitoring. The bispectrum, a third-order statistic, helps identify pairs of phase-related spectral components, which is useful for fault detection and isolation. In particular, the bispectrum helps sort through the clutter of usual (second-order) vibration spectra to extract useful information associated with the health of particular components. Seeded and non-seeded helicopter gearbox fault results (CH-46E and CH-47D, respectively) show that bispectral algorithms can detect faults at the level of an individual component (i.e. bearings or gears). Fault isolation is implicit with detection based on characteristic a priori -known defect frequencies. Important attributes of the bispectral SCD approach include: (1) it does not require a priori training data as is needed for traditional pattern-classifier-based approaches (and thereby avoids the significant time and cost investments necessary to obtain such data); (2) being based on higher-order moment-based energy detection, it makes no assumptions about the statistical model of the bispectral sequences that are generated; (3) it is operating-regime independent (i.e. works across different operating conditions, flight regimes, torque levels, etc., without knowledge of same); (4) it can be used to isolate faults to the level of specific machinery components (e.g. bearings and gears); and (5) it can be implemented using relatively inexpensive computer hardware, since only low-frequency vibrations need to be processed. The bispectral SCD algorithm thus represents a

  11. 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.

  12. Robust unknown input observer design for state estimation and fault detection using linear parameter varying model

    NASA Astrophysics Data System (ADS)

    Li, Shanzhi; Wang, Haoping; Aitouche, Abdel; Tian, Yang; Christov, Nicolai

    2017-01-01

    This paper proposes a robust unknown input observer for state estimation and fault detection using linear parameter varying model. Since the disturbance and actuator fault is mixed together in the physical system, it is difficult to isolate the fault from the disturbance. Using the state transforation, the estimation of the original state becomes to associate with the transform state. By solving the linear matrix inequalities (LMIs)and linear matrix equalities (LMEs), the parameters of the UIO can be obtained. The convergence of the UIO is also analysed by the Layapunov theory. Finally, a wind turbine system with disturbance and actuator fault is tested for the proposed method. From the simulations, it demonstrates the effectiveness and performances of the proposed method.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Optimizing automated gas turbine fault detection using statistical pattern recognition

    NASA Astrophysics Data System (ADS)

    Loukis, E.; Mathioudakis, K.; Papailiou, K.

    1992-06-01

    A method enabling the automated diagnosis of Gas Turbine Compressor blade faults, based on the principles of statistical pattern recognition is initially presented. The decision making is based on the derivation of spectral patterns from dynamic measurements data and then the calculation of discriminants with respect to reference spectral patterns of the faults while it takes into account their statistical properties. A method of optimizing the selection of discriminants using dynamic measurements data is also presented. A few scalar discriminants are derived, in such a way that the maximum available discrimination potential is exploited. In this way the success rate of automated decision making is further improved, while the need for intuitive discriminant selection is eliminated. The effectiveness of the proposed methods is demonstrated by application to data coming from an Industrial Gas Turbine while extension to other aspects of Fault Diagnosis is discussed.

  18. Probabilistic approaches to fault detection in networked discrete event systems.

    PubMed

    Athanasopoulou, Eleftheria; Hadjicostis, Christoforos N

    2005-09-01

    In this paper, we consider distributed systems that can be modeled as finite state machines with known behavior under fault-free conditions, and we study the detection of a general class of faults that manifest themselves as permanent changes in the next-state transition functionality of the system. This scenario could arise in a variety of situations encountered in communication networks, including faults occurred due to design or implementation errors during the execution of communication protocols. In our approach, fault diagnosis is performed by an external observer/diagnoser that functions as a finite state machine and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the given system at intermittent time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free. An application of these ideas in the context of protocol testing/classification is provided as an example.

  19. 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.

  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.

  1. An online tacholess order tracking technique based on generalized demodulation for rolling bearing fault detection

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

    Vibration analysis has been proved to be an effective and powerful tool for the condition monitoring and fault diagnosis of rolling bearings. During the past decades, the conventional envelope analysis has been one of the main approaches in vibration signal processing. However, the envelope analysis is based on stationary assumption, thus it is not applicable to the fault diagnosis of bearings under rotating speed variation conditions. This constraint limits the bearing diagnosis in industrial applications. In recent years, order tracking methods based on time-frequency representation have been proposed for bearing fault detection under speed variation operating conditions. However, the methods are only applicable for offline bearing fault detection. Aiming at the shortcomings of the current tacholess order tracking techniques, an online tacholess order tracking method is proposed in this paper. The proposed method is on the basis of extracting the instantaneous tachometer information from the collected vibration signal itself continuously, and resampling the original signal with equal angle increment. The envelope order spectrum is used for bearing fault identification. The effectiveness of the proposed method has been validated by both simulated and experimental bearing vibration signals.

  2. 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.

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

    PubMed

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

    2007-01-01

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

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

    PubMed Central

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

    2009-01-01

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

  5. A new SKRgram based demodulation technique for planet bearing fault detection

    NASA Astrophysics Data System (ADS)

    Wang, Tianyang; Han, Qinkai; Chu, Fulei; Feng, Zhipeng

    2016-12-01

    Planet bearing fault detection is one of the most challenging issues in planetary gearbox condition monitoring. The intricate structure of a planetary gearbox will fail traditional bearing fault diagnosis algorithms by bringing in strong and complex planetary gear noise. In specific, planetary gear noise with multi-sidebands and high magnitude will not only fail the former gear noise elimination algorithms but also affect the methods designed for highlighting bearing-fault-related content. As such, we propose a new approach with four main steps to address this issue: a) calculate the spectral kurtosis (SK) matrix of a healthy planetary gearbox as baseline, b) obtain SKRgram (Spectral kurtosis ratio gram) by calculating the ratio between SK matrix of raw signal and the baseline, c) locate potential filtering areas from the SKRgram using SKR value as criterion and then select potential optimal filter bands among them with the standard of kurtosis value, d) highlight the faulty planet bearing content by filtering the raw signal through potential filter bands and identify the fault type of planet bearing by comparing the filtered results with the fault envelope pattern. The accuracy and effectiveness of the proposed planet bearing fault detection algorithm are verified by both the simulated and experimental data.

  6. Statistical Approach to Fault Detection of Gears

    DTIC Science & Technology

    1989-12-01

    by use of a cyanoacrylate ester compound (super glue ). Reference [19] indicates that this method of attachment is the most favorable method of...of the subject, that the serious student often has difficulty in seeing the wood for the trees." He goes on to state: "Geometrical relationships apart...affixing 41 the pads. It should be noted that the glue became brittle after a period of about one year leading to the pads falling off the model. Midway

  7. A hybrid approach for detecting and isolating faults in nuclear power plant interacting systems

    SciTech Connect

    Hines, J.W.; Miller, D.W.; Hajek, B.K.

    1996-09-01

    A fault detection and isolation (FDI) system is presented that can detect and isolate nuclear power plant (NPP) faults occurring in interacting systems. The proposed methodology combines two tools, observer-based residual generation and neural network pattern matching, into a powerful, hybrid diagnostic system. A computer-based model of a commercial boiling water reactor (BWR) is used as the reference plant. Two FDI methods are implemented on each of two BWR systems, and their performance characteristics are compared. One method uses conventional neural network techniques that use parameter values for input, and a second, hybrid methodology uses system models to create residuals for input to a neural network. Both FDI systems show good generalization abilities, but only the hybrid system decouples system interactions. Although implementation is impractical for all NPP systems, this hybrid technique is most useful in specific applications where operators have difficulty diagnosing faults in strongly interacting systems.

  8. 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.

  9. Aircraft Fault Detection Using Real-Time Frequency Response Estimation

    NASA Technical Reports Server (NTRS)

    Grauer, Jared A.

    2016-01-01

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

  10. Seismic slip propagation along a fault in the Shimanto accretionary prism detected by vitrinite reflectance studies

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

    Quantitative assessment of heat generation along faults during fault movement is of primary importance in understanding the dynamics of earthquakes. Last several years localized heat anomaly in a fault zone due to rapid seismic sliding has been detected by various analyses of fault zone materials, such as ferromagnetic resonance signal (Fukuchi et al., 2005), trace elements and isotopes (e.g., Ishikawa et al., 2008) and mineralogical change of clay (e.g., Hirono et al., 2008) and vitrinite reflectance (O'Hara, 2004). Here we report a heat anomaly found in a fault zone in the Shimanto accretionary complex by vitrinite reflectance measurements. Mature faults in nature mostly experience multiple seismic events, resulting in integrated heat anomaly. Thus, in addition to vitrinite reflectance measurements across natural faults, we performed high-velocity friction experiments on a mixture of quartz and vitrinite grains to evaluate how multiple rapid-slip events affect vitrinite reflectance in a fault zone. A localized heat anomaly is found in one of fault zones which are developed within a mélange unit in the Cretaceous Shimanto belt, SW Japan. A principle slip zone with thickness of ~5 mm forms within cataclastic damage zone with thickness of ~3 m. The slip zone is mainly composed of well-foliated clay minerals. Host rocks are characterized by a block-in-matrix texture: aligned sandstone and chert blocks embedded in mudstone matrix. We measured vitrinite reflectance across the fault zone by the same method as reported in Sakaguchi et al., (2011). The measurement reveals that the principle slip zone underwent localized temperature of more than 220°C, while background temperature of both damage zone and host rocks is ~170°C. Since fault motion along most active faults occurs seismological, that inevitably generates frictional heat, the localized heat anomaly is possibly caused by the rapid seismic slip. In order to evaluate the change in vitrinite reflectance by

  11. 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.

  12. Feature extraction using adaptive multiwavelets and synthetic detection index for rotor fault diagnosis of rotating machinery

    NASA Astrophysics Data System (ADS)

    Lu, Na; Xiao, Zhihuai; Malik, O. P.

    2015-02-01

    State identification to diagnose the condition of rotating machinery is often converted to a classification problem of values of non-dimensional symptom parameters (NSPs). To improve the sensitivity of the NSPs to the changes in machine condition, a novel feature extraction method based on adaptive multiwavelets and the synthetic detection index (SDI) is proposed in this paper. Based on the SDI maximization principle, optimal multiwavelets are searched by genetic algorithms (GAs) from an adaptive multiwavelets library and used for extracting fault features from vibration signals. By the optimal multiwavelets, more sensitive NSPs can be extracted. To examine the effectiveness of the optimal multiwavelets, conventional methods are used for comparison study. The obtained NSPs are fed into K-means classifier to diagnose rotor faults. The results show that the proposed method can effectively improve the sensitivity of the NSPs and achieve a higher discrimination rate for rotor fault diagnosis than the conventional methods.

  13. Repetitive transients extraction algorithm for detecting bearing faults

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

    Rolling-element bearing vibrations are random cyclostationary. This paper addresses the problem of noise reduction with simultaneous components extraction in vibration signals for faults diagnosis of bearing. The observed vibration signal is modeled as a summation of two components contaminated by noise, and each component composes of repetitive transients. To extract the two components simultaneously, an approach by solving an optimization problem is proposed in this paper. The problem adopts convex sparsity-based regularization scheme for decomposition, and non-convex regularization is used to further promote the sparsity but preserving the global convexity. A synthetic example is presented to illustrate the performance of the proposed approach for repetitive feature extraction. The performance and effectiveness of the proposed method are further demonstrated by applying to compound faults and single fault diagnosis of a locomotive bearing. The results show the proposed approach can effectively extract the features of outer and inner race defects.

  14. Application of Hermitian wavelet to crack fault detection in gearbox

    NASA Astrophysics Data System (ADS)

    Li, Hui; Zhang, Yuping; Zheng, Haiqi

    2011-05-01

    The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This advantage makes it very suitable for the detection of singularity generated by localized defects in the mechanical system. However, most of the applications of the continuous wavelet transform have widely focused on the use of Morlet wavelet transform. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. The Fourier spectrum of Hermitian wavelet is real; therefore, Hermitian wavelet does not affect the phase of a signal in the complex domain. This gives a desirable ability to extract the singularity characteristic of a signal precisely. In this study, Hermitian wavelet is used to diagnose the gear localized crack fault. The simulative and experimental results show that Hermitian wavelet can extract the transients from strong noise signals and can effectively diagnose the localized gear fault.

  15. 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.

  16. Digital electronic engine control fault detection and accommodation flight evaluation

    NASA Technical Reports Server (NTRS)

    Baer-Ruedhart, J. L.

    1984-01-01

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

  17. 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

  18. Fault detection and isolation in motion monitoring system.

    PubMed

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

    2012-01-01

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

  19. 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.

  20. 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

  1. Detecting Aseismic Fault Slip and Magmatic Intrusion From Seismicity Data

    NASA Astrophysics Data System (ADS)

    Llenos, A. L.; McGuire, J. J.

    2007-12-01

    Seismicity triggered by aseismic deformation, such as magmatic intrusions or afterslip, can be used to detect the occurrence of these otherwise difficult to observe processes. Recent studies suggest that aseismic deformation can trigger large amounts of seismicity in a variety of plate tectonic settings. We have developed a new technique that takes advantage of this triggered seismicity to estimate the time-history of aseismic stressing rate on a fault- zone by combining the rate and state dependent friction and the Epidemic Type Aftershock Sequence (ETAS) models of seismicity-rate [ Dieterich, 1994; Ogata, 1988]. In the rate-state model, the integration of an observed seismicity rate results in an estimate of the stress rate acting in a given space-time window. However, the seismicity rate observed in any catalog comes from 3 primary sources: coseismically-triggered seismicity (aftershocks), tectonically-triggered seismicity (i.e., from long-term tectonic loading), and aseismically-triggered seismicity (e.g., from dike intrusion, aseismic slip transients, or fluid migration). In catalogs dominated by directly triggered aftershocks (i.e., ETAS branching ratios >~0.7), the coseismically-triggered seismicity rate will be much larger than the aseismically-triggered rate and will dominate the estimate of stressing-rate, obscuring the aseismic transient of interest if the rate-state method is applied directly. The challenge therefore lies in isolating the aseismically-triggered seismicity rate from the coseismically-triggered seismicity rate. The ETAS model [ Ogata, 1988] provides a natural way to separate the aseismic and coseismic seismicity rates, as the ETAS parameter μ essentially reflects the aseismically-triggered rate (as well as the background tectonically-triggered rate). To develop a method that can resolve the magnitude and time history of aseismic stress transients even in high branching ratio regions, we combine the rate-state and ETAS models into a

  2. 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

  3. Fault detection for networked control systems subject to quantisation and packet dropout

    NASA Astrophysics Data System (ADS)

    Long, Yue; Yang, Guang-Hong

    2013-06-01

    This article addresses the stochastic fault detection (SFD) problem in finite-frequency domain for a class of networked control systems (NCSs) with respect to signal quantisation and data packet dropout. Considering a logarithmic quantiser and Markovian packet dropout, the NCS is modelled as a Markov jump linear system (MJLS) with quantisation error. Further, a new definition of finite-frequency stochastic H - index is given, which gives a measurement of sensitivity. Subsequently, sufficient conditions are derived to guarantee that the MJLS can achieve such a performance. By virtue of the obtained conditions, the fault detection filters (FDFs) are designed in finite-frequency domain, which are valid in characterising the disturbance attenuation performance and finite-frequency fault sensitivity performance. Finally, a simulation example is given to illustrate the method and its effectiveness.

  4. Fault Detection of Gearbox from Inverter Signals Using Advanced Signal Processing Techniques

    NASA Astrophysics Data System (ADS)

    Pislaru, C.; Lane, M.; Ball, A. D.; Gu, F.

    2012-05-01

    The gear faults are time-localized transient events so time-frequency analysis techniques (such as the Short-Time Fourier Transform, Wavelet Transform, motor current signature analysis) are widely used to deal with non-stationary and nonlinear signals. Newly developed signal processing techniques (such as empirical mode decomposition and Teager Kaiser Energy Operator) enabled the recognition of the vibration modes that coexist in the system, and to have a better understanding of the nature of the fault information contained in the vibration signal. However these methods require a lot of computational power so this paper presents a novel approach of gearbox fault detection using the inverter signals to monitor the load, rather than the motor current. The proposed technique could be used for continuous monitoring as well as on-line damage detection systems for gearbox maintenance.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. Scalable and Fault Tolerant Failure Detection and Consensus

    SciTech Connect

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

    2015-01-01

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

  10. 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.

  11. H-/H∞ fault detection observer design based on generalized output for polytopic LPV system

    NASA Astrophysics Data System (ADS)

    Zhou, Meng; Rodrigues, Mickael; Shen, Yi; Theilliol, Didier

    2017-01-01

    This paper proposes an H-/H∞ fault detection observer design method by using generalized output for a class of polytopic linear parameter-varying (LPV) system. First, with the aid of the relative degree of output, a new output vector is generated by gathering the original and its time derivative. The actuator fault is introduced into the measurement equation of the new system. An H-/H∞ observer is designed for the new LPV polytopic system to guarantee the robustness against disturbances and to improve the fault sensitivity, simultaneously. The existence conditions of the H-/H∞ observer are given and solved by a set of linear matrix inequalities (LMIs). Finally simulation results are given to illustrate the effectiveness of the proposed method.

  12. Adaptive Parameter Identification Based on Morlet Wavelet and Application in Gearbox Fault Feature Detection

    NASA Astrophysics Data System (ADS)

    Wang, Shibin; Zhu, Z. K.; He, Yingping; Huang, Weiguo

    2010-12-01

    Localized defects in rotating mechanical parts tend to result in impulse response in vibration signal, which contain important information about system dynamics being analyzed. Thus, parameter identification of impulse response provides a potential approach for localized fault diagnosis. A method combining the Morlet wavelet and correlation filtering, named Cyclic Morlet Wavelet Correlation Filtering (CMWCF), is proposed for identifying both parameters of impulse response and the cyclic period between adjacent impulses. Simulation study concerning cyclic impulse response signal with different SNR shows that CMWCF is effective in identifying the impulse response parameters and the cyclic period. Applications in parameter identification of gearbox vibration signal for localized fault diagnosis show that CMWCF is effective in identifying the parameters and thus provides a feature detection method for gearbox fault diagnosis.

  13. 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.

  14. Electromagnetic and acoustic bimodality for the detection and localization of electrical arc faults

    NASA Astrophysics Data System (ADS)

    Vasile, C.; Ioana, C.; Digulescu, A.; Candel, I.

    2016-12-01

    Electrical arc faults pose an important problem to electrical installations worldwide, be it production facilities or distribution systems. In this context, it is easy to assess the economic repercussions of such a fault, when power supply is cut off downstream of its location, while also realizing that an early detection of the on-site smaller scale faults would be of great benefit. This articles serves as a review of the current state-of-the-art work that has been carried out on the subject of detection and localization of electrical arc faults, by exploiting the bimodality of this phenomenon, which generates simultaneously electromagnetic and acoustic waves, propagating in a free space path. En experimental setup has been defined, to demonstrate principles stated in previous works by the authors, and signal processing methods have been used in order to determine the DTOA (difference-of-time-of-arrival) of the acoustic signals, which allows localization of the transient fault. In the end there is a discussion regarding the results and further works, which aims to validate this approach in more real-life applications.

  15. Sparse maximum harmonics-to-noise-ratio deconvolution for weak fault signature detection in bearings

    NASA Astrophysics Data System (ADS)

    Miao, Yonghao; Zhao, Ming; Lin, Jing; Xu, Xiaoqiang

    2016-10-01

    De-noising and enhancement of the weak fault signature from the noisy signal are crucial for fault diagnosis, as features are often very weak and masked by the background noise. Deconvolution methods have a significant advantage in counteracting the influence of the transmission path and enhancing the fault impulses. However, the performance of traditional deconvolution methods is greatly affected by some limitations, which restrict the application range. Therefore, this paper proposes a new deconvolution method, named sparse maximum harmonics-noise-ratio deconvolution (SMHD), that employs a novel index, the harmonics-to-noise ratio (HNR), to be the objective function for iteratively choosing the optimum filter coefficients to maximize HNR. SMHD is designed to enhance latent periodic impulse faults from heavy noise signals by calculating the HNR to estimate the period. A sparse factor is utilized to further suppress the noise and improve the signal-to-noise ratio of the filtered signal in every iteration step. In addition, the updating process of the sparse threshold value and the period guarantees the robustness of SMHD. On this basis, the new method not only overcomes the limitations associated with traditional deconvolution methods, minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD), but visual inspection is also better, even if the fault period is not provided in advance. Moreover, the efficiency of the proposed method is verified by simulations and bearing data from different test rigs. The results show that the proposed method is effective in the detection of various bearing faults compared with the original MED and MCKD.

  16. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis

    PubMed Central

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

    2016-01-01

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

  17. Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis.

    PubMed

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

    2016-04-16

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

  18. 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.

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

    PubMed

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

    2017-01-01

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

  20. A Frequency-Weighted Energy Operator and complementary ensemble empirical mode decomposition for bearing fault detection

    NASA Astrophysics Data System (ADS)

    Imaouchen, Yacine; Kedadouche, Mourad; Alkama, Rezak; Thomas, Marc

    2017-01-01

    Signal processing techniques for non-stationary and noisy signals have recently attracted considerable attentions. Among them, the empirical mode decomposition (EMD) which is an adaptive and efficient method for decomposing signals from high to low frequencies into intrinsic mode functions (IMFs). Ensemble EMD (EEMD) is proposed to overcome the mode mixing problem of the EMD. In the present paper, the Complementary EEMD (CEEMD) is used for bearing fault detection. As a noise-improved method, the CEEMD not only overcomes the mode mixing, but also eliminates the residual of added white noise persisting into the IMFs and enhance the calculation efficiency of the EEMD method. Afterward, a selection method is developed to choose relevant IMFs containing information about defects. Subsequently, a signal is reconstructed from the sum of relevant IMFs and a Frequency-Weighted Energy Operator is tailored to extract both the amplitude and frequency modulations from the selected IMFs. This operator outperforms the conventional energy operator and the enveloping methods, especially in the presence of strong noise and multiple vibration interferences. Furthermore, simulation and experimental results showed that the proposed method improves performances for detecting the bearing faults. The method has also high computational efficiency and is able to detect the fault at an early stage of degradation.

  1. 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.

  2. 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.

  3. CH-46 rotor head acoustic fault detection analysis

    NASA Astrophysics Data System (ADS)

    Schoess, Jeffrey N.; Busch, Darryl; Menon, Sunil

    1999-05-01

    Reductions in Navy maintenance budgets and available personnel have dictated the need to transition from time-based to 'condition-based' maintenance. Achieving this requires new enabling diagnostic technologies. Stress-wave acoustic emission technology has shown promise for the early detection of helicopter rotor head dynamic component faults. In September 1997, Honeywell Technology Center (HTC) successfully completed a three-week flight test of its rotor acoustic monitoring system (RAMS) at Patuxent River Flight Test Center. This flight test was the culmination of an ambitious 38-month, proof-of-concept effort directed at demonstrating the feasibility of detecting crack propagation in helicopter rotor components. Honeywell is presently developing a time- frequency-based real-time processing algorithm under internal research efforts to automate the fault-detection process. The focus of this paper is to overview the CH-46 flight test and system configuration and present preliminary results of the time-frequency analysis of the flight-test dataset.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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

  9. Constraining recent fault offsets with statistical and geometrical methods: Example from the Jasneuf Fault (Western Alps, France)

    NASA Astrophysics Data System (ADS)

    Billant, Jérémy; Bellier, Olivier; Godard, Vincent; Hippolyte, Jean-Claude

    2016-12-01

    We propose two new approaches regarding recent fault offset measurements by studying the seismogenic potential of the Jasneuf Fault. The NE trending right-lateral strike-slip Jasneuf Fault is the southwestern extension of the Belledonne fault system in the Vercors Massif. This fault, which is located in an intraplate domain, does not show strongly associated seismicity but displays morphological anomalies that are related to recent faulting (right-lateral offset of Late Cenozoic cliffs, recent talwegs, and post Günz scarps). The two methods that we use to quantify fault slip are as follows. 1 - Recursive measurements of stream offsets are conducted. This analysis reveals that a stream network can display characteristic distances between streams that can be mistakenly interpreted as long-term cumulative fault offsets. A comparison of the apparent stream offset values and stream spacing values is necessary to identify the true offsets. 2 - We propose a new method that enables us to determine the fault offset and kinematics by using recursive measurements of topographic apparent offsets to counter the lack of morphologic features that are used to determine piercing points or lines. This method enables us to define each possible slip vector for numerous artificial piercing points along a fault. The slip vector that is shared by these piercing points is the true slip vector. By applying these methodologies, we determine that the Jasneuf Fault has accumulated slip since the Messinian, which corresponds to an average slip rate of 0.13 ± 0.03 mm year-1. The extension of the fault is poorly constrained. Nevertheless, if we do not consider the potential aseismic (creep slip) component of the faulting, we calculate that this fault could generate Mw 5.7 earthquakes every 500 years according to Wells and Coppersmith's scaling laws and by assuming that the faulting is limited to the sedimentary cover and the Vercors Plateau.

  10. Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks

    NASA Astrophysics Data System (ADS)

    Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Zhao, Jiangbin; Peng, Zhongxiao

    2011-03-01

    A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.

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

    PubMed

    Tafinine, Farid; Mokrani, Karim

    2012-11-01

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

  13. 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.

  14. EKF-based fault detection for guided missiles flight control system

    NASA Astrophysics Data System (ADS)

    Feng, Gang; Yang, Zhiyong; Liu, Yongjin

    2017-03-01

    The guided missiles flight control system is essential for guidance accuracy and kill probability. It is complicated and fragile. Since actuator faults and sensor faults could seriously affect the security and reliability of the system, fault detection for missiles flight control system is of great significance. This paper deals with the problem of fault detection for the closed-loop nonlinear model of the guided missiles flight control system in the presence of disturbance. First, set up the fault model of flight control system, and then design the residual generation based on the extended Kalman filter (EKF) for the Eulerian-discrete fault model. After that, the Chi-square test was selected for the residual evaluation and the fault detention task for guided missiles closed-loop system was accomplished. Finally, simulation results are provided to illustrate the effectiveness of the approach proposed in the case of elevator fault separately.

  15. A study of high-impedance fault detection in distribution systems using a Kalman-filtering approach

    SciTech Connect

    Chang Wen Bin.

    1991-01-01

    Pitfalls associated with application of conventional filtering, fast fourier transform (FFT), and discrete Fourier transform (DFT) on signals with time-varying amplitude are first studied. These pitfalls are demonstrated by two numerical examples. The Kalman-filtering algorithm is then illustrated by examples to be well capable of tracking time-varying amplitude. The proposed high-impedance fault-detection scheme uses the Kalman-filtering algorithm for the optimal estimation of low-order harmonic components. A logic reasoning method is then applied to the estimated harmonic components for determining the possible cause of the disturbance. The performance of the detection scheme is evaluated by several staged arcing faults. A full investigation of these actual recorded arcing-fault data is also performed. Analysis results are used to justify the threshold values used in the proposed detection algorithm and to study the trends of variation of low-order harmonic components in the presence of a high-impedance fault.

  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. Fault Detection and Diagnosis System for the Air-conditioning

    NASA Astrophysics Data System (ADS)

    Nakahara, Nobuo

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

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

    SciTech Connect

    Kim, Woohyun; Braun, J.

    2016-03-05

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

  19. 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

    2013-01-18

    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.

  20. 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

  1. 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.

  2. 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.

  3. Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed

    NASA Astrophysics Data System (ADS)

    Zhao, Dezun; Li, Jianyong; Cheng, Weidong; Wen, Weigang

    2016-09-01

    Multi-fault detection of the rolling element bearing under time-varying rotational speed presents a challenging issue due to its complexity, disproportion and interaction. Computed order analysis (COA) is one of the most effective approaches to remove the influences of speed fluctuation, and detect all the features of multi-fault. However, many interference components in the envelope order spectrum may lead to false diagnosis results, in addition, the deficiencies of computational accuracy and efficiency also cannot be neglected. To address these issues, a novel method for compound faults detection of rolling element bearing based on the generalized demodulation (GD) algorithm is proposed in this paper. The main idea of the proposed method is to exploit the unique property of the generalized demodulation algorithm in transforming an interested instantaneous frequency trajectory of compound faults bearing signal into a line paralleling to the time axis, and then the FFT algorithm can be directly applied to the transformed signal. This novel method does not need angular resampling algorithm which is the key step of the computed order analysis, and is hence free from the deficiencies of computational error and efficiency. On the other hand, it only acts on the instantaneous fault characteristic frequency trends in envelope signal of multi-fault bearing which include rich fault information, and is hence free from irrelevant items interferences. Both simulated and experimental faulty bearing signal analysis validate that the proposed method is effective and reliable on the compound faults detection of rolling element bearing under variable rotational speed conditions. The comprehensive comparison with the computed order analysis further shows that the proposed method produces higher accurate results in less computation time.

  4. Fault Detection, Diagnosis, and Mitigation for Long-Duration AUV Missions with Minimal Human Intervention

    DTIC Science & Technology

    2014-09-30

    1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Fault Detection, Diagnosis , and Mitigation for Long...DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Fault Detection, Diagnosis , and Mitigation for Long-Duration AUV Missions with...description which is both intuitive to the operator and can be used to automate fault detection and recovery. The base platforms for this effort are the

  5. An Expert System Approach to Global Fault Detection and Isolation Design

    DTIC Science & Technology

    1990-01-01

    Fault Detection and Isolation Design S. M. Allen and A. K. Cagavan Charles River...ELEMENT 90. no, no PCW o WAB1945433-6553 65502P 3005 40 1 50 11. TITLE Odo ~~ v~~n An Expert System Approach to Global Fault Detection and isolation Design...identify by bkod number) FIELD GROUP SUB-GROUP Fault Detection and Isolation , Global PDI, Erpert Systems 01 03 Neural Networks, Computer-Aided Design

  6. Fault detection and isolation for a full-scale railway vehicle suspension with multiple Kalman filters

    NASA Astrophysics Data System (ADS)

    Jesussek, Mathias; Ellermann, Katrin

    2014-12-01

    Reliability and dependability in complex mechanical systems can be improved by fault detection and isolation (FDI) methods. These techniques are key elements for maintenance on demand, which could decrease service cost and time significantly. This paper addresses FDI for a railway vehicle: the mechanical model is described as a multibody system, which is excited randomly due to track irregularities. Various parameters, like masses, spring- and damper-characteristics, influence the dynamics of the vehicle. Often, the exact values of the parameters are unknown and might even change over time. Some of these changes are considered critical with respect to the operation of the system and they require immediate maintenance. The aim of this work is to detect faults in the suspension system of the vehicle. A Kalman filter is used in order to estimate the states. To detect and isolate faults the detection error is minimised with multiple Kalman filters. A full-scale train model with nonlinear wheel/rail contact serves as an example for the described techniques. Numerical results for different test cases are presented. The analysis shows that for the given system it is possible not only to detect a failure of the suspension system from the system's dynamic response, but also to distinguish clearly between different possible causes for the changes in the dynamical behaviour.

  7. 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.

  8. 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

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

    PubMed

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

    2015-10-23

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

  10. A Novel Arc Fault Detector for Early Detection of Electrical Fires.

    PubMed

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

    2016-04-09

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

  11. 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.

  12. A Novel Arc Fault Detector for Early Detection of Electrical Fires

    PubMed Central

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

    2016-01-01

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

  13. 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.

  14. 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.

  15. Probabilistic Model of Fault Detection in Quantum Circuits

    NASA Astrophysics Data System (ADS)

    Banerjee, A.; Pathak, A.

    Since the introduction of quantum computation, several protocols (such as quantum cryptography, quantum algorithm, quantum teleportation) have established quantum computing as a superior future technology. Each of these processes involves quantum circuits, which are prone to different kinds of faults. Consequently, it is important to verify whether the circuit hardware is defective or not. The systematic procedure to do so is known as fault testing. Normally testing is done by providing a set of valid input states and measuring the corresponding output states and comparing the output states with the expected output states of the perfect (fault less) circuit. This particular set of input vectors are known as test set [6]. If there exists a fault then the next step would be to find the exact location and nature of the defect. This is known as fault localization. A model that explains the logical or functional faults in the circuit is a fault model. Conventional fault models include (i) stuck at faults, (ii) bridge faults, and (iii) delay faults. These fault models have been rigorously studied for conventional irreversible circuit. But with the advent of reversible classical computing and quantum computing it has become important to enlarge the domain of the study on test vectors.

  16. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  18. An Ensemble Approach for Cognitive Fault Detection and Isolation in Sensor Networks.

    PubMed

    Roveri, Manuel; Trovò, Francesco

    2017-05-01

    Cognitive fault detection and diagnosis systems are systems able to provide timely information about possibly occurring faults without requiring any a priori knowledge about the process generating the data or the possible faults. This ability is crucial in sensor network scenarios where a priori information about the data generating process, the noise level or the dictionary of the possibly occurring faults is generally hard to obtain. We here present a novel cognitive fault detection and isolation system for sensor networks. The proposed solution relies on the modeling of spatial and temporal relationships present in the acquired datastreams and an ensemble of Hidden Markov Model change-detection tests working in the space of estimated parameters for fault detection and isolation purposes. The effectiveness of the proposed solution has been evaluated on both synthetically generated and real datasets.

  19. Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries.

    PubMed

    Casteleiro-Roca, José-Luis; Calvo-Rolle, José Luis; Méndez Pérez, Juan Albino; Roqueñí Gutiérrez, Nieves; de Cos Juez, Francisco Javier

    2017-01-18

    This paper presents a new fault detection system in hypnotic sensors used for general anesthesia during surgery. Drug infusion during surgery is based on information received from patient monitoring devices; accordingly, faults in sensor devices can put patient safety at risk. Our research offers a solution to cope with these undesirable scenarios. We focus on the anesthesia process using intravenous propofol as the hypnotic drug and employing a Bispectral Index (BIS(TM)) monitor to estimate the patient's unconsciousness level. The method developed identifies BIS episodes affected by disturbances during surgery with null clinical value. Thus, the clinician-or the automatic controller-will not take those measures into account to calculate the drug dose. Our method compares the measured BIS signal with expected behavior predicted by the propofol dose provider and the electromyogram (EMG) signal. For the prediction of the BIS signal, a model based on a hybrid intelligent system architecture has been created. The model uses clustering combined with regression techniques. To validate its accuracy, a dataset taken during surgeries with general anesthesia was used. The proposed fault detection method for BIS sensor measures has also been verified using data from real cases. The obtained results prove the method's effectiveness.

  20. Feeder line fault detection in the Indian MST radar

    NASA Astrophysics Data System (ADS)

    Sarkar, B. K.

    1993-08-01

    The MST Radar uses semi-rigid cables and directional couplers for feeding power from the transmitters to the antennas. Coaxial directional couplers used in the feeder network are made of aluminum. Since the feeder network is exposed to the sun, it heats up and expands its length. At night the feeder network cools down and the coaxial directional couplers contract in length. Due to the expansion and contraction, sometimes it is found that the contact between the center conductors of two consecutive directional couplers are separated and thereby make part of the antenna array ineffective. Contact between directional couplers may be broken also due to oxidation of aluminum. Although steps are taken to remove this problem using anti-corrosive grease, it is worthwhile to monitor the 'health' of the feeder line from time to time. A measurement scheme is suggested which helps to detect the faulty contact of the directional couplers and the location of the fault.

  1. 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,

  2. A fault detection scheme for linear discrete-time systems with an integrated online performance evaluation

    NASA Astrophysics Data System (ADS)

    Ding, Steven X.; Shen, Bo; Wang, Zidong; Zhong, Maiying

    2014-12-01

    This paper is concerned with the design of the fault detection systems, into which a residual generation, evaluation and threshold are integrated, for linear discrete time-varying processes over a finite horizon. In the proposed design scheme, the residual generation is realised in the context of H∞ fault estimation with a prescribed attenuation level. This attenuation level is minimised by using the Krein-space linear estimation theory and, subsequently, an H∞ fault estimator with the minimum attenuation level is designed in terms of the solution to a set of Riccati-like recursions. For the residual evaluation and decision making purpose, the false alarm rate and fault detection rate indicators are introduced in the norm-based framework, which is integrated into the decision making procedure. For the online computations of the false alarm rate and fault detection rate indicators, further estimates delivered by the H∞ fault estimator are applied without additional (online) computations. By means of checking the change in the false alarm rate and fault detection rate indicators, a decision is then made. In this way, the fault detection performance can be significantly improved. Finally, one application example is exploited to demonstrate the application of the proposed integrated fault detection and performance evaluation schemes.

  3. Hybrid Intelligent System to Perform Fault Detection on BIS Sensor During Surgeries

    PubMed Central

    Casteleiro-Roca, José-Luis; Calvo-Rolle, José Luis; Méndez Pérez, Juan Albino; Roqueñí Gutiérrez, Nieves; de Cos Juez, Francisco Javier

    2017-01-01

    This paper presents a new fault detection system in hypnotic sensors used for general anesthesia during surgery. Drug infusion during surgery is based on information received from patient monitoring devices; accordingly, faults in sensor devices can put patient safety at risk. Our research offers a solution to cope with these undesirable scenarios. We focus on the anesthesia process using intravenous propofol as the hypnotic drug and employing a Bispectral Index (BISTM) monitor to estimate the patient’s unconsciousness level. The method developed identifies BIS episodes affected by disturbances during surgery with null clinical value. Thus, the clinician—or the automatic controller—will not take those measures into account to calculate the drug dose. Our method compares the measured BIS signal with expected behavior predicted by the propofol dose provider and the electromyogram (EMG) signal. For the prediction of the BIS signal, a model based on a hybrid intelligent system architecture has been created. The model uses clustering combined with regression techniques. To validate its accuracy, a dataset taken during surgeries with general anesthesia was used. The proposed fault detection method for BIS sensor measures has also been verified using data from real cases. The obtained results prove the method’s effectiveness. PMID:28106793

  4. 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.

  5. 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

  6. Intelligent background noise reduction technology in cable fault locator using the magneto-acoustic synchronous method

    NASA Astrophysics Data System (ADS)

    Mi, JianWei; Huang, JiFa; Fang, XiaoLi; Fan, LiBin

    2017-01-01

    The magneto-acoustic synchronous method has found wide application in accurate positioning of power cable fault due to its advantages of high accuracy and strong ability to reject interference. In the view of principle, the magneto-acoustic synchronous method needs to detect the discharge sound signal and electromagnetic signal emitted from the fault point, but the discharge sound signal is easy to be interfered by the ambient noise around and the magnetic sound synchronization. Therefore, it is challenging to quickly and accurately detect the fault location of cable especially in strong background noise environment. On the other hand, the spectral subtraction is a relatively traditional and effective method in many intelligent background noise reduction technologies, which is characterized by a relatively small computational cost and strong real-time performance. However, its application is limited because the algorithm displays poor performance in low Signal to Noise Ratio (SNR) environment. Aiming at the shortcoming of the spectral subtraction that de-noising effect is weak in low SNR environment, this paper proposes an improved spectral subtraction combining the magnetic sound synchronous principle and analyzing the properties of discharging sound. This method can accurately estimate noise in real time and optimize the performance of the basic spectral subtraction thus solving the problem that the magneto-acoustic synchronous method is unsatisfactory for positioning cable fault in the strong background noise environment.

  7. 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

  8. A Compound fault diagnosis for rolling bearings method based on blind source separation and ensemble empirical mode decomposition.

    PubMed

    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.

  9. [The Application of the Fault Tree Analysis Method in Medical Equipment Maintenance].

    PubMed

    Liu, Hongbin

    2015-11-01

    In this paper, the traditional fault tree analysis method is presented, detailed instructions for its application characteristics in medical instrument maintenance is made. It is made significant changes when the traditional fault tree analysis method is introduced into the medical instrument maintenance: gave up the logic symbolic, logic analysis and calculation, gave up its complicated programs, and only keep its image and practical fault tree diagram, and the fault tree diagram there are also differences: the fault tree is no longer a logical tree but the thinking tree in troubleshooting, the definition of the fault tree's nodes is different, the composition of the fault tree's branches is also different.

  10. 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.

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

    SciTech Connect

    Frank, Stephen; Heaney, Michael; Jin, Xin; Robertson, Joseph; Cheung, Howard; Elmore, Ryan; Henze, Gregor

    2016-08-26

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

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

    SciTech Connect

    Frank, Stephen; Heaney, Michael; Jin, Xin; Robertson, Joseph; Cheung, Howard; Elmore, Ryan; Henze, Gregor

    2016-08-01

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

  13. 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.

  14. 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.

  15. 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.

  16. Fault Detection and Localization Using Laser-Measured Surface Vibration

    DTIC Science & Technology

    2005-01-01

    details of the struc- tural acoustic approach to fault monitoring, describe various “inversion” algorithms for extracting the fault information...Without detail - ing the mathematics here, in locations away from the applied force, a variational form of the above equation is constructed through...the details of the derived force could be used to further characterize the fault, although we have not yet exploited this possibility. ω-k Mapping

  17. 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.

  18. 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.

  19. Fault detection for a class of uncertain nonlinear Markovian jump stochastic systems with mode-dependent time delays and sensor saturation

    NASA Astrophysics Data System (ADS)

    Zhuang, Guangming; Li, Yongmin; Li, Ze

    2016-05-01

    This paper considers the problem of robust H∞ fault detection for a class of uncertain nonlinear Markovian jump stochastic systems with mode-dependent time delays and sensor saturation. We aim to design a linear mode-dependent H∞ fault detection filter that ensures, 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 the Lyapunov stability theory and generalised Itô formula, some novel delay-dependent sufficient conditions in terms of linear matrix inequality are proposed to guarantee the existence of the desired fault detection filter. Explicit expression of the desired mode-dependent linear filter parameters is characterised by matrix decomposition, congruence transformation and convex optimisation technique. Sector condition method is utilised to deal with sensor saturation, a definite relation of sector condition parameters with fault detection system robustness against disturbances and sensitivity to faults is put forward, and weighting fault signal approach is employed to improve the performance of the fault detection system. A simulation example and an industrial nonisothermal continuous stirred tank reactor system are utilised to verify the effectiveness and usefulness of the proposed method.

  20. A multi-fault diagnosis method for sensor systems based on principle component analysis.

    PubMed

    Zhu, Daqi; Bai, Jie; Yang, Simon X

    2010-01-01

    A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time.

  1. A Multi-Fault Diagnosis Method for Sensor Systems Based on Principle Component Analysis

    PubMed Central

    Zhu, Daqi; Bai, Jie; Yang, Simon X.

    2010-01-01

    A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time. PMID:22315537

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

    PubMed

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

    2014-12-05

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

  3. 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

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

    PubMed

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

    2014-09-01

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

  5. Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

    NASA Astrophysics Data System (ADS)

    Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Peng, Zhongxiao; Li, Li

    2011-10-01

    Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and

  6. 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.

  7. Turbine Engine Fault Detection and Isolation Program. Phase I. Requirements Definition for an Integrated Engine Monitoring System. Volume II.

    DTIC Science & Technology

    1980-04-01

    FAULT DETECTION AND ISOLATION PROGRAM PHASE I - Requirements Definition Study for an Integrated Engine Monitoring System DTIC SYSTEMS CONTROL...performance monitoring on-condition maintenance performance trending engine fault detection and isolation 20 ABSTRACT (Continue an rsierse aide If...operational units and organizations actively participated in this study effort. Their technical input to the Turbine Engine Fault Detection and Isolation

  8. Bearing fault detection based on hybrid ensemble detector and empirical mode decomposition

    NASA Astrophysics Data System (ADS)

    Georgoulas, George; Loutas, Theodore; Stylios, Chrysostomos D.; Kostopoulos, Vassilis

    2013-12-01

    Aiming at more efficient fault diagnosis, this research work presents an integrated anomaly detection approach for seeded bearing faults. Vibration signals from normal bearings and bearings with three different fault locations, as well as different fault sizes and loading conditions are examined. The Empirical Mode Decomposition and the Hilbert Huang transform are employed for the extraction of a compact feature set. Then, a hybrid ensemble detector is trained using data coming only from the normal bearings and it is successfully applied for the detection of any deviation from the normal condition. The results prove the potential use of the proposed scheme as a first stage of an alarm signalling system for the detection of bearing faults irrespective of their loading condition.

  9. Building method of diagnostic model of Bayesian networks based on fault tree

    NASA Astrophysics Data System (ADS)

    Liu, Xiao; Li, Haijun; Li, Lin

    2008-10-01

    Fault tree (FT) is usually a reliability and security analysis and diagnoses decision model. It is also in common use that expressing fault diagnosis question with fault tree model. But it will not be changed easily if fault free model was built, and it could not accept and deal with new information easily. It is difficult to put the information which have nothing to do with equipment fault but can be used to fault diagnosis into diagnostic course. Bayesian Networks (BN) can learn and improve its network architecture and parameters at any time by way of practice accumulation, and raises the ability of fault diagnosis. The method of building BN based on FT is researched on this article, this method could break through the limitations of FT itself, make BN be more extensively applied to the domain of fault diagnosis and gains much better ability of fault analysis and diagnosis.

  10. Fault detection and classification in chemical processes based on neural networks with feature extraction.

    PubMed

    Zhou, Yifeng; Hahn, Juergen; Mannan, M Sam

    2003-10-01

    Feed forward neural networks are investigated here for fault diagnosis in chemical processes, especially batch processes. The use of the neural model prediction error as the residual for fault diagnosis of sensor and component is analyzed. To reduce the training time required for the neural process model, an input feature extraction process for the neural model is implemented. An additional radial basis function neural classifier is developed to isolate faults from the residual generated, and results are presented to demonstrate the satisfactory detection and isolation of faults using this approach.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. A method for detection and diagnosis on batch fermentations.

    PubMed

    Dondo, Rodolfo G

    2003-01-01

    In this work we present some basic ideas about detection and diagnosis of faults and abrupt dynamic changes in batch fermentations. Our work focuses on the simultaneous use of two detection methods (residual based and balances based) within the estimation procedure. The idea behind the use of both methods is that the weakness of one of them can be compensated by the use of the other one. Thus the simultaneous use of both methods allows detecting and possibly isolating a wide range of faults. Observations such as the effect of nonlinearities on the detection tests and robustness to model uncertainty are discussed. Numerical results on a particular case, the xanthan gum batch fermentation, are presented. Simulated faults and abnormal behaviors were promptly detected but diagnostics showed mixed results.

  16. 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.

  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.

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

    DOEpatents

    Gaubatz, Donald C.

    1990-01-01

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

  19. Methods of Endotoxin Detection.

    PubMed

    Su, Wenqiong; Ding, Xianting

    2015-08-01

    Endotoxin, present in the outer membrane of all gram-negative bacteria, can pose serious risks to human health, from irreversible shock to death. Therefore, it is essential to develop sensitive, accurate, and rapid methods for its detection. The rabbit pyrogen test is the first standard technique for endotoxin detection and, nowadays, has been replaced by the Limulus Amoebocyte Lysate test, which is the most popular detection technique for endotoxin. With in-depth understanding of endotoxin, biosensors based on endotoxin-sensing components are promising alternatives to pursue in developing low-cost, easy-operation, and fast-response endotoxin detection techniques. This article summarizes the recent advances of endotoxin detection methods with a particular emphasis on optical and electrochemical biosensors based on various sensing elements ranging from nature biomolecules to artificial materials. As the research and technological revolution continues, the highly integrated and miniaturized commercial devices for sensitively and reliably detecting endotoxin will provide a wide range of applications in people's daily life.

  20. 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.

  1. A novel fault diagnosis method of PV based-on power loss and I-V characteristics

    NASA Astrophysics Data System (ADS)

    Chen, Y. H.; Liang, R.; Tian, Y.; Wang, F.

    2016-08-01

    The power loss and the changes of internal I-V output characteristics of photovoltaic (PV) module in the typical fault condition were analyzed. We proposed an on-line real time fault diagnosis method for PV module, which takes into account the power loss and the internal I-V characteristics. Taking into account the changes of temperature and irradiation, the running status of the PV module were simulated in real time. Firstly, by comparing the simulated power with the measured power, it could determine whether the abnormal power loss has occurred. Then based on the change of output voltage, it could decide if short-circuit fault has occurred and estimate the number of short circuited cells roughly. Further, the value of fill factor (FF) can be utilized to determine whether aging fault has occurred and to acquire the remaining service life of the module. The results of simulation and experiment show that this method can effectively detect the partial shadow short-circuit fault and aging fault. It proves the feasibility and accuracy of the fault diagnosis method.

  2. The detection and interpretation of hydrogen in fault gases

    NASA Astrophysics Data System (ADS)

    Ware, Randolph H.; Roecken, Christian; Wyss, Max

    1984-03-01

    Hydrogen gas can be released by chemical and mechanical changes in crustal rocks. Once released, it is highly mobile, buoyant, and almost insoluble in groundwater. A fault system may act as a conduit, allowing hydrogen to accumulate in soil gases near a surface expression. Since hydrogen is scarce in ambient air, its presence at elevated levels in soil gases may be a tool for fault mapping. In order to evaluate this tool, we surveyed eleven different faults by measuring the concentration of hydrogen and methane in 2 to 21 soil-gas samples that were collected near each of them. The sense of motion at four of those faults is normal (western United States, Greece), at five it is strike-slip or dip-slip (California, Colorado, Japan), and at two it is thrusting (California). At four of these faults (Hebgen Lake, Yellowstone, Yamasaki, Burro Mountain) maximum concentrations of hydrogen ranged from 80 ppm to 70% and methane from 300 ppm to 5%. All other sites showed ambient levels of both gases, except for one sample taken at Mt. Borah, Idaho, that was 2% methane. From this preliminary study it is not clear whether the presence of hydrogen is correlated uniquely to the location of faults or whether it occurs randomly. The conditions required to produced and accumulate hydrogen are also not clear. Excess hydrogen may well be produced by different mechanisms in different geological regimes. For example, if ferrous hydroxide is present in local rocks, it may react to produce hydrogen. Detailed and extensive studies are needed to clarify the connection between hydrogen and tectonic faulting.

  3. One-class classification based on the convex hull for bearing fault detection

    NASA Astrophysics Data System (ADS)

    Zeng, Ming; Yang, Yu; Luo, Songrong; Cheng, Junsheng

    2016-12-01

    Originating from a nearest point problem, a novel method called one-class classification based on the convex hull (OCCCH) is proposed for one-class classification problems. The basic goal of OCCCH is to find the nearest point to the origin from the reduced convex hull of training samples. A generalized Gilbert algorithm is proposed to solve the nearest point problem. It is a geometric algorithm with high computational efficiency. OCCCH has two different forms, i.e., OCCCH-1 and OCCCH-2. The relationships among OCCCH-1, OCCCH-2 and one-class support vector machine (OCSVM) are investigated theoretically. The classification accuracy and the computational efficiency of the three methods are compared through the experiments conducted on several benchmark datasets. Experimental results show that OCCCH (including OCCCH-1 and OCCCH-2) using the generalized Gilbert algorithm performs more efficiently than OCSVM using the well-known sequential minimal optimization (SMO) algorithm; at the same time, OCCCH-2 can always obtain comparable classification accuracies to OCSVM. Finally, these methods are applied to the monitoring model constructions for bearing fault detection. Compared with OCCCH-2 and OCSVM, OCCCH-1 can significantly decrease the false alarm ratio while detecting the bearing fault successfully.

  4. 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.

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

    PubMed

    Zhao, Jinsong; Huang, Jianchao; Sun, Wei

    2008-11-01

    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.

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

    NASA Astrophysics Data System (ADS)

    Li, Gang; Zhao, Qing

    2017-03-01

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

  7. Application of electric and electromagnetic prospection methods for the investigation of geological fault zones

    NASA Astrophysics Data System (ADS)

    Schaumann, G.; Günther, T.; Musmann, P.; Grinat, M.

    2012-04-01

    Electric and electromagnetic prospection methods are applied in combination and investigated concerning their ability to image geological fault zones with depths up to a few km. Faults are prominent targets to explore because they bear possible flow paths for hydrothermal fluids. Therefore resistivity can become a valuable key parameter. Within the German Research Association gebo (Geothermal Energy and High Performance Drilling, www.gebo-nds.de) the electric/electromagnetic methods are operated alongside with the seismic exploration method. While seismic investigations yield information about the subsurface structure, electric and electromagnetic methods supplement these results with their ability to provide information about the resistivity distribution. Commonly used survey setups are analysed with respect to their investigation depth. Non-standard large-scale DC resistivity measurements in a dipole-dipole configuration energized by a high current source were applied in the field. Furthermore, Transient electromagnetic (TEM) soundings with a high transmitter moment were carried out. The setup in the field was modified in order to reach greater investigation depths. The course of seismic reflectors was incorporated into the inversion of the DC resistivity data by structural constraints. Especially thin low-resistive layers, detected by a 1D interpretation of the TEM data show a correlation to the seismic reflectors. While the 2D DC results give information about the resistivity structure of the fault zone, layers of low resistivity that are poorly determined with the DC measurements can be observed with an adapted TEM survey setup. After an initial investigation of known shallow fault zones more emphasis will be attached to the exploration of deeper structures in the subsurface, significant for geothermal tasks. A concept for a suitable field survey design is under development, especially adapted to the specific geological features in the sedimentary basin of

  8. 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.

  9. Magnetometric and gravimetric surveys in fault detection over Acambay System

    NASA Astrophysics Data System (ADS)

    García-Serrano, A.; Sanchez-Gonzalez, J.; Cifuentes-Nava, G.

    2013-05-01

    In commemoration of the centennial of the Acambay intraplate earthquake of November 19th 1912, we carry out gravimetric and magnetometric surveys to define the structure of faults caused by this event. The study area is located approximately 11 km south of Acambay, in the Acambay-Tixmadeje fault system, where we performed two magnetometric surveys, the first consisting of 17 lines with a spacing of 35m between lines and 5m between stations, and the second with a total of 12 lines with the same spacing, both NW. In addition to these two lines we performed gravimetric profiles located in the central part of each magnetometric survey, with a spacing of 25m between stations, in order to correlate the results of both techniques, the lengths of such profiles were of 600m and 550m respectively. This work describes the data processing including directional derivatives, analytical signal and inversion, by means of which we obtain results of magnetic variations and anomaly traits highly correlated with those faults. It is of great importance to characterize these faults given the large population growth in the area and settlement houses on them, which involves a high risk in the security of the population, considering that these are active faults and cannot be discard earthquakes associated with them, so it is necessary for the authorities and people have relevant information to these problem.

  10. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    DOE PAGES

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

  11. Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

    SciTech Connect

    Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; Sheng, Shuangwen

    2014-12-18

    Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issue is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.

  12. Fault detection and diagnosis for singular stochastic systems via B-spline expansions.

    PubMed

    Hu, Zhuohuan; Han, Zhengzhi; Tian, Zuohua

    2009-10-01

    This paper deals with the problem of fault detection and diagnosis (FDD) for singular stochastic systems. The outputs of singular stochastic systems are described by probability density functions (PDFs) based on square root B-spline expansions. Then, two non-linear observers are designed for the FDD. The conditions of stability of the correlative error estimation systems are given by using linear matrix inequalities (LMIs). Finally, the simulation results are presented to indicate that the approach can detect faults and estimate the size of faults.

  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. Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines

    NASA Astrophysics Data System (ADS)

    Zheng, Jinde; Pan, Haiyang; Cheng, Junsheng

    2017-02-01

    To timely detect the incipient failure of rolling bearing and find out the accurate fault location, a novel rolling bearing fault diagnosis method is proposed based on the composite multiscale fuzzy entropy (CMFE) and ensemble support vector machines (ESVMs). Fuzzy entropy (FuzzyEn), as an improvement of sample entropy (SampEn), is a new nonlinear method for measuring the complexity of time series. Since FuzzyEn (or SampEn) in single scale can not reflect the complexity effectively, multiscale fuzzy entropy (MFE) is developed by defining the FuzzyEns of coarse-grained time series, which represents the system dynamics in different scales. However, the MFE values will be affected by the data length, especially when the data are not long enough. By combining information of multiple coarse-grained time series in the same scale, the CMFE algorithm is proposed in this paper to enhance MFE, as well as FuzzyEn. Compared with MFE, with the increasing of scale factor, CMFE obtains much more stable and consistent values for a short-term time series. In this paper CMFE is employed to measure the complexity of vibration signals of rolling bearings and is applied to extract the nonlinear features hidden in the vibration signals. Also the physically meanings of CMFE being suitable for rolling bearing fault diagnosis are explored. Based on these, to fulfill an automatic fault diagnosis, the ensemble SVMs based multi-classifier is constructed for the intelligent classification of fault features. Finally, the proposed fault diagnosis method of rolling bearing is applied to experimental data analysis and the results indicate that the proposed method could effectively distinguish different fault categories and severities of rolling bearings.

  15. A method of measuring fault latency in a digital flight control system

    NASA Technical Reports Server (NTRS)

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

    1988-01-01

    This paper describes the motivation, conduct, and analysis of some 2500 low-level hardware fault cases applied in automated testing at the NASA Ames Reconfigurable Digital Flight Control System Facility. Fault detection was correlated with hardware and software fault monitoring and, in limited cases, with sensitivity to flight program execution modes. The results are statistically assessed to ascertain system-level reliability implications based on a single-fault model. Extension to multiple-fault models is addressed. The overall methodology/facility itself is judged to be a promising enhancement to current practice.

  16. Fault prediction for nonlinear stochastic system with incipient faults based on particle filter and nonlinear regression.

    PubMed

    Ding, Bo; Fang, Huajing

    2017-03-31

    This paper is concerned with the fault prediction for the nonlinear stochastic system with incipient faults. Based on the particle filter and the reasonable assumption about the incipient faults, the modified fault estimation algorithm is proposed, and the system state is estimated simultaneously. According to the modified fault estimation, an intuitive fault detection strategy is introduced. Once each of the incipient fault is detected, the parameters of which are identified by a nonlinear regression method. Then, based on the estimated parameters, the future fault signal can be predicted. Finally, the effectiveness of the proposed method is verified by the simulations of the Three-tank system.

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

    NASA Astrophysics Data System (ADS)

    Sun, Junhua; Xiao, Zhongwen

    2016-10-01

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

  18. 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.

  19. 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.).

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

    PubMed

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

    2017-02-09

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

  1. 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.

  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. 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

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

    DTIC Science & Technology

    2007-11-02

    A TRIDENT SCHOLAR PROJECT REPORT NO. 266 NON-INVASIVE DETECTION OF CH-46 AFT GEARBOX FAULTS USING DIGITAL PATTERN RECOGNITION AND CLASSIFICATION...NUMBERS Non-invasive detection of CH-46 AFT gearbox faults using digital pattern recognition and classification techniques 6. AUTHOR(S) Rex, Bryan D...helicopter gearboxes in order to d~iagnose end correct possible fault condi.itons (incipient faults ) which could eventually lead to gearbox failure. This

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

    NASA Astrophysics Data System (ADS)

    McDonald, Geoff L.; Zhao, Qing

    2017-01-01

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

  6. Nucleic Acid Detection Methods

    DOEpatents

    Smith, Cassandra L.; Yaar, Ron; Szafranski, Przemyslaw; Cantor, Charles 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'-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.

  7. 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.

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

    PubMed

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

    2008-03-01

    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.

  9. 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.

  10. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

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

  12. 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.

  13. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  15. 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.

  16. 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

    2013-08-16

    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.

  17. Tacholess Envelope Order Analysis and Its Application to Fault Detection of Rolling Element Bearings with Varying Speeds

    PubMed Central

    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

  18. Active fault detection and isolation of discrete-time linear time-varying systems: a set-membership approach

    NASA Astrophysics Data System (ADS)

    Mojtaba Tabatabaeipour, Seyed

    2015-08-01

    Active fault detection and isolation (AFDI) is used for detection and isolation of faults that are hidden in the normal operation because of a low excitation signal or due to the regulatory actions of the controller. In this paper, a new AFDI method based on set-membership approaches is proposed. In set-membership approaches, instead of a point-wise estimation of the states, a set-valued estimation of them is computed. If this set becomes empty the given model of the system is not consistent with the measurements. Therefore, the model is falsified. When more than one model of the system remains un-falsified, the AFDI method is used to generate an auxiliary signal that is injected into the system for detection and isolation of faults that remain otherwise hidden or non-isolated using passive FDI (PFDI) methods. Having the set-valued estimation of the states for each model, the proposed AFDI method finds an optimal input signal that guarantees FDI in a finite time horizon. The input signal is updated at each iteration in a decreasing receding horizon manner based on the set-valued estimation of the current states and un-falsified models at the current sample time. The problem is solved by a number of linear and quadratic programming problems, which result in a computationally efficient algorithm. The method is tested on a numerical example as well as on the pitch actuator of a benchmark wind turbine.

  19. Laser ultrasound technology for fault detection on carbon fiber composites

    NASA Astrophysics Data System (ADS)

    Seyrkammer, Robert; Reitinger, Bernhard; Grün, Hubert; Sekelja, Jakov; Burgholzer, Peter

    2014-05-01

    The marching in of carbon fiber reinforced polymers (CFRPs) to mass production in the aeronautic and automotive industry requires reliable quality assurance methods. Laser ultrasound (LUS) is a promising nondestructive testing technique for sample inspection. The benefits compared to conventional ultrasound (US) testing are couplant free measurements and an easy access to complex shapes due to remote optical excitation and detection. Here the potential of LUS is present on composite test panels with relevant testing scenarios for industry. The results are evaluated in comparison to conventional ultrasound used in the aeronautic industry.

  20. 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.

  1. Early detection for short-circuit fault in low-voltage systems based on fractal exponent wavelet analysis

    NASA Astrophysics Data System (ADS)

    Kang, Shanlin; Wang, Bingjun; Kang, Yuzhe

    2006-11-01

    By combining wavelet transform (WT ) with fractal theory, a novel approach is put forward to detect early short-circuit fault. The application of signal denoising based on the statistic rule is brought forward to determine the threshold of each order of wavelet space, and an effective method is proposed to determine the decomposition adaptively, increasing the signal-noise-ratio (SNR). In a view of the inter relationship of wavelet transform and fractal theory, the whole and local fractal exponents obtained from WT coefficients as features are presented for extracting fault signals. The effectiveness of the new algorithm used to extract the characteristic signal is described, which can be realized by the value of the fractal dimensions of those types of short-circuit fault. In accordance with the threshold value of each type of short-circuit fault in each frequency band, the correlation between the type of short-circuit and the fractal dimensions can be figured to perform extraction. This model incorporates the advantages of morphological filter and multi-scale WT to extract the feature of faults meanwhile restraining various noises. Besides, it can be implemented in real time using the available hardware. The effectiveness of this model was verified with the simulation results.

  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.

  3. An adaptive fault-tolerant event detection scheme for wireless sensor networks.

    PubMed

    Yim, Sung-Jib; Choi, Yoon-Hwa

    2010-01-01

    In this paper, we present an adaptive fault-tolerant event detection scheme for wireless sensor networks. Each sensor node detects an event locally in a distributed manner by using the sensor readings of its neighboring nodes. Confidence levels of sensor nodes are used to dynamically adjust the threshold for decision making, resulting in consistent performance even with increasing number of faulty nodes. In addition, the scheme employs a moving average filter to tolerate most transient faults in sensor readings, reducing the effective fault probability. Only three bits of data are exchanged to reduce the communication overhead in detecting events. Simulation results show that event detection accuracy and false alarm rate are kept very high and low, respectively, even in the case where 50% of the sensor nodes are faulty.

  4. A hybrid fault detection and isolation strategy for a team of cooperating unmanned vehicles

    NASA Astrophysics Data System (ADS)

    Tousi, M. M.; Khorasani, K.

    2015-01-01

    In this paper, a hybrid fault detection and isolation (FDI) methodology is developed for a team of cooperating unmanned vehicles. The proposed approach takes advantage of the cooperative nature of the team to detect and isolate relatively low-severity actuator faults that are otherwise not detectable and isolable by the vehicles themselves individually. The approach is hybrid and consists of both low-level (agent/team level) and high-level [discrete-event systems (DES) level] FDI modules. The high-level FDI module is formulated in the DES supervisory control framework, whereas the low-level FDI module invokes classical FDI techniques. By properly integrating the two FDI modules, a larger class of faults can be detected and isolated as compared to the existing techniques in the literature that rely on each level separately. Simulation results for a team of five unmanned aerial vehicles are also presented to demonstrate the effectiveness and capabilities of our proposed methodology.

  5. 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.

  6. 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.

  7. Weak-signal detection based on the stochastic resonance of bistable Duffing oscillator and its application in incipient fault diagnosis

    NASA Astrophysics Data System (ADS)

    Lai, Zhi-hui; Leng, Yong-gang

    2016-12-01

    Stochastic resonance (SR) is an important approach to detect weak vibration signals from heavy background noise and further realize mechanical incipient fault diagnosis. The stochastic resonance of a bistable Duffing oscillator is limited by strict small-parameter conditions, i.e., SR can only take place under small values of signal parameters (signal amplitude, frequency, and noise intensity). We propose a method to treat the large-parameter SR for this oscillator. The linear amplitude-transformed, time/frequency scale-transformed, and parameter-adjusted methods are presented and used to produce SR for signals with large-amplitude, large-frequency and/or large-intensity noise. Furthermore, we propose the weak-signal detection approach based on large-parameter SR in the oscillator. Finally, we employ two practical examples to demonstrate the feasibility of the proposed approach in incipient fault diagnosis.

  8. 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.

  9. 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

  10. SOM neural network fault diagnosis method of polymerization kettle equipment optimized by improved PSO algorithm.

    PubMed

    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.

  11. 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

  12. Waveguide disturbance detection method

    DOEpatents

    Korneev, Valeri A.; Nihei, Kurt T.; Myer, Larry R.

    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.

  13. 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

  14. 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

    2015-08-31

    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).

  15. Neural network based fault detection using different signal processing techniques as pre-processor

    SciTech Connect

    Petrilli, O.; Paya, B.; Esat, I.I.; Badi, M.N.M.

    1995-09-01

    The purpose of vibration monitoring is to detect faults occurring in machinery, in order to maintain safety and minimize the breakdown cost. The authors of this paper monitored the condition of two meshing spur gears with the ratio of 1:2, where intentionally a gear fault (a welded blip) was introduced on the loaded driven gear. The signals obtained from the faulty gear and the good or reference gear were preprocessed by using three spectral analysis techniques: Fourier transform, Power Cepstrum, and Wavelet transform. For each type of preprocessing a separate artificial neural network was trained and tested to distinguish the faulty gear from the good gear. Although similar work has been done before, the authors of this paper has expanded the work on to the transient signals by using Wavelet on the whole transformation rather than the amplitude of the meshing frequency. In order to achieve this the whole transformation was discretized for the artificial neural networks (ANNs) inputs. This is different from the commonly practiced method which selects the meshing frequency band.

  16. 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.

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

    PubMed Central

    Ghosh, Arup; Qin, Shiming; Lee, Jooyeoun

    2016-01-01

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

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

    PubMed

    Adhikari, Shuma; Sinha, Nidul; Dorendrajit, Thingam

    2016-01-01

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

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

    PubMed

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

    2016-01-01

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

  20. 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.

  1. 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.

  2. 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.

  3. Online motor fault detection and diagnosis using a hybrid FMM-CART model.

    PubMed

    Seera, Manjeevan; Lim, Chee Peng

    2014-04-01

    In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

  4. Fault detection for linear distributed-parameter systems using finite-dimensional functional observers

    NASA Astrophysics Data System (ADS)

    Deutscher, Joachim

    2016-03-01

    In this article, finite-dimensional residual generators are directly designed for Riesz-spectral systems with bounded input and output operators to detect faults. This is achieved by using finite-dimensional observers, that can estimate linear functionals of the state without spillover. These observers allow for a decoupling of the unknown disturbances from the estimation error dynamics under mild assumptions. Then, a finite-dimensional residual generator is obtained by approximately decoupling the state from the residual, that is generated by the observer states and the outputs. It is shown that the resulting approximation error can be made small by increasing the observer order. Then, fault detection with the finite-dimensional residual generator can be assured by introducing a time-varying threshold. A faulty Euler-Bernoulli beam with structural damping illustrates the proposed finite-dimensional fault detection approach.

  5. Effective confidence interval estimation of fault-detection process of software reliability growth models

    NASA Astrophysics Data System (ADS)

    Fang, Chih-Chiang; Yeh, Chun-Wu

    2016-09-01

    The quantitative evaluation of software reliability growth model is frequently accompanied by its confidence interval of fault detection. It provides helpful information to software developers and testers when undertaking software development and software quality control. However, the explanation of the variance estimation of software fault detection is not transparent in previous studies, and it influences the deduction of confidence interval about the mean value function that the current study addresses. Software engineers in such a case cannot evaluate the potential hazard based on the stochasticity of mean value function, and this might reduce the practicability of the estimation. Hence, stochastic differential equations are utilised for confidence interval estimation of the software fault-detection process. The proposed model is estimated and validated using real data-sets to show its flexibility.

  6. 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.

  7. Robust fault detection for networked control systems with nonlinear disturbances and imperfect measurements

    NASA Astrophysics Data System (ADS)

    Jiang, Shun; Fang, Huajing

    2013-11-01

    In this article, the robust fault detection (FD) problem is investigated for networked control systems with nonlinear disturbances and imperfect measurements. Under the consideration of packet-dropout compensation, a new measurement model is proposed to take the time-varying delay, random packet dropout and quantisation effect into account simultaneously. After properly augmenting the states of the original system and the FD filter, the robust FD problem is formulated as an auxiliary H ∞ filtering problem for a stochastic parameter system with time-varying delays and uncertainties. A sufficient condition for the existence of the robust FD filter is derived in terms of linear matrix inequalities, which depends on both the network status and the quantisation density. A numerical example is provided to illustrate the effectiveness of the proposed method.

  8. 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

  9. Detection of broken rotor bar faults in induction motor at low load using neural network.

    PubMed

    Bessam, B; Menacer, A; Boumehraz, M; Cherif, H

    2016-09-01

    The knowledge of the broken rotor bars characteristic frequencies and amplitudes has a great importance for all related diagnostic methods. The monitoring of motor faults requires a high resolution spectrum to separate different frequency components. The Discrete Fourier Transform (DFT) has been widely used to achieve these requirements. However, at low slip this technique cannot give good results. As a solution for these problems, this paper proposes an efficient technique based on a neural network approach and Hilbert transform (HT) for broken rotor bar diagnosis in induction machines at low load. The Hilbert transform is used to extract the stator current envelope (SCE). Two features are selected from the (SCE) spectrum (the amplitude and frequency of the harmonic). These features will be used as input for neural network. The results obtained are astonishing and it is capable to detect the correct number of broken rotor bars under different load conditions.

  10. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

    NASA Astrophysics Data System (ADS)

    Zhou, Haitao; Chen, Jin; Dong, Guangming; Wang, Ran

    2016-05-01

    Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.

  11. 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

    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.

  12. 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

  13. 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.

  14. 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.

  15. Envelope analysis with a genetic algorithm-based adaptive filter bank for bearing fault detection.

    PubMed

    Kang, Myeongsu; Kim, Jaeyoung; Choi, Byeong-Keun; Kim, Jong-Myon

    2015-07-01

    This paper proposes a fault detection methodology for bearings using envelope analysis with a genetic algorithm (GA)-based adaptive filter bank. Although a bandpass filter cooperates with envelope analysis for early identification of bearing defects, no general consensus has been reached as to which passband is optimal. This study explores the impact of various passbands specified by the GA in terms of a residual frequency components-to-defect frequency components ratio, which evaluates the degree of defectiveness in bearings and finally outputs an optimal passband for reliable bearing fault detection.

  16. An Intelligent Fault Detection and Isolation Architecture for Antenna Arrays

    NASA Astrophysics Data System (ADS)

    Rahnamai, K.; Arabshahi, P.; Yan, T.-Y.; Pham, T.; Finley, S. G.

    1997-10-01

    This article describes a general architecture for fault modeling, diagnosis, and isolation of the DSN antenna array based on computationally intelligent techniques(neural networks and fuzzy logic). It encompasses a suite of intelligent test and diagnosis algorithms in software. By continuously monitoring the health of the highly complex and nonlinear array observables, the automated diagnosis software will be able to identify and isolate the most likely causes of system failure in cases of faulty operation. Furthermore, it will be able to recommend a series of corresponding corrective actions and effectively act as an automated real-time and interactive system supervisor. In so doing, it will enhance the array capability by reducing the operational workload, increasing science information availability, reducing the overall cost of operation by reducing system downtimes, improving risk management, and making mission planning much more reliable. Operation of this architecture is illustrated using examples from observables available from the 34-meter arraying task.

  17. 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.

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

    PubMed

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

    2016-12-08

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

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

    PubMed

    Huang, Rimao; Qiu, Xuesong; Rui, Lanlan

    2011-01-01

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

  20. An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis

    NASA Astrophysics Data System (ADS)

    Qiao, Zijian; Lei, Yaguo; Lin, Jing; Jia, Feng

    2017-02-01

    In mechanical fault diagnosis, most traditional methods for signal processing attempt to suppress or cancel noise imbedded in vibration signals for extracting weak fault characteristics, whereas stochastic resonance (SR), as a potential tool for signal processing, is able to utilize the noise to enhance fault characteristics. The classical bistable SR (CBSR), as one of the most widely used SR methods, however, has the disadvantage of inherent output saturation. The output saturation not only reduces the output signal-to-noise ratio (SNR) but also limits the enhancement capability for fault characteristics. To overcome this shortcoming, a novel method is proposed to extract the fault characteristics, where a piecewise bistable potential model is established. Simulated signals are used to illustrate the effectiveness of the proposed method, and the results show that the method is able to extract weak fault characteristics and has good enhancement performance and anti-noise capability. Finally, the method is applied to fault diagnosis of bearings and planetary gearboxes, respectively. The diagnosis results demonstrate that the proposed method can obtain larger output SNR, higher spectrum peaks at fault characteristic frequencies and therefore larger recognizable degree than the CBSR method.

  1. Cross-correlation-based detection and characterisation of microseismicity adjacent to the locked, late-interseismic Alpine Fault, South Westland, New Zealand

    NASA Astrophysics Data System (ADS)

    Chamberlain, Calum J.; Boese, Carolin M.; Townend, John

    2017-01-01

    The Alpine Fault is inferred on paleoseismological grounds to produce magnitude 8 earthquakes approximately every 330 yrs and to have last ruptured almost 300 yrs ago in 1717 AD. Despite approximately 90% of its typical interseismic period having elapsed since the last major earthquake, the Alpine Fault exhibits little present-day microseismicity and no geodetic evidence for shallow creep. Determining the mechanical state of the fault ahead of a future earthquake is a key objective of several studies, including the Deep Fault Drilling Project (DFDP). Here we use a network of borehole seismometers installed in conjunction with DFDP to detect and characterise low-magnitude seismicity adjacent to the central section of the Alpine Fault. We employ matched-filter detection techniques, automated cross-correlation phase picking, and singular value decomposition-derived magnitude estimation to construct a high-precision catalogue of 283 earthquakes within 5 km of the fault trace in an otherwise seismically quiet zone. The newly recognised seismicity occurs in non-repeating, spatially and temporally limited sequences, similar to sequences previously documented using standard methods but at significantly lower magnitudes of ML < 1.8. These earthquakes are not clustered on a single distinctive structure, and we infer that they are distributed throughout a highly fractured zone surrounding the Alpine Fault. Focal mechanisms computed for 13 earthquakes using manual polarity picks exhibit predominantly strike-slip faulting, consistent with focal mechanisms observed further from the fault. We conclude that the Alpine Fault is locked and accumulating strain throughout the seismogenic zone at this location.

  2. Detection of fault structure under a near-surface low velocity layer by seismic tomography: synthetics studies

    NASA Astrophysics Data System (ADS)

    Sanny, Teuku A.; Sassa, Koichi

    1996-09-01

    We have developed a new method to detect a fault structure under a near-surface low velocity layer (LVL) by seismic tomography. The field study showed that the tomography image reconstructed using borehole-surface configuration had a different result from that of using a crosshole configuration. The image reconstructed by using a borehole-surface configuration showed a decrease in seismic velocities along boreholes, and also the tomogram result using both configurations can not detect the subsurface fault structure. These phenomena are caused by the low velocity layer (LVL) at the top of investigation area. The basic idea hard is based on a downward continuation principle. By knowing the thickness of the LVL and the top of bedrock enables us to place 'virtual receiver' and/or 'virtual source' below the LVL. In this way, we can reconstruct the image by various tomographic methodologies. As an advantage, this method is easy to be use with the aid of ray tracing methodology. It can also reduce the effect of the near-surface LVL and can maximize the reconstructed image. The final result of our synthetic images by ILST, SIRT, and modified SIRT shows high accuracy and resolution for detection of fault structure under the low velocity layer.

  3. 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.

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed

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

    2017-02-21

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

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

    PubMed

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

    2015-02-02

    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.

  7. 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.

  8. A computerized method to estimate friction coefficient from orientation distribution of meso-scale faults

    NASA Astrophysics Data System (ADS)

    Sato, Katsushi

    2016-08-01

    The friction coefficient controls the brittle strength of the Earth's crust for deformation recorded by faults. This study proposes a computerized method to determine the friction coefficient of meso-scale faults. The method is based on the analysis of orientation distribution of faults, and the principal stress axes and the stress ratio calculated by a stress tensor inversion technique. The method assumes that faults are activated according to the cohesionless Coulomb's failure criterion, where the fluctuations of fluid pressure and the magnitude of differential stress are assumed to induce faulting. In this case, the orientation distribution of fault planes is described by a probability density function that is visualized as linear contours on a Mohr diagram. The parametric optimization of the function for an observed fault population yields the friction coefficient. A test using an artificial fault-slip dataset successfully determines the internal friction angle (the arctangent of the friction coefficient) with its confidence interval of several degrees estimated by the bootstrap resampling technique. An application to natural faults cutting a Pleistocene forearc basin fill yields a friction coefficient around 0.7 which is experimentally predicted by the Byerlee's law.

  9. 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

  10. 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.

  11. 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.

  12. Rupture Dynamics Simulation for Non-Planar fault by a Curved Grid Finite Difference Method

    NASA Astrophysics Data System (ADS)

    Zhang, Z.; Zhu, G.; Chen, X.

    2011-12-01

    We first implement the non-staggered finite difference method to solve the dynamic rupture problem, with split-node, for non-planar fault. Split-node method for dynamic simulation has been used widely, because of that it's more precise to represent the fault plane than other methods, for example, thick fault, stress glut and so on. The finite difference method is also a popular numeric method to solve kinematic and dynamic problem in seismology. However, previous works focus most of theirs eyes on the staggered-grid method, because of its simplicity and computational efficiency. However this method has its own disadvantage comparing to non-staggered finite difference method at some fact for example describing the boundary condition, especially the irregular boundary, or non-planar fault. Zhang and Chen (2006) proposed the MacCormack high order non-staggered finite difference method based on curved grids to precisely solve irregular boundary problem. Based upon on this non-staggered grid method, we make success of simulating the spontaneous rupture problem. The fault plane is a kind of boundary condition, which could be irregular of course. So it's convinced that we could simulate rupture process in the case of any kind of bending fault plane. We will prove this method is valid in the case of Cartesian coordinate first. In the case of bending fault, the curvilinear grids will be used.

  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. 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).

  16. 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

  17. 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

  18. Application of cyclic coherence function to bearing fault detection in a wind turbine generator under electromagnetic vibration

    NASA Astrophysics Data System (ADS)

    Teng, Wei; Ding, Xian; Zhang, Yangyang; Liu, Yibing; Ma, Zhiyong; Kusiak, Andrew

    2017-03-01

    In a wind turbine generator, there is an intrinsic electromagnetic vibration originated from an alternating magnetic field acting on a low stiffness stator, which modulates vibration signals of the generator and impedes fault feature extraction of bearings. When defects arise in a bearing, the statistics of the vibration signal are periodic and this phenomenon is described as cyclostationarity. Correspondingly, cyclostationary analysis enables finding the degree of cyclostationarity representing potential fault modulation information. In this paper, the electromagnetic vibration acting as a disturbance source for fault feature extraction is deduced. Additionally, the spectral correlation density and cyclic coherence function used for vibration analysis are estimated. A real 2 MW wind turbine generator with a faulty bearing was tested and the vibration signals were analyzed separately using conventional demodulation analysis, cyclic coherence function, complex wavelet transform and spectral kurtosis. The analysis results have demonstrated that the cyclic coherence function can detect the fault feature of inner race successfully, while the feature is concealed by intensive electromagnetic vibration in the other three methods. The disassembled bearing of the wind turbine generator illustrates the effectiveness of the analysis result, and precautionary measures for protecting bearings in generators are suggested.

  19. 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

    2014-10-28

    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.

  20. 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.

  1. 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

  2. A fault diagnosis system for PV power station based on global partitioned gradually approximation method

    NASA Astrophysics Data System (ADS)

    Wang, S.; Zhang, X. N.; Gao, D. D.; Liu, H. X.; Ye, J.; Li, L. R.

    2016-08-01

    As the solar photovoltaic (PV) power is applied extensively, more attentions are paid to the maintenance and fault diagnosis of PV power plants. Based on analysis of the structure of PV power station, the global partitioned gradually approximation method is proposed as a fault diagnosis algorithm to determine and locate the fault of PV panels. The PV array is divided into 16x16 blocks and numbered. On the basis of modularly processing of the PV array, the current values of each block are analyzed. The mean current value of each block is used for calculating the fault weigh factor. The fault threshold is defined to determine the fault, and the shade is considered to reduce the probability of misjudgments. A fault diagnosis system is designed and implemented with LabVIEW. And it has some functions including the data realtime display, online check, statistics, real-time prediction and fault diagnosis. Through the data from PV plants, the algorithm is verified. The results show that the fault diagnosis results are accurate, and the system works well. The validity and the possibility of the system are verified by the results as well. The developed system will be benefit for the maintenance and management of large scale PV array.

  3. A data-driven method to enhance vibration signal decomposition for rolling bearing fault analysis

    NASA Astrophysics Data System (ADS)

    Grasso, M.; Chatterton, S.; Pennacchi, P.; Colosimo, B. M.

    2016-12-01

    Health condition analysis and diagnostics of rotating machinery requires the capability of properly characterizing the information content of sensor signals in order to detect and identify possible fault features. Time-frequency analysis plays a fundamental role, as it allows determining both the existence and the causes of a fault. The separation of components belonging to different time-frequency scales, either associated to healthy or faulty conditions, represents a challenge that motivates the development of effective methodologies for multi-scale signal decomposition. In this framework, the Empirical Mode Decomposition (EMD) is a flexible tool, thanks to its data-driven and adaptive nature. However, the EMD usually yields an over-decomposition of the original signals into a large number of intrinsic mode functions (IMFs). The selection of most relevant IMFs is a challenging task, and the reference literature lacks automated methods to achieve a synthetic decomposition into few physically meaningful modes by avoiding the generation of spurious or meaningless modes. The paper proposes a novel automated approach aimed at generating a decomposition into a minimal number of relevant modes, called Combined Mode Functions (CMFs), each consisting in a sum of adjacent IMFs that share similar properties. The final number of CMFs is selected in a fully data driven way, leading to an enhanced characterization of the signal content without any information loss. A novel criterion to assess the dissimilarity between adjacent CMFs is proposed, based on probability density functions of frequency spectra. The method is suitable to analyze vibration signals that may be periodically acquired within the operating life of rotating machineries. A rolling element bearing fault analysis based on experimental data is presented to demonstrate the performances of the method and the provided benefits.

  4. Fault detection of feed water treatment process using PCA-WD with parameter optimization.

    PubMed

    Zhang, Shirong; Tang, Qian; Lin, Yu; Tang, Yuling

    2017-04-03

    Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T(2) and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA-WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCA-WD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000MW generation units in a coal-fired power plant, is taken as a study case. Its operation data is collected for following verification study. The results show that the optimized WD is effective to restrain the noises of T(2) and SPE statistics, so as to improve the performance of PCA-WD algorithm. And, the parameter optimization enables PCA-WD to get its optimal parameters in an automatic way rather than on individual experience. The optimized PCA-WD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCA-WD, against classical PCA and SWPCA, is finally convinced with the results.

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1996-03-01

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

  7. Incipient Fault Detection Using Higher-Order Statistics

    DTIC Science & Technology

    1991-08-01

    109 5.2 Simulated Wear Experiment..................................109 5.2. 1 Experimental Design ... Design .................................... 128 5.3.2 Collected Data.........................................130 5.3.3 Results...detecting crankshaft drill wear (Liu and Wiu, 1990) using thrust force and axial acceleration amplitude signals. Acoustic emission spectrum features and

  8. Fault detection, diagnosis, and data-driven modeling in HVAC chillers

    NASA Astrophysics Data System (ADS)

    Namburu, Setu M.; Luo, Jianhui; Azam, Mohammad; Choi, Kihoon; Pattipati, Krishna R.

    2005-05-01

    Heating, Ventilation and Air Conditioning (HVAC) systems constitute the largest portion of energy consumption equipment in residential and commercial facilities. Real-time health monitoring and fault diagnosis is essential for reliable and uninterrupted operation of these systems. Existing fault detection and diagnosis (FDD) schemes for HVAC systems are only suitable for a single operating mode with small numbers of faults, and most of the schemes are systemspecific. A generic real-time FDD scheme, applicable to all possible operating conditions, can significantly reduce HVAC equipment downtime, thus improving the efficiency of building energy management systems. This paper presents a FDD methodology for faults in centrifugal chillers. The FDD scheme compares the diagnostic performance of three data-driven techniques, namely support vector machines (SVM), principal component analysis (PCA), and partial least squares (PLS). In addition, a nominal model of a chiller that can predict system response under new operating conditions is developed using PLS. We used the benchmark data on a 90-ton real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables under nominal and eight fault conditions of different severities at twenty seven operating modes.

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

    NASA Astrophysics Data System (ADS)

    Li, Bing; Zhang, Xining; Wu, Jili

    2017-02-01

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

  10. 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.

  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. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing

    PubMed Central

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery. PMID

  13. Fault Diagnosis for Rotating Machinery: A Method based on Image Processing.

    PubMed

    Lu, Chen; Wang, Yang; Ragulskis, Minvydas; Cheng, Yujie

    2016-01-01

    Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.

  14. Aircraft engine sensor fault diagnostics using an on-line OBEM update method

    PubMed Central

    Liu, Xiaofeng; Xue, Naiyu; Yuan, Ye

    2017-01-01

    This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM) to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI) system, in which a Hybrid Kalman Filter (HKF) was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV) model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault. PMID:28182692

  15. Aircraft engine sensor fault diagnostics using an on-line OBEM update method.

    PubMed

    Liu, Xiaofeng; Xue, Naiyu; Yuan, Ye

    2017-01-01

    This paper proposed a method to update the on-line health reference baseline of the On-Board Engine Model (OBEM) to maintain the effectiveness of an in-flight aircraft sensor Fault Detection and Isolation (FDI) system, in which a Hybrid Kalman Filter (HKF) was incorporated. Generated from a rapid in-flight engine degradation, a large health condition mismatch between the engine and the OBEM can corrupt the performance of the FDI. Therefore, it is necessary to update the OBEM online when a rapid degradation occurs, but the FDI system will lose estimation accuracy if the estimation and update are running simultaneously. To solve this problem, the health reference baseline for a nonlinear OBEM was updated using the proposed channel controller method. Simulations based on the turbojet engine Linear-Parameter Varying (LPV) model demonstrated the effectiveness of the proposed FDI system in the presence of substantial degradation, and the channel controller can ensure that the update process finishes without interference from a single sensor fault.

  16. 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.

  17. 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.

  18. 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.

  19. A planar EMT system for the detection of faults on thin metallic plates

    NASA Astrophysics Data System (ADS)

    Yin, W.; Peyton, A. J.

    2006-08-01

    This paper describes a novel planar electromagnetic tomography system for the detection of conductivity inhomogeneity on a metallic plate. The proposed system differs from traditional electromagnetic inductance tomography (EMT) systems in its spatial arrangements of coils. Sensor coils are distributed to form a circular array with their axes not parallel but perpendicular to the plate under inspection. The forward solution for the sensor array next to a homogeneous conductive plate is based on the analytical solution provided by Cheng. The sensitivity matrix for a prototype sensor was computed by numerical evaluation of the analytical solution. For the inverse solution, a modified Newton-Raphson method was used to adjust the conductivity distribution to fit a set of inductances measured from the sensor array in a least-squared sense. Frequency- dependent sensitivity analysis was performed to find an optimum testing frequency. The far-field and near-field effects in electrical tomography are discussed. Good estimates for the conductivity distribution were obtained at the optimum frequency. Experimental tests were performed by taking the difference in mutual inductance of the coil pairs when placed next to a homogeneous reference conductor and next to a conductor with faults. Inverse results based on experimental data verified this method.

  20. 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.

  1. Threshold of Geomorphic Detectability Estimated from Geologic Observations of Active Slow-Slipping Strike-Slip Faults

    NASA Astrophysics Data System (ADS)

    Kaneda, H.

    2002-12-01

    Sources of catastrophic earthquakes include not only major active faults, but also slow-slipping ones. However, geomorphic characteristics and long-term seismic behavior of slow-slipping faults have not been well understood, although intensive paleoseismic studies were carried out after the unexpected 1992 Landers and 1999 Hector Mine earthquakes. Two Japanese surface faulting earthquakes on slow-slipping strike-slip faults (the 1927 Mw=7.0 Kita-Tango and 1943 Mw=7.0 Tottori earthquakes) provided good opportunity to examine these problems. Analysis of coseismic surface slip, cumulative geomorphic expressions, and paleoseismicity for these two events not only supports a characteristic-slip behavior for these faults, but also suggests a concept of threshold of geomorphic detectability for intramontane strike-slip faults, which must be exceeded in order that progressive coseismic surface offsets can be preserved against surface processes as detectable systematic deflections of channels and ridge crests. The determined threshold slip rates for these examples are in the range of 0.06-0.13 mm/yr, which can be a quantitative explanation for an extremely small number of mapped active faults with slip rates of less than 0.1 mm/yr in Japan islands. On the contrary, the threshold of geomorphic detectability is probably negligible in arid regions where denudation rate would be extremely low. To date, the issue of geomorphologically undetectable active faults has been that of blind thrust faults buried beneath thick sediments, but another type of blind active faults or fault segments can exist in humid and mountainous regions. In spite of their low slip rates and long recurrence intervals, their potential presence must be considered, especially in regions under the tectonically undeveloped regime, where regional strain is accommodated by many scattered slow-slipping faults.

  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. 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.

  4. 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.

  5. A Tool to Assist Pressure Management by Detecting and Localizing Low Permeability Faults

    NASA Astrophysics Data System (ADS)

    Vilarrasa, V.; Bustarret, G.; Laloui, L.

    2015-12-01

    Fluid injection and its subsequent induced seismicity have significantly increased recently. Injection of fluids due to wastewater disposal, geothermal energy, seasonal natural gas storage and geologic carbon storage causes pressure buildup, which reduces the effective stresses. This reduction brings the stress state closer to failure conditions, which may yield shear slip of pre-existing fractures or faults. Shear slip induces seismic events, which in some cases are felt by the local population. Felt induced seismicity negatively affects public acceptance and may lead to the closure of injection projects. To avoid inducing felt seismic events, a good pressure management is crucial. We propose a methodology to identify and locate undetected low permeability faults using diagnostic plots. This method is useful to assist decision making to adopt the proper mitigation measures to keep overpressure below the maximum sustainable injection pressure when a low permeability fault is causing an additional overpressure within the injection formation. Diagnostic tools allow a rapid identification of the divergence between the pressure measurements and the expected overpressure evolution in a homogeneous aquifer. The divergence time is an indicator of the presence of a low permeability fault and can be used to determine its position. We formulate the problem in its dimensionless form, so it can be generalized to all injection sites. We apply our methodology to water and CO2 injection through a horizontal well in a confined aquifer that has a fault parallel to the well. Nevertheless, the methodology can be extended to other geometrical configurations and geological settings.

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

    NASA Astrophysics Data System (ADS)

    Ushaq, Muhammad; Fang, Jiancheng

    2013-10-01

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

  7. 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.

  8. Novel synthetic index-based adaptive stochastic resonance method and its application in bearing fault diagnosis

    NASA Astrophysics Data System (ADS)

    Zhou, Peng; Lu, Siliang; Liu, Fang; Liu, Yongbin; Li, Guihua; Zhao, Jiwen

    2017-03-01

    Stochastic resonance (SR), which is characterized by the fact that proper noise can be utilized to enhance weak periodic signals, has been widely applied in weak signal detection. SR is a nonlinear parameterized filter, and the output signal relies on the system parameters for the deterministic input signal. The most commonly used index for parameter tuning in the SR procedure is the signal-to-noise ratio (SNR). However, using the SNR index to evaluate the denoising effect of SR quantitatively is insufficient when the target signal frequency cannot be estimated accurately. To address this issue, six different indexes, namely, power spectral kurtosis of the SR output signal, correlation coefficient between the SR output and the original signal, peak SNR, structural similarity, root mean square error, and smoothness, are constructed in this study to measure the SR output quantitatively. These six quantitative indexes are fused into a new synthetic quantitative index (SQI) via a back propagation neural network to guide the adaptive parameter selection of the SR procedure. The index fusion procedure reduces the instability of each index and thus improves the robustness of parameter tuning. In addition, genetic algorithm is utilized to quickly select the optimal SR parameters. The efficiency of bearing fault diagnosis is thus further improved. The effectiveness and efficiency of the proposed SQI-based adaptive SR method for bearing fault diagnosis are verified through numerical and experiment analyses.

  9. 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

  10. A microprocessor-based digital feeder monitor with high-impedance fault detection

    SciTech Connect

    Patterson, R.; Tyska, W.; Russell, B.D.

    1994-12-31

    The high impedance fault detection technology developed at Texas A&M University after more than a decade of research, funded in large part by the Electric Power Research Institute, has been incorporated into a comprehensive monitoring device for overhead distribution feeders. This digital feeder monitor (DFM) uses a high waveform sampling rate for the ac current and voltage inputs in conjunction with a high-performance reduced instruction set (RISC) microprocessor to obtain the frequency response required for arcing fault detection and power quality measurements. Expert system techniques are employed to assure security while maintaining dependability. The DFM is intended to be applied at a distribution substation to monitor one feeder. The DFM is packaged in a non-drawout case which fits the panel cutout for a GE IAC overcurrent relay to facilitate retrofits at the majority of sites were electromechanical overcurrent relays already exist.

  11. 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

  12. 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

    2014-11-14

    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.

  13. 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.

  14. 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.

  15. Blockage fault diagnosis method of combine harvester based on BPNN and DS evidence theory

    NASA Astrophysics Data System (ADS)

    Chen, Jin; Xu, Kai; Wang, Yifan; Wang, Kun; Wang, Shuqing

    2017-01-01

    According to the complexity and the lack of intelligent analysis method of combine harvester blockage fault , this paper puts forward a method , based on the combination of BP neural network (BPNN)and DS evidence theory , for combine harvester blockage fault diagnosis. Choosing cutting table auger, conveyer trough, threshing cylinder and grain conveying auger as the study, this paper divides the condition of combine harvester into four categories, namely, normal, slightly blocking, blockage, severe blockage, which being as an identification framework for DS evidence theory. BP neural network is used for analysing speed information of monitoring points and distributing basic probability for each proposition in the identification framework. Dempster combination rule converged information at different time to obtain diagnostic results.Test results show that this method can timely and accurately judge the work state of combine harvester, the blocking fault warning time will be increased to 2 seconds and the success probability of blocking fault warning reach more than 90%.

  16. Elastoplastic dynamic analysis of strike-slip faults with bends using finite element method

    NASA Astrophysics Data System (ADS)

    Duan, B.; Day, S. M.

    2006-12-01

    Nonelastic off-fault response may play a role in rupture dynamics on geometrically complex faults, particularly in the vicinity of bends or other points of stress concentration. In this study, we have performed nonelastic dynamic analysis of strike-slip faults with bends by using a finite element method. The Coulomb yield criterion has been implemented in the code to model off-fault nonelastic response. We find that a smooth scheme (such as viscoplasticity) is required to regularize the numerical calculation of plastic yielding near a fault bend. The method is extensible to other material rheologies (e.g., damage mechanics models, tensile failure, etc), and amenable to parallel implementation. Compared with those from a calculation with elastic off-fault response, results from a calculation with nonelastic off-fault response show that (1) bends are locations of large plastic deformation; (2) stress near a bend is less heterogeneous; (3) less radiation is generated from a bend; (4) lower strong ground motion is produced.

  17. Event-Triggered Fault Detection Filter Design for a Continuous-Time Networked Control System.

    PubMed

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

    2016-12-01

    This paper studies the problem of event-triggered fault detection filter (FDF) and controller coordinated design for a continuous-time networked control system (NCS) with biased sensor faults. By considering sensor-to-FDF network-induced delays and packet dropouts, which do not impose a constraint on the event-triggering mechanism, and proposing the simultaneous network bandwidth utilization ratio and fault occurrence probability-based event-triggering mechanism, a new closed-loop model for the considered NCS is established. Based on the established model, the event-triggered H ∞ performance analysis, and FDF and controller coordinated design are presented. The combined mutually exclusive distribution and Wirtinger-based integral inequality approach is proposed for the first time to deal with integral inequalities for products of vectors. This approach is proved to be less conservative than the existing Wirtinger-based integral inequality approach. The designed FDF and controller can guarantee the sensitivity of the residual signal to faults and the robustness of the NCS to external disturbances. The simulation results verify the effectiveness of the proposed event-triggering mechanism, and the FDF and controller coordinated design.

  18. 3D Seismic Flexure Analysis for Subsurface Fault Detection and Fracture Characterization

    NASA Astrophysics Data System (ADS)

    Di, Haibin; Gao, Dengliang

    2017-03-01

    Seismic flexure is a new geometric attribute with the potential of delineating subtle faults and fractures from three-dimensional (3D) seismic surveys, especially those overlooked by the popular discontinuity and curvature attributes. Although the concept of flexure and its related algorithms have been published in the literature, the attribute has not been sufficiently applied to subsurface fault detection and fracture characterization. This paper provides a comprehensive study of the flexure attribute, including its definition, computation, as well as geologic implications for evaluating the fundamental fracture properties that are essential to fracture characterization and network modeling in the subsurface, through applications to the fractured reservoir at Teapot Dome, Wyoming (USA). Specifically, flexure measures the third-order variation of the geometry of a seismic reflector and is dependent on the measuring direction in 3D space; among all possible directions, flexure is considered most useful when extracted perpendicular to the orientation of dominant deformation; and flexure offers new insights into qualitative/quantitative fracture characterization, with its magnitude indicating the intensity of faulting and fracturing, its azimuth defining the orientation of most-likely fracture trends, and its sign differentiating the sense of displacement of faults and fractures.

  19. 3D Seismic Flexure Analysis for Subsurface Fault Detection and Fracture Characterization

    NASA Astrophysics Data System (ADS)

    Di, Haibin; Gao, Dengliang

    2016-10-01

    Seismic flexure is a new geometric attribute with the potential of delineating subtle faults and fractures from three-dimensional (3D) seismic surveys, especially those overlooked by the popular discontinuity and curvature attributes. Although the concept of flexure and its related algorithms have been published in the literature, the attribute has not been sufficiently applied to subsurface fault detection and fracture characterization. This paper provides a comprehensive study of the flexure attribute, including its definition, computation, as well as geologic implications for evaluating the fundamental fracture properties that are essential to fracture characterization and network modeling in the subsurface, through applications to the fractured reservoir at Teapot Dome, Wyoming (USA). Specifically, flexure measures the third-order variation of the geometry of a seismic reflector and is dependent on the measuring direction in 3D space; among all possible directions, flexure is considered most useful when extracted perpendicular to the orientation of dominant deformation; and flexure offers new insights into qualitative/quantitative fracture characterization, with its magnitude indicating the intensity of faulting and fracturing, its azimuth defining the orientation of most-likely fracture trends, and its sign differentiating the sense of displacement of faults and fractures.

  20. Analysis of the vibratory excitation of gear systems for fault detection in rotating machinery

    SciTech Connect

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

    1997-07-01

    The concepts of model-based fault detection for vibration condition monitoring of rotating machinery are discussed and presented in this paper. The mathematical model presented and fully developed in the earlier works is further modified so it incorporates a typical gear tooth irregularity fault. This fault was simulated on the contact line of the gear model. The results obtained from this analytical model are compared with the ones obtained from a model drive-line. The drive-line consists of a number of rotating parts including an electric motor, a gear system, and a disk brake. The gear system has two meshing spur gears which is equivalent to the analytical model. The comparison of the results are very good as some vibration frequencies of the analytical results correlates with the experimental ones. it is shown that certain vibration frequencies of a real experimental model gear system can be obtained from its analytical counterpart. It is also shown that it is possible to model a typical gear tooth fault by simulating a forcing function as a shock to the modelled system.

  1. 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.

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

    PubMed

    Naderi, Peyman

    2016-09-01

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

  3. 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

  4. 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.

  5. Is the Multigrid Method Fault Tolerant? The Two-Grid Case

    SciTech Connect

    Ainsworth, Mark; Glusa, Christian

    2016-06-30

    The predicted reduced resiliency of next-generation high performance computers means that it will become necessary to take into account the effects of randomly occurring faults on numerical methods. Further, in the event of a hard fault occurring, a decision has to be made as to what remedial action should be taken in order to resume the execution of the algorithm. The action that is chosen can have a dramatic effect on the performance and characteristics of the scheme. Ideally, the resulting algorithm should be subjected to the same kind of mathematical analysis that was applied to the original, deterministic variant. The purpose of this work is to provide an analysis of the behaviour of the multigrid algorithm in the presence of faults. Multigrid is arguably the method of choice for the solution of large-scale linear algebra problems arising from discretization of partial differential equations and it is of considerable importance to anticipate its behaviour on an exascale machine. The analysis of resilience of algorithms is in its infancy and the current work is perhaps the first to provide a mathematical model for faults and analyse the behaviour of a state-of-the-art algorithm under the model. It is shown that the Two Grid Method fails to be resilient to faults. Attention is then turned to identifying the minimal necessary remedial action required to restore the rate of convergence to that enjoyed by the ideal fault-free method.

  6. 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.

  7. Fault finder

    DOEpatents

    Bunch, Richard H.

    1986-01-01

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

  8. 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.

  9. 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.

  10. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2017-03-01

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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Compactness vs. Smoothness: Methods for regularizing fault slip inversions with application to subduction zone earthquakes.

    NASA Astrophysics Data System (ADS)

    Lohman, R. B.; Simons, M.

    2004-12-01

    We examine inversions of geodetic data for fault slip and discuss how inferred results are affected by choices of regularization. The final goal of any slip inversion is to enhance our understanding of the dynamics governing fault zone processes through kinematic descriptions of fault zone behavior at various temporal and spatial scales. Important kinematic observations include ascertaining whether fault slip is correlated with topographic and gravitational anomalies, whether coseismic and postseismic slip occur on complementary or overlapping regions of the fault plane, and how aftershock distributions compare with areas of coseismic and postseismic slip. Fault slip inversions are generally poorly-determined inverse problems requiring some sort of regularization. Attempts to place inversion results in the context of understanding fault zone processes should be accompanied by careful treatment of how the applied regularization affects characteristics of the inferred slip model. Most regularization techniques involve defining a metric that quantifies the solution "simplicity". A frequently employed method defines a "simple" slip distribution as one that is spatially smooth, balancing the fit to the data vs. the spatial complexity of the slip distribution. One problem related to the use of smoothing constraints is the "smearing" of fault slip into poorly-resolved areas on the fault plane. In addition, even if the data is fit well by a point source, the fact that a point source is spatially "rough" will force the inversion to choose a smoother model with slip over a broader area. Therefore, when we interpret the area of inferred slip we must ask whether the slipping area is truly constrained by the data, or whether it could be fit equally well by a more spatially compact source with larger amplitudes of slip. We introduce an alternate regularization technique for fault slip inversions, where we seek an end member model that is the smallest region of fault slip that

  18. Fault detection, isolation, and diagnosis of status self-validating gas sensor arrays.

    PubMed

    Chen, Yin-Sheng; Xu, Yong-Hui; Yang, Jing-Li; Shi, Zhen; Jiang, Shou-da; Wang, Qi

    2016-04-01

    The traditional gas sensor array has been viewed as a simple apparatus for information acquisition in chemosensory systems. Gas sensor arrays frequently undergo impairments in the form of sensor failures that cause significant deterioration of the performance of previously trained pattern recognition models. Reliability monitoring of gas sensor arrays is a challenging and critical issue in the chemosensory system. Because of its importance, we design and implement a status self-validating gas sensor array prototype to enhance the reliability of its measurements. A novel fault detection, isolation, and diagnosis (FDID) strategy is presented in this paper. The principal component analysis-based multivariate statistical process monitoring model can effectively perform fault detection by using the squared prediction error statistic and can locate the faulty sensor in the gas sensor array by using the variables contribution plot. The signal features of gas sensor arrays for different fault modes are extracted by using ensemble empirical mode decomposition (EEMD) coupled with sample entropy (SampEn). The EEMD is applied to adaptively decompose the original gas sensor signals into a finite number of intrinsic mode functions (IMFs) and a residual. The SampEn values of each IMF and the residual are calculated to reveal the multi-scale intrinsic characteristics of the faulty sensor signals. Sparse representation-based classification is introduced to identify the sensor fault type for the purpose of diagnosing deterioration in the gas sensor array. The performance of the proposed strategy is compared with other different diagnostic approaches, and it is fully evaluated in a real status self-validating gas sensor array experimental system. The experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID of status self-validating gas sensor arrays.

  19. Fault detection, isolation, and diagnosis of status self-validating gas sensor arrays

    NASA Astrophysics Data System (ADS)

    Chen, Yin-sheng; Xu, Yong-hui; Yang, Jing-li; Shi, Zhen; Jiang, Shou-da; Wang, Qi

    2016-04-01

    The traditional gas sensor array has been viewed as a simple apparatus for information acquisition in chemosensory systems. Gas sensor arrays frequently undergo impairments in the form of sensor failures that cause significant deterioration of the performance of previously trained pattern recognition models. Reliability monitoring of gas sensor arrays is a challenging and critical issue in the chemosensory system. Because of its importance, we design and implement a status self-validating gas sensor array prototype to enhance the reliability of its measurements. A novel fault detection, isolation, and diagnosis (FDID) strategy is presented in this paper. The principal component analysis-based multivariate statistical process monitoring model can effectively perform fault detection by using the squared prediction error statistic and can locate the faulty sensor in the gas sensor array by using the variables contribution plot. The signal features of gas sensor arrays for different fault modes are extracted by using ensemble empirical mode decomposition (EEMD) coupled with sample entropy (SampEn). The EEMD is applied to adaptively decompose the original gas sensor signals into a finite number of intrinsic mode functions (IMFs) and a residual. The SampEn values of each IMF and the residual are calculated to reveal the multi-scale intrinsic characteristics of the faulty sensor signals. Sparse representation-based classification is introduced to identify the sensor fault type for the purpose of diagnosing deterioration in the gas sensor array. The performance of the proposed strategy is compared with other different diagnostic approaches, and it is fully evaluated in a real status self-validating gas sensor array experimental system. The experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID of status self-validating gas sensor arrays.

  20. A New 600 V Punch Through-Insulated Gate Bipolar Transistor with the Monolithic Fault Protection Circuit Using the Floating p-Well Voltage Detection

    NASA Astrophysics Data System (ADS)

    Ji, In-Hwan; Jeon, Byung-Chul; Choi, Young-Hwan; Ha, Min-Woo; Han, Min-Koo

    2006-10-01

    A new fault sensing scheme of the insulated gate bipolar transistor (IGBT) employing the floating p-well, which detects the over-voltage of the floating p-well under the short circuit fault condition, is proposed and implemented by fabricating the main IGBT and gate voltage pull-down circuit using the widely used planar IGBT process. The floating p-well structure also improves the avalanche energy of IGBT in addition to detecting the fault signal. The detection of fault and gate voltage pull-down operation is achieved by the proposed fault protection scheme employing the floating p-well voltage detection. The proposed fault protection circuit was measured under the hard switching fault (HSF) and fault under load (FUL) conditions. The normal switching behavior of the main IGBT with the proposed protection circuit was also investigated under inductive load switching conditions.