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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. Fault Detection and Diagnosis Method for VAV Terminal Units 

    E-print Network

    Miyata, M.; Yoshida, H.; Asada, M.; Wang, F.; Hashiguchi, S.

    2004-01-01

    This paper proposes two fault detection and diagnosis methods for VAV units without a sensor of supply air volume, and the results of applying these methods to a real building are presented. One method detects faults by applying a statistical method...

  3. Active fault detection: A comparison of probabilistic methods

    NASA Astrophysics Data System (ADS)

    Škach, Jan; Pun?ochá?, Ivo

    2015-11-01

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

  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. Improved Hidden-Markov-Model Method Of Detecting Faults

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic J.

    1994-01-01

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

  6. 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.

  7. A Comparison of Fault Detection Methods For a Transcritical Refrigeration System 

    E-print Network

    Janecke, Alex Karl

    2012-10-19

    in the 1970’s. Wilsky summarized many of the early fault detection and diagnosis (FDD) schemes used [7]. Clark gave an early observer based method with the example of a hydrofoil [8]. Overviews of dynamic fault detection as a general field can be found...

  8. 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.

  9. Solar system fault detection

    DOEpatents

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

    1986-01-01

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

  10. A novel method for high-performance fault detection of induction machine

    NASA Astrophysics Data System (ADS)

    Su, Hua; Kim, Yeong-Min; Chong, Kil To

    2005-12-01

    Induction machine is probably the most commonly utilized electromechanical device in modern society. However, there are many undesirable problems arising in the machine operation of industrial plants. It is desirable for early detection and diagnosis of incipient faults for online condition monitoring, product quality assurance, and improved operational efficiency of induction motors. In this paper, a high-performance residual-based novel method is developed for induction machine fault detection, using Fourier-based signal processing for steady-state vibration signals. The proposed approach uses only motor vibration measurements without the nameplate information. The reference model in spectra is obtained statistically to represent the healthy condition. The effectiveness of the proposed approach in detecting a wide range of mechanical and electrical faults is demonstrated through staged motor faults, and it is shown that a robust and reliable induction machine fault detection system has been produced.

  11. 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.

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

    SciTech Connect

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

    1989-05-30

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

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

    DOEpatents

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

    2010-12-07

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

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

    DOEpatents

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

    2010-08-17

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

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

    DOEpatents

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

    2008-06-03

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

  16. Model-Based Methods for Textile Fault Detection J. G. Campbell,1

    E-print Network

    Raftery, Adrian

    Model-Based Methods for Textile Fault Detection J. G. Campbell,1 C. Fraley,2,3 D. Stanford,2 F for woven textiles in discriminating subtle flaw patterns from the pronounced background of repetitive. Int J Imaging Syst Technol, 10, 339­346, 1999 I. FLAW DETECTION IN TEXTILE FABRIC Obstacles to machine

  17. 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.

  18. Fault Detection and Classification

    NASA Astrophysics Data System (ADS)

    Scanlan, John

    2004-09-01

    Plasma processes are used widely in the manufacture of semiconductor devices. Recent trends in this industry have focussed on methods for automated process control. For limiting processes such as plasma etch, an emerging focus is on real time Fault Detection and Classification (FDC). Simply put, the aim is to provide a system that not only detects faults but also identifies the root cause. For example, semiconductor production fabs regularly encounter faults that result in unscheduled tool downtime and reduced yield. Among these are real-time process and tool faults, post maintenance recovery problems and tool mis-matching at start-up and process transfer. The objective of any FDC scheme should be to reduce this product loss and tool downtime by identifying the core problem as rapidly as possible, and replace the usual "trial-and-error" approach to fault identification. There are a couple of key requirements in any control system. Firstly, an estimation of the process state, and secondly, a scheme for providing real-time control. This paper focuses on methods for addressing both problems on plasma etch tools. A non-intrusive high-resolution RF sensor is used to provide in situ process-state and tool-state data. Examples will be presented on how such a sensor can give a repeatable fingerprint of any plasma process. The challenge then becomes the manipulation of this data into usable information. The process control scheme presented is knowledge-based, in that it is trained and does not rely on statistical methods with underlying assumptions of Gaussian data spread. A fingerprint of known fault states is the knowledge set and real-time control is provided by comparison of the sensor fingerprint to the fault fingerprints.

  19. 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.

  20. Robust Fault Detection

    NASA Technical Reports Server (NTRS)

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

    2003-01-01

    This research used mixed structured singular value theory to develop new estimator (or observer) based approaches to fault detection for dynamic systems. The initial developments were based on minimizing the H-infinity, I-1 and H2 system norms. The resultant fault detection algorithms were each shown to be successful, but the fault detection algorithm based on the I-1 norm was best able to detect abrupt faults. This latter technique was further improved by using fuzzy logic for the fault evaluation. Based on an anomaly observed in this research and apparently ignored in the literature, current research focuses on the determination of a fault using a norm of the change in the residual (the difference between the output of the system and observer) and not simply a norm of the residual itself. This research may lead to a fundamental contribution to research in fault detection and isolation.

  1. Presented at Institute of Navigation GPS2002 (September 24-27, 2002, Portland, OR) 1 Fault Detection Methods And Testing

    E-print Network

    Calgary, University of

    Presented at Institute of Navigation GPS2002 (September 24-27, 2002, Portland, OR) 1 Fault at the same institution. He has previous experience in GPS related R&D at NovAtel Inc. and Trimble Navigation and measure the impact of undetected faults in a navigation system. Fault detection methods and testing

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

    DOEpatents

    Parlos, Alexander G; Kim, Kyusung

    2003-07-08

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

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

    PubMed

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

    2012-01-01

    Studies on fault detection and diagnosis of planetary gearboxes are quite limited compared with those of fixed-axis gearboxes. Different from fixed-axis gearboxes, planetary gearboxes exhibit unique behaviors, which invalidate fault diagnosis methods that work well for fixed-axis gearboxes. It is a fact that for systems as complex as planetary gearboxes, multiple sensors mounted on different locations provide complementary information on the health condition of the systems. On this basis, a fault detection method based on multi-sensor data fusion is introduced in this paper. In this method, two features developed for planetary gearboxes are used to characterize the gear health conditions, and an adaptive neuro-fuzzy inference system (ANFIS) is utilized to fuse all features from different sensors. In order to demonstrate the effectiveness of the proposed method, experiments are carried out on a planetary gearbox test rig, on which multiple accelerometers are mounted for data collection. The comparisons between the proposed method and the methods based on individual sensors show that the former achieves much higher accuracies in detecting planetary gearbox faults. PMID:22438750

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

    PubMed 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

  6. Randomness fault detection system

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

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

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

    PubMed

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

    2015-02-01

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

  8. Fault detection and isolation

    NASA Technical Reports Server (NTRS)

    Bernath, Greg

    1994-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  11. 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.

  12. Application of novelty detection methods to health monitoring and typical fault diagnosis of a turbopump

    NASA Astrophysics Data System (ADS)

    Hu, Lei; Hu, Niaoqing; Fan, Bin; Gu, Fengshou

    2012-05-01

    Novelty detection is the identification of deviations from a training set. It is suitable for monitoring the health of mechanical systems where it usually is impossible to know every potential fault. In this paper, two novelty detectors are presented. The first detector which integrates One-Class Support Vector Machine (OCSVM) with an incremental clustering algorithm is designed for health monitoring of the turbopump, while the second one which is trained on sensor fault samples is designed to recognize faults from sensors and faults actually from the turbopump. Analysis results showed that these two detectors are both sensitive and efficient for the health monitoring of the turbopump.

  13. Fault Tolerant Quantum Filtering and Fault Detection for Quantum Systems

    E-print Network

    Qing Gao; Daoyi Dong; Ian R. Petersen

    2015-12-21

    This paper aims to determine the fault tolerant quantum filter and fault detection equation for a class of open quantum systems coupled to a laser field that is subject to stochastic faults. In order to analyze this class of open quantum systems, we propose a quantum-classical Bayesian inference method based on the definition of a so-called quantum-classical conditional expectation. It is shown that the proposed Bayesian inference approach provides a convenient tool to simultaneously derive the fault tolerant quantum filter and the fault detection equation for this class of open quantum systems. An example of two-level open quantum systems subject to Poisson-type faults is presented to illustrate the proposed method. These results have the potential to lead to a new fault tolerant control theory for quantum systems.

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

    NASA Technical Reports Server (NTRS)

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

    2013-01-01

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

  15. 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.

  16. Fault Tolerant Quantum Filtering and Fault Detection for Quantum Systems

    E-print Network

    Qing Gao; Daoyi Dong; Ian R. Petersen

    2015-04-26

    This paper aims to determine the fault tolerant quantum filter and fault detection equation for a class of open quantum systems coupled to laser fields and subject to stochastic faults. In order to analyze open quantum systems where the system dynamics involve both classical and quantum random variables, a quantum-classical probability space model is developed. Using a reference probability approach, a fault tolerant quantum filter and a fault detection equation are simultaneously derived for this class of open quantum systems. An example of two-level open quantum systems subject to Poisson-type faults is presented to illustrate the proposed method. These results have the potential to lead to a new fault tolerant control theory for quantum systems.

  17. Fault detection methods for vapor-compression air conditioners using electrical measurements

    E-print Network

    Laughman, Christopher Reed.

    2008-01-01

    (cont.) This method was experimentally tested and validated on a commercially available air handler and duct system. In the second class of faults studied, liquid refrigerant, rather than vapor, enters the cylinder of a ...

  18. 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.

  19. Row fault detection system

    DOEpatents

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

    2008-10-14

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

  20. Arc burst pattern analysis fault detection system

    NASA Technical Reports Server (NTRS)

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

    1997-01-01

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

  1. A Boosting Method for Process Fault Detection with Detection Delay Reduction and Label Denoising

    E-print Network

    Vucetic, Slobodan

    Mihajlo Grbovic, Slobodan Vucetic Department of Computer and Information Sciences, Temple University Philadelphia, PA 19122, USA {mihajlo.grbovic, slobodan.vucetic} @temple.edu Weichang Li, Peng Xu, Adam K. Usadi include low detection delay time, low false positive rate, and high detection probability. Early detection

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Arc fault detection system

    DOEpatents

    Jha, Kamal N. (Bethel Park, PA)

    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.

  7. 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.

  8. 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.

  9. Row fault detection system

    DOEpatents

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

    2010-02-23

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

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

    NASA Astrophysics Data System (ADS)

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

    1999-11-01

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

  11. 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.

  12. Cell boundary fault detection system

    DOEpatents

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

    2009-05-05

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

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

    DOEpatents

    Hagen, Ronald A. (Stillwater, MN); Comte, Christophe (Montreal, CA); Knudson, Orlin B. (Vadnais Heights, MN); Rosenthal, Brian (Stillwater, MN); Rouillard, Jean (Saint-Luc, CA)

    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.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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)

    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.

  19. The Fault Detection Problem Andreas Haeberlen

    E-print Network

    Ives, Zachary G.

    The Fault Detection Problem Andreas Haeberlen Petr Kuznetsov Abstract One of the most important challenges in distributed computing is ensuring that services are correct and available despite faults. Recently it has been argued that fault detection can be factored out from computation, and that a generic

  20. The Fault Detection Problem Andreas Haeberlen1

    E-print Network

    Ives, Zachary G.

    The Fault Detection Problem Andreas Haeberlen1 and Petr Kuznetsov2 1 Max Planck Institute challenges in distributed com- puting is ensuring that services are correct and available despite faults. Recently it has been argued that fault detection can be factored out from computation, and that a generic

  1. 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.

  2. Evaluation of a Decoupling-Based Fault Detection and Diagnostic Technique - Part I: Field Emulation Evaluation 

    E-print Network

    Li, H.; Braun, J.

    2006-01-01

    Existing methods addressing automated fault detection and diagnosis (FDD) for vapor compression air conditioning system have good performance for faults that occur individually, but they have difficulty in handling multiple-simultaneous faults...

  3. Fault detection and diagnosis capabilities of test sequence selection

    E-print Network

    Thulsiraman, Krishnaiyan

    Review Fault detection and diagnosis capabilities of test sequence selection methods based on the FSM model T Ramalingam*, Anindya Dast and K ThuIasiraman* Different test sequence selection methods resolution in diagnosing the fault. The test sequence selection methods are then compared based on the length

  4. Observer-based fault detection for nuclear reactors

    E-print Network

    Li, Qing, 1972-

    2001-01-01

    This is a study of fault detection for nuclear reactor systems. Basic concepts are derived from fundamental theories on system observers. Different types of fault- actuator fault, sensor fault, and system dynamics fault ...

  5. Fault detection in dielectric grid scatterers.

    PubMed

    Brancaccio, Adriana; Solimene, Raffaele

    2015-04-01

    The problem of diagnosing a grid of small (in terms of the probing wavelength) dielectric scatterers is considered. The aim is to detect and locate possible defects occurring within a known grid when one (or more) scatterer is removed/missing (fault). The study is developed for the canonical case of a TM scalar two-dimensional geometry with the scatterers consisting of dielectric cylinders of small circular cross section. The scattering by a fault is modeled by relaying only to a priori information about the complete grid which leads to a numerically effective inversion procedures as the bulk of the numerical effort is to be done only once. Inversion is achieved by a truncated singular value decomposition scheme and results are provided in terms of closed form expressions for the probability of detection and of false alarm. This allows us to foreseen the achievable performance and to highlight the role of scattering configuration parameters. Numerical examples are also enclosed to corroborate theoretical outcomes. The case of two or more faults is considered as well. For such a case it is numerically shown that detection method still works well even though multiple scattering (occurring between faults) is neglected. PMID:25968659

  6. 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

  7. Model reconstruction using POD method for gray-box fault detection

    NASA Technical Reports Server (NTRS)

    Park, H. G.; Zak, M.

    2003-01-01

    This paper describes using Proper Orthogonal Decomposition (POD) method to create low-order dynamical models for the Model Filter component of Beacon-based Exception Analysis for Multi-missions (BEAM).

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

    DOEpatents

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

    1995-01-01

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

  9. 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.

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

    PubMed

    Li, Xiao-Jian; Yang, Guang-Hong

    2014-08-01

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

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

    NASA Technical Reports Server (NTRS)

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

    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.

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

    NASA Technical Reports Server (NTRS)

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

    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.

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

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  14. Output-Only Techniques for Fault Detection

    NASA Astrophysics Data System (ADS)

    Brzezinski, Adam John

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

  15. A Game Theoretic Fault Detection Filter

    NASA Technical Reports Server (NTRS)

    Chung, Walter H.; Speyer, Jason L.

    1995-01-01

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

  16. All-to-all sequenced fault detection system

    DOEpatents

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

    2010-11-02

    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.

  17. ECE 586 Fault Detection in Digital Circuits Lecture 13 Fault Simulation II

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 13 Fault Simulation II Professor Jia Wang ­ Fault Detection in Digital Circuits Spring 2015 1/17 #12;Reading Assignment This lecture: 5.2 Next lecture: 5.3 ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/17 #12;Outline Deductive Fault

  18. Detection of faults and software reliability analysis

    NASA Technical Reports Server (NTRS)

    Knight, J. C.

    1986-01-01

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

  19. 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.

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

    PubMed

    Bailey, Margaret B; Kreider, Jan F

    2003-07-01

    Reliable, automated detection and diagnosis of abnormal behavior within vapor compression refrigeration cycle (VCRC) equipment is extremely desirable for equipment owners and operators. The specific type of VCRC equipment studied in this paper is a 70-ton helical rotary, air-cooled chiller. The fault detection and diagnostic (FDD) tool developed as part of this research analyzes chiller operating data and detects faults through recognizing trends or patterns existing within the data. The FDD method incorporates a neural network (NN) classifier to infer the current state given a vector of observables. Therefore the FDD method relies upon the availability of normal and fault empirical data for training purposes and therefore a fault library of empirical data is assembled. This paper presents procedures for conducting sophisticated fault experiments on chillers that simulate air-cooled condenser, refrigerant, and oil related faults. The experimental processes described here are not well documented in literature and therefore will provide the interested reader with a useful guide. In addition, the authors provide evidence, based on both thermodynamics and empirical data analysis, that chiller performance is significantly degraded during fault operation. The chiller's performance degradation is successfully detected and classified by the NN FDD classifier as discussed in the paper's final section. PMID:12858981

  1. Predicting Fault Detection Effectiveness J. A. Morgan & G. J. Knafl W. E. Wong

    E-print Network

    Morgan, Joseph

    Predicting Fault Detection Effectiveness J. A. Morgan & G. J. Knafl W. E. Wong School of Computer Regression methods are used to model fault detection effectiveness in terms of several product and testing process measures. The relative importance of these product/process measures for predicting fault detection

  2. Outlier Detection Rules for Fault Detection in Solar Photovoltaic Arrays

    E-print Network

    Lehman, Brad

    Abstract-- Solar photovoltaic (PV) arrays are unique power sources that may have uncleared fault current/sub-array has a significant underperformance. In addition, requirement of weather conditions, such as solarOutlier Detection Rules for Fault Detection in Solar Photovoltaic Arrays Ye Zhao, Brad Lehman

  3. Evaluation of a Decoupling-Based Fault Detection and Diagnostic Technique - Part II: Field Evaluation and Application 

    E-print Network

    Li, H.; Braun, J.

    2006-01-01

    Existing methods addressing automated fault detection and diagnosis (FDD) for vapor compression air conditioning system have good performance for faults that occur individually, but they have difficulty in handling multiple-simultaneous faults...

  4. 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.

  5. Data Fault Detection in Medical Sensor Networks

    PubMed Central

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

    2015-01-01

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

  6. Data fault detection in medical sensor networks.

    PubMed

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

    2015-01-01

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

  7. ECE 586 Fault Detection in Digital Circuits Lecture 09 Fault Modeling I

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 09 Fault Modeling I Professor Jia Wang ­ Fault Detection in Digital Circuits Spring 2015 1/24 #12;Reading Assignment This lecture: 4.1, 4.2 Next lecture: 4.2 ­ 4.4 ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/24 #12;Homework 2 Due: 5

  8. ECE 586 Fault Detection in Digital Circuits Lecture 11 Fault Modeling III

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 11 Fault Modeling III Professor Jia Wang ­ Fault Detection in Digital Circuits Spring 2015 1/19 #12;Reading Assignment This lecture: 4.5, 4.6 Next lecture: 5.1, 5.2 ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/19 #12;Outline The Single

  9. ECE 586 Fault Detection in Digital Circuits Lecture 12 Fault Simulation I

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 12 Fault Simulation I Professor Jia Wang ­ Fault Detection in Digital Circuits Spring 2015 1/17 #12;Reading Assignment This lecture: 5.1, 5.2 Next lecture: 5.2, 5.3 ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/17 #12;Outline Applications

  10. ECE 586 Fault Detection in Digital Circuits Lecture 10 Fault Modeling II

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 10 Fault Modeling II Professor Jia Wang ­ Fault Detection in Digital Circuits Spring 2015 1/17 #12;Literature Survey Choose one research article for details. ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/17 #12;Literature Survey Choose one

  11. ECE 586 Fault Detection in Digital Circuits Lecture 14 Fault Simulation III

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 14 Fault Simulation III Professor Jia Wang ­ Fault Detection in Digital Circuits Spring 2015 1/27 #12;Reading Assignment This lecture: 5.3 Next lecture: 6.1, 6.2.1 ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/27 #12;Outline Concurrent

  12. Detecting Faults By Use Of Hidden Markov Models

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic J.

    1995-01-01

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

  13. 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.

  14. Cell boundary fault detection system

    DOEpatents

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

    2011-04-19

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

  15. Unbalanced Underground Distribution Systems Fault Detection and Section Estimation

    NASA Astrophysics Data System (ADS)

    de Oliveira, Karen Rezende Caino; Salim, Rodrigo Hartstein; Filomena, André Darós; Resener, Mariana; Bretas, Arturo Suman

    This paper presents a novel fault detection and section estimation method for unbalanced underground distribution systems (UDS). The method proposed is based on artificial neural networks (ANNs) and wavelet transforms (WTs). The majority of UDS are characterized by having several single/double phase laterals and non-symmetrical lines. Also, Digital Fourier Transforms (DFT), used in the majority of traditional protection relays, supplies a low level of robustness to the fault diagnosis process due to its inversely proportional time-frequency characteristic. These characteristics compromise the traditional fault diagnosis methods performance. ANNs are capable of learning and generalizing, whereas WTs are robust tools capable of evaluating a signal's frequency range that can characterize the fault phenomenon. This paper describes the proposed diagnosis method and discusses the results obtained from simulated implementation. The obtained results demonstrate the capability and robustness of the technique indicating its potential for on-line applications.

  16. 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.

  17. Fault detection in programmable logic arrays

    NASA Astrophysics Data System (ADS)

    Somenzi, F.; Gai, S.

    1986-05-01

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

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

    PubMed

    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

  19. 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.

  20. 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.

  1. On Distributed Fault-Tolerant Detection in Wireless Sensor Networks

    E-print Network

    Dong, Ming

    On Distributed Fault-Tolerant Detection in Wireless Sensor Networks Xuanwen Luo, Student Member problems for distributed fault-tolerant detection in wireless sensor networks: 1) how to address both it possible to perform energy- efficient fault-tolerant detection in a wireless sensor network. Index Terms

  2. Symbolic identification for fault detection in aircraft gas turbine engines

    E-print Network

    Ray, Asok

    Symbolic identification for fault detection in aircraft gas turbine engines S Chakraborty, S Sarkar and computationally inexpensive technique of component-level fault detection in aircraft gas-turbine engines the NASA C-MAPSS model of a generic commercial aircraft engine. Keywords: fault detection, model

  3. Fault Detection, Identification and Accommodation for an Electro-hydraulic

    E-print Network

    Yao, Bin

    Fault Detection, Identification and Accommodation for an Electro-hydraulic System: An Adaptive@purdue.edu) Abstract: In the present work, we use an adaptive robust approach for fault detection and accommodation, such a scheme becomes a natural choice for designing robust fault detection algorithms for electro

  4. ECE 586 Fault Detection in Digital Circuits Lecture 28 Diagnosis

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 28 Diagnosis Professor Jia Wang Department of Electrical and Computer Engineering Illinois Institute of Technology April 27, 2015 ECE 586 ­ Fault Detection review ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/25 #12;Final Exam Final exam: Mon. May

  5. A probabilistic method to diagnose faults of air handling units

    NASA Astrophysics Data System (ADS)

    Dey, Debashis

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

  6. 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.

  7. Multi-directional fault detection system

    DOEpatents

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

    2010-11-23

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

  8. Multi-directional fault detection system

    DOEpatents

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

    2009-03-17

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

  9. Statistical Fault Detection & Diagnosis Expert System

    Energy Science and Technology Software Center (ESTSC)

    1996-12-18

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

  10. Fault Tolerant Filtering and Fault Detection for Quantum Systems Driven By Fields in Single Photon States

    E-print Network

    Qing Gao; Daoyi Dong; Ian R. Petersen; Herschel Rabitz

    2015-08-11

    The purpose of this paper is to derive the quantum filtering and fault detection equations for a class of open quantum systems driven by a continuous-mode bosonic input field in single photon states when the systems are subject to stochastic faults. A quantum-classical probability space model is used as a convenient tool to simultaneously estimate the system observables and the fault process, that are characterized by a quantum filter and a fault detection equation, respectively.

  11. Online Fault Detection and Tolerance for Photovoltaic Energy Harvesting Systems

    E-print Network

    Pedram, Massoud

    Online Fault Detection and Tolerance for Photovoltaic Energy Harvesting Systems Xue Lin 1 , Yanzhi (PV systems) are subject to PV cell faults, which decrease the efficiency of PV systems and even shorten the PV system lifespan. Manual PV cell fault detection and elimination are expensive and nearly

  12. Occupancy Based Fault Detection on Building Level - a Feasibility Study 

    E-print Network

    Tuip, B.; Houten, M.; Trcka, M.; Hensen, M.

    2010-01-01

    -line, self learning fault detection tool on building level. Taking passive user behavior into account, the tool aims to distinguish real faults from unexpected user behavior. An artificial neural network model is used to predict building energy consumption...

  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. 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.

  15. 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.

  16. An iterative inversion method for transmission line fault location

    NASA Astrophysics Data System (ADS)

    Wu, Shang Chieh

    2011-12-01

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

  17. Probabilistic model of fault detection in quantum circuits

    E-print Network

    Anindita Banerjee; Anirban Pathak

    2009-05-12

    It is shown that the fault testing for quantum circuits does not follow conventional classical techniques. If probabilistic gate like Hadamard gate is included in a circuit then the classical notion of test vector is shown to fail. We have reported several new and distinguishing features of quantum fault and also presented a general methodology for detection of functional faults in a quantum circuit. The technique can generate test vectors for detection of different kinds of fault. Specific examples are given and time complexity of the proposed quantum fault detection algorithm is reported.

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

    NASA Astrophysics Data System (ADS)

    Wang, Jingang; Xu, Cheng; Sun, Jiaxiang

    2014-06-01

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

  19. Development of New Whole Building Fault Detection and Diagnosis Techniques for Commissioning Persistence 

    E-print Network

    Lin, Guanjing

    2012-12-07

    to detect abnormal whole building energy consumption, and two approaches called Cosine Similarity method and Euclidean Distance Similarity method are developed to isolate the possible fault reasons. The effectiveness of these approaches is demonstrated...

  20. A Probability Extension of PCA to Detect and Diagnose Sensor Faults in Air Handling Units 

    E-print Network

    Li, Z.

    2011-01-01

    Due to sensor faults, it is a challenge to successfully detect and diagnose component faults in HVAC systems. The Principal Component Analysis (PCA) method has become a popular method to tackle this problem in recent years, but PCA is not capable...

  1. VCSEL fault location apparatus and method

    DOEpatents

    Keeler, Gordon A. (Albuquerque, NM); Serkland, Darwin K. (Albuquerque, NM)

    2007-05-15

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

  3. An adaptive envelope spectrum technique for bearing fault detection

    NASA Astrophysics Data System (ADS)

    Sui, Wentao; Osman, Shazali; Wang, Wilson

    2014-09-01

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

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

    PubMed

    Zhong, Guang-Xin; Yang, Guang-Hong

    2015-09-01

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

  5. Toward Reducing Fault Fix Time: Understanding Developer Behavior for the Design of Automated Fault Detection Tools, the Full Report

    E-print Network

    Young, R. Michael

    Toward Reducing Fault Fix Time: Understanding Developer Behavior for the Design of Automated Fault}@csc.ncsu.edu Abstract The longer a fault remains in the code from the time it was injected, the more time it will take to fix the fault. Increasingly, automated fault detection (AFD) tools are providing developers

  6. 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. PMID:21813356

  7. Machine-learning Based Automated Fault Detection in Seismic Chiyuan Zhang and Charlie Frogner (MIT),

    E-print Network

    Poggio, Tomaso

    Machine-learning Based Automated Fault Detection in Seismic Traces Chiyuan Zhang and Charlie no seismic data has been migrated. Our novel method is based on machine learning tech- niques and can a machine learning approach: We generate a set of seismic traces from velocity models containing faults

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

    PubMed

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

    2014-11-01

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

  9. 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. PMID:25290670

  10. Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet

    NASA Astrophysics Data System (ADS)

    Chen, Jinglong; Zi, Yanyang; He, Zhengjia; Yuan, Jing

    2013-07-01

    Due to the character of diversity and complexity, the compound faults detection of rotating machinery under non-stationary operation turns into a challenging task. Multiwavelet with two or more base functions and many excellent properties provides a possibility to detect and extract all the features of compound faults at one time. However, the fixed basis functions independent of the vibration signal may decrease the accuracy of fault detection. Moreover, the decomposition result of discrete multiwavelet transform does not possess time invariance, which is harmful to extract the feature of periodical impulses. To overcome these deficiencies, based on the Hermite splines interpolation, taking the minimum envelope spectrum entropy as the optimization objective, adaptive redundant lifting multiwavelet is developed. Additionally, in order to eliminate error propagation of decomposition results, adaptive redundant lifting multiwavelet is improved by adding the normalization factors. As an effective method, Hilbert transform demodulation analysis is used to extract the fault feature from the high frequency modulation signal. The proposed method incorporating improved adaptive redundant lifting multiwavelet (IARLM) with Hilbert transform demodulation analysis is applied to compound faults detection for the simulation experiment, rolling element bearing test bench and traveling unit of electric locomotive. Compared with some other fault detection methods, the results show the superior effectiveness and reliability on the compound faults detection.

  11. 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.

  12. Methods for quantitatively determining fault slip using fault separation

    NASA Astrophysics Data System (ADS)

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

    2007-10-01

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

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

    NASA Astrophysics Data System (ADS)

    Valavani, L.; Tantouris, N.

    2013-12-01

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

  14. Dynamic faulting on a conjugate fault system detected by near-fault tilt measurements

    NASA Astrophysics Data System (ADS)

    Fukuyama, Eiichi

    2015-12-01

    There have been reports of conjugate faults that have ruptured during earthquakes. However, it is still unclear whether or not these conjugate faults ruptured coseismically during earthquakes. In this paper, we investigated near-fault ground tilt motions observed at the IWTH25 station during the 2008 Iwate-Miyagi Nairiku earthquake ( M w 6.9). Since near-fault tilt motion is very sensitive to the fault geometry on which the slip occurs during an earthquake, these data make it possible to distinguish between the main fault rupture and a rupture on the conjugate fault. We examined several fault models that have already been proposed and confirmed that only the models with a conjugated fault could explain the tilt data observed at IWTH25. The results support the existence of simultaneous conjugate faulting during the main rupture. This will contribute to the understanding of earthquake rupture dynamics because the conjugate rupture releases the same shear strain as that released on the main fault, and thus it has been considered quite difficult for both ruptures to accelerate simultaneously.

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

    PubMed

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

    2015-01-01

    This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB(®) environment. PMID:26180754

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

    NASA Astrophysics Data System (ADS)

    Chen, Jie; Du, Lei

    2015-08-01

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

  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%. PMID:22035775

  18. Low cost fault detection system for railcars and tracks 

    E-print Network

    Vengalathur, Sriram T.

    2004-09-30

    A "low cost fault detection system" that identifies wheel flats and defective tracks is explored here. This is achieved with the conjunction of sensors, microcontrollers and Radio Frequency (RF) transceivers. The objective ...

  19. Non-intrusive fault detection in reciprocating compressors

    E-print Network

    Schantz, Christopher James

    2011-01-01

    This thesis presents a set of techniques for non-intrusive sensing and fault detection in reciprocating compressors driven by induction motors. The procedures developed here are "non-intrusive" because they rely only on ...

  20. Fault detection of multivariable system using its directional properties 

    E-print Network

    Pandey, Amit Nath

    2006-04-12

    -1 FAULT DETECTION OF MULTIVARIABLE SYSTEM USING ITS DIRECTIONAL PROPERTIES A Thesis by AMIT PANDEY Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree... of MASTER OF SCIENCE December 2004 Major Subject: Mechanical Engineering FAULT DETECTION OF MULTIVARIABLE SYSTEM USING ITS DIRECTIONAL PROPERTIES A Thesis by AMIT PANDEY Submitted to Texas A&M University...

  1. 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. PMID:24755865

  2. Detecting Hidden Faults and Other Lineations with UAVSAR

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  3. Defect Detection Capability of Delay Tests for Path Delay Faults

    E-print Network

    Krovi, Venkat

    Defect Detection Capability of Delay Tests for Path Delay Faults Sreejit Chakravarty Dept show that adding at speed tests to test suites detects defective ICs missed by slow speed and I DDQ often fail to detect many defects that causes faulty dynamic faulty behaviour. This implies

  4. 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.

  5. 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.

  6. A Novel Fault Location Method for Radial Distribution Systems

    NASA Astrophysics Data System (ADS)

    Liao, Yuan

    2015-06-01

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

  7. Robust Fault Detection System for Insulin Pump Therapy Using Continuous Glucose Monitoring

    PubMed Central

    Herrero, Pau; Calm, Remei; Vehí, Josep; Armengol, Joaquim; Georgiou, Pantelis; Oliver, Nick; Tomazou, Christofer

    2012-01-01

    Background The popularity of continuous subcutaneous insulin infusion (CSII), or insulin pump therapy, as a way to deliver insulin more physiologically and achieve better glycemic control in diabetes patients has increased. Despite the substantiated therapeutic advantages of using CSII, its use has also been associated with an increased risk of technical malfunctioning of the device, which leads to an increased risk of acute metabolic complications, such as diabetic ketoacidosis. Current insulin pumps already incorporate systems to detect some types of faults, such as obstructions in the infusion set, but are not able to detect other types of fault such as the disconnection or leakage of the infusion set. Methods In this article, we propose utilizing a validated robust model-based fault detection technique, based on interval analysis, for detecting disconnections of the insulin infusion set. For this purpose, a previously validated metabolic model of glucose regulation in type 1 diabetes mellitus (T1DM) and a continuous glucose monitoring device were used. As a first step to assess the performance of the presented fault detection system, a Food and Drug Administration-accepted T1DM simulator was employed. Results Of the 100 in silico tests (10 scenarios on 10 subjects), only two false negatives and one false positive occurred. All faults were detected before plasma glucose concentration reached 300 mg/dl, with a mean plasma glucose detection value of 163 mg/dl and a mean detection time of 200 min. Conclusions Interval model-based fault detection has been proven (in silico) to be an effective tool for detecting disconnection faults in sensor-augmented CSII systems. Proper quantification of the uncertainty associated with the employed model has been observed to be crucial for the good performance of the proposed approach. PMID:23063040

  8. 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.

  9. 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.

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

    NASA Technical Reports Server (NTRS)

    Lala, J. H.

    1985-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Wong, Derek; Poll, Scott; KrishnaKumar, Kalmanje

    2005-01-01

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

  12. Arc Fault Signal Detection -Fourier Transformation vs. Wavelet Decomposition Techniques using Synthesized Data

    E-print Network

    Arc Fault Signal Detection - Fourier Transformation vs. Wavelet Decomposition Techniques using Abstract -- Arc faults are a significant reliability and safety concern for photovoltaic (PV) systems faults in deployed systems are seemingly random and challenging to faithfully create experimentally

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed

    Gao, Wensheng; Bai, Cuifen; Liu, Tong

    2015-01-01

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

  16. 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.

  17. 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.

  18. 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. PMID:24744774

  19. 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

  20. Experimental analysis of change detection algorithms for multitooth machine tool fault detection

    NASA Astrophysics Data System (ADS)

    Reñones, Aníbal; de Miguel, Luis J.; Perán, José R.

    2009-10-01

    This paper describes an industrial application of fault diagnosis method for a multitooth machine tool. Different statistical approaches have been used to detect and diagnose insert breakage in multitooth tools based on the analysis of electrical power consumption of the tool drives. Great effort has been made to obtain a robust method, able to avoid any needed re-calibration process, after, for example, a maintenance operation. From the point of view of maintenance costs, these multitooth tools are the most critical part of the machine tools used for mass production in the car industry. These tools integrate different kinds of machining operations and cutting conditions.

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

    PubMed

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

    2015-07-01

    The fault-tolerant control problem belongs to the domain of complex control systems in which inter-control-disciplinary information and expertise are required. This paper proposes an improved faults detection, reconstruction and fault-tolerant control (FTC) scheme for motor systems (MS) with typical faults. For this purpose, a sliding mode controller (SMC) with an integral sliding surface is adopted. This controller can make the output of system to track the desired position reference signal in finite-time and obtain a better dynamic response and anti-disturbance performance. But this controller cannot deal directly with total system failures. However an appropriate combination of the adopted SMC and sliding mode observer (SMO), later it is designed to on-line detect and reconstruct the faults and also to give a sensorless control strategy which can achieve tolerance to a wide class of total additive failures. The closed-loop stability is proved, using the Lyapunov stability theory. Simulation results in healthy and faulty conditions confirm the reliability of the suggested framework. PMID:25747198

  2. 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.

  3. 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.

  4. Fuzzy model-based observers for fault detection in CSTR.

    PubMed

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. PMID:26521723

  5. 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.

  6. 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.

  7. 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.

  8. Online Monitoring System for Performance Fault Detection

    SciTech Connect

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

    2014-12-31

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

  9. Online Monitoring System for Performance Fault Detection

    SciTech Connect

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

    2014-05-19

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

  10. 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.

  11. Automatic Fault Detection in Cheese using Computer Vision

    E-print Network

    Lunds Universitet

    Automatic Fault Detection in Cheese using Computer Vision David Wrangborg Centre for Mathematical 2007 #12;Abstract In production of cheese with eyes (bubbles of CO2 often referred to as holes) there are occasionally problems with cracks in the cheese. These cracks can pose a problem when cutting up the cheese

  12. Sensor Fault Detection in Power Plants Andrew Kusiak1

    E-print Network

    Kusiak, Andrew

    sensors monitor assembly quality Li and Chen 2006 . In a safety- critical process e.g., a nuclear power and Soroush 2003 . Any false reading could lead to di- sastrous outcomes. In a coal-fired power plant, faultySensor Fault Detection in Power Plants Andrew Kusiak1 and Zhe Song2 Abstract: This paper presents

  13. Fault Detection and Elimination for Galileo-GPS Vertical Guidance

    E-print Network

    Stanford University

    Fault Detection and Elimination for Galileo-GPS Vertical Guidance Alexandru Ene, Juan Blanch, J focus is software simulation in the area of combined GPS/Galileo signals, positioning error threat space, such as Galileo and GPS block III, a series of new developments has taken place in the field of Receiver

  14. ECE 586 Fault Detection in Digital Circuits Spring 2015

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 Instructor: Professor Jia Wang Office/ Office Hrs: TBD Required Textbook: "Digital Systems Testing and Testable Designs" M. Abramovici, M. A. Breuer, A. D. Friedman, IEEE Press, 1990. ISBN: 0-7803-1062-4 (eBook available from http://library

  15. Fault Detection Effectiveness of Spathic Test Data Jane Huffman Hayes

    E-print Network

    Hayes, Jane E.

    Fault Detection Effectiveness of Spathic Test Data Jane Huffman Hayes Computer Science Department This paper presents an approach for generating test data for unit-level, and possibly integration-level, testing based on sampling over intervals of the input probability distribution, i.e., one that has been

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

    NASA Astrophysics Data System (ADS)

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

    2014-01-01

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

  17. Development of an Automated Fault Detection and Diagnosis tool for AHU's 

    E-print Network

    Bruton, K.; Raftery, P.; Aughney, N.; Keane, M.; O'Sullivan, D.

    2012-01-01

    -commissioning HVAC systems to rectify faulty operation with savings of over 20 percent of total energy cost possible by continuously commissioning. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with automating the detection of faults...

  18. Fault detection and exclusion in multisensor navigation systems

    NASA Technical Reports Server (NTRS)

    Bernath, Gregory N.

    1995-01-01

    In order for a multisensor navigation system to meet integrity requirements, there must be a way of detecting erroneous measurements, using only data from those measurements. This can be accomplished using a parity space estimation algorithm. Erroneous measurements must then be removed from the position solution; the entire process is called fault detection and exclusion (FDE). A baseline FDE algorithm has been determined, and is capable of working in real time on present affordable hardware.

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

    PubMed

    Ferdowsi, Hasan; Jagannathan, Sarangapani; Zawodniok, Maciej

    2014-05-01

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

  20. Concurrent Fault Detection in Random Combinational Logic Petros Drineas and Yiorgos Makris

    E-print Network

    Drineas, Petros

    added in parallel to the original circuit, which is assumed to be optimized and may not be modified to duplication, wherein a replica of the circuit acts as a predictor that immediately detects poten- tial faults by comparison to the original circuit. However, instead of duplicating the circuit, the proposed method se

  1. Detection and extraction of fault surfaces in 3D seismic data Israel Cohen1

    E-print Network

    Cohen, Israel

    introduced a multiscale analysis method for the estimation of seismic coherency that is both robust for noiseDetection and extraction of fault surfaces in 3D seismic data Israel Cohen1 , Nicholas Coult2 surfaces in 3D-seismic volumes. The seismic data are transformed into a volume of local

  2. 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.

  3. 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.

  4. ECE 586 Fault Detection in Digital Circuits Lecture 27 Functional Testing

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 27 Functional Testing Professor Jia Wang ­ Fault Detection in Digital Circuits Spring 2015 1/22 #12;Reading Assignment This lecture: 8 Next lecture: 12, 15 ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/22 #12;Outline Functional Testing

  5. ECE 586 Fault Detection in Digital Circuits Lecture 19 Design for Testability I

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 19 Design for Testability I Professor Jia ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 1/20 #12;Reading Assignment This lecture: 9.1, 9.2 Next lecture: 9.3 ­ 9.7 ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/20 #12

  6. ECE 586 Fault Detection in Digital Circuits Lecture 26 Self-Checking Design

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Lecture 26 Self-Checking Design Professor Jia Wang ­ Fault Detection in Digital Circuits Spring 2015 1/22 #12;Reading Assignment This lecture: 13 Next lecture: 8 ECE 586 ­ Fault Detection in Digital Circuits Spring 2015 2/22 #12;Outline Self-Checking Design

  7. 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.

  8. 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.

  9. Fault detection and predictive maintenance program using SEMY Statistical Machine Control (SMC)

    NASA Astrophysics Data System (ADS)

    Ogasawara, Tammie; Izzio, Brian

    2000-08-01

    This presentation describes the introduction of a fault detection and predictive maintenance strategy into the Diffusion area of a semiconductor manufacturing facility. The goal of the fault detection and predictive maintenance strategy is to maximize tool availability for production while minimizing the risk to product. The predictive maintenance methods allow the user to increase the elapsed time between maintenance activities while minimizing the risk of unexpected equipment failure. The predictive maintenance methods are based on the use of a statistically based fault detection system. The selected equipment parameters are monitored throughout the run for drift beyond established threshold limits. Fault detection is reported directly to a maintenance planning and scheduling application which in turn sends a message to the operating personnel. An option is available which will inhibit the use of the tool until the maintenance activity has been completed. A major part of this project was the identification of equipment parameters for monitoring, the statistical methods used in the analysis and the determination of the threshold values at which t originate a maintenance activity. The decision process leading to the definition of these factors is discussed.

  10. Deflated CG Method for Modelling Groundwater Flow Near Faults

    E-print Network

    Vuik, Kees

    Deflated CG Method for Modelling Groundwater Flow Near Faults Interim Report L.A. Ros Delft and market parties. The institute is located in two cities: Delft and Utrecht. Since September 2007 I am Introduction 1 1.1 Subsurface, Groundwater and Faults . . . . . . . . . . . . . . . . . . . . . . 1 1

  11. Fault Detection and Isolation for Hydraulic Control

    NASA Technical Reports Server (NTRS)

    1987-01-01

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

  12. 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

  13. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2006-06-01

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

  15. 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.

  16. 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.

  17. Dynamic Structural Fault Detection and Identification

    NASA Technical Reports Server (NTRS)

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

    2009-01-01

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

  18. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  20. On-Line Fault Detection and Compensation of Hydraulic Driven Machines Using Modelling Techniques

    E-print Network

    Thawonmas, Ruck

    On-Line Fault Detection and Compensation of Hydraulic Driven Machines Using Modelling Techniques C model-based fault detection systems in machinery improves the operational reliability of industrial purpose of hydraulic driven machines as well as for the compensation of incipient faults where applicable

  1. FAULT DETECTION IN HVAC SYSTEMS USING MODEL-BASED FEEDFORWARD CONTROL

    E-print Network

    1 FAULT DETECTION IN HVAC SYSTEMS USING MODEL- BASED FEEDFORWARD CONTROL T. I. Salsbury, Ph.D. R. C and detect faults in the controlled process. The scheme uses static simulation models of the system under control action, the models act as a reference of correct operation. Faults that occur in the system under

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

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  3. 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.

  4. 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.

  5. Exact Computation of Maximally Dominating Faults and Its Application to -Detection Tests

    E-print Network

    Polian, Ilia

    Exact Computation of Maximally Dominating Faults and Its Application to -Detection Tests Ilia, IN 47907, USA Abstract £ -detection test sets for stuck-at faults have been shown to be useful in detecting an important role in controlling the increase in the size of an £ -detection test set as £ is increased

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

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

    PubMed

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

    2013-01-01

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

  8. 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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

  11. 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.

  12. 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.

  13. Hidden Markov models for fault detection in dynamic systems

    NASA Astrophysics Data System (ADS)

    Smyth, Padhraic J.

    1993-04-01

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

  14. Fault Detection in Nonlinear Continuous-Time Systems with Uncertain Parameters

    E-print Network

    Stadtherr, Mark A.

    Fault Detection in Nonlinear Continuous-Time Systems with Uncertain Parameters Youdong Lin and Mark. E-mail: markst@nd.edu #12;Abstract In model-based fault diagnosis for dynamic systems with uncertain parameters, an envelope of all fault-free behaviors can be determined from the model and used as a reference

  15. 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

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

    PubMed

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

    2015-01-01

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

  17. Instantaneous energy density as a feature for gear fault detection

    NASA Astrophysics Data System (ADS)

    Loutridis, S. J.

    2006-07-01

    In this work, energy-based features for gear fault diagnosis and prediction are proposed. The instantaneous energy density is shown to obtain high values when defected teeth are engaged. Three methods are compared in terms of sensitivity, reliability and computation effectiveness. The Wigner-Ville distribution is contrasted to the wavelet transform and the newly proposed empirical mode decomposition scheme. It is shown that all three methods are capable of a reliable prediction. An empirical law, which relates the energy content to the crack magnitude is established.

  18. Fault detection and isolation in aircraft gas turbine engines. Part 1: underlying concept

    E-print Network

    Ray, Asok

    307 Fault detection and isolation in aircraft gas turbine engines. Part 1: underlying concept methodology of degradation monitoring of aircraft gas tur- bine engines with emphasis on detection two-spool turbofan aircraft engine model for detection and isolation of incipient faults. Keywords

  19. 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.

  20. 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.

  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. 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.

  4. Detecting seismogenic stress evolution and constraining fault zone rheology in the San Andreas Fault following

    E-print Network

    Niu, Fenglin

    earthquake- related coseismic damage of rock and subsequent healing nearby fault zones [e.g., Li et al., 1998 Fault following the 2004 Parkfield earthquake Taka'aki Taira,1 Paul G. Silver,1 Fenglin Niu,2 and Robert attributed to the 2004 M 6 Parkfield earthquake, making use of the San Andreas Fault Observatory at Depth

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

    NASA Astrophysics Data System (ADS)

    Mosdorf, M.

    2015-09-01

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

  6. 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

  7. 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,

  8. RICE UNIVERSITY Fault Detection and Fault Tolerance Methods for

    E-print Network

    Cavallaro, Joseph R.

    Engineering Dr. Ian D. Walker, Co-Chairman Assistant Professor Electrical and Computer Engineering Dr. John B allows robots to e ectively cope with inter- nal failures and continue performing designated tasks appreciation to Dr. Joseph Cavallaro and Dr. Ian Walker for their support and encouragement throughout

  9. 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

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

    PubMed

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1997-01-01

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

  12. 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.

  13. 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. PMID:25289644

  14. 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.

  15. 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.

  16. Fault Detection and Isolation Filters for Three-Phase AC-DC Power Electronics Systems

    E-print Network

    Dominguez-Garcia, Alejandro

    1 Fault Detection and Isolation Filters for Three-Phase AC-DC Power Electronics Systems Xiangyu a new class of high-fidelity model-based fault detection and isolation filters for three-phase AC-DC used power electronics systems implementing three-phase AC-DC converters that are used in, e.g., motor

  17. Neural network-based detection of mechanical, sensor and biological faults in deep-trough

    E-print Network

    Selman, Bart

    -trough hydroponics K.P. Ferentinos a, *, L.D. Albright a , B. Selman b a Department of Biological and Environmental for the detection of faulty operation of a deep-trough hydroponic system which is caused by mechanical, actuator reserved. Keywords: Fault detection; Hydroponics; Feedforward neural networks; Biological faults

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

    PubMed

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

    2014-01-01

    Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio. PMID:24776937

  19. An Uncertainty-Based Distributed Fault Detection Mechanism for Wireless Sensor Networks

    PubMed Central

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

    2014-01-01

    Exchanging too many messages for fault detection will cause not only a degradation of the network quality of service, but also represents a huge burden on the limited energy of sensors. Therefore, we propose an uncertainty-based distributed fault detection through aided judgment of neighbors for wireless sensor networks. The algorithm considers the serious influence of sensing measurement loss and therefore uses Markov decision processes for filling in missing data. Most important of all, fault misjudgments caused by uncertainty conditions are the main drawbacks of traditional distributed fault detection mechanisms. We draw on the experience of evidence fusion rules based on information entropy theory and the degree of disagreement function to increase the accuracy of fault detection. Simulation results demonstrate our algorithm can effectively reduce communication energy overhead due to message exchanges and provide a higher detection accuracy ratio. PMID:24776937

  20. 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.

  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. Methods of Melanoma Detection.

    PubMed

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

    2016-01-01

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

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

    PubMed

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

    2014-01-01

    To prevent irreversible demagnetization of a permanent magnet (PM) for interior permanent magnet synchronous motors (IPMSMs) by flux-weakening control, a robust PM flux-linkage nonsingular fast terminal-sliding-mode observer (NFTSMO) is proposed to detect demagnetization faults. First, the IPMSM mathematical model of demagnetization is presented. Second, the construction of the NFTSMO to estimate PM demagnetization faults in IPMSM is described, and a proof of observer stability is given. The fault decision criteria and fault-processing method are also presented. Finally, the proposed scheme was simulated using MATLAB/Simulink and implemented on the RT-LAB platform. A number of robustness tests have been carried out. The scheme shows good performance in spite of speed fluctuations, torque ripples and the uncertainties of stator resistance. PMID:25490582

  4. 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

  5. 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.

  6. Adaptive approximation for multiple sensor fault detection and isolation of nonlinear uncertain systems.

    PubMed

    Reppa, Vasso; Polycarpou, Marios M; Panayiotou, Christos G

    2014-01-01

    This paper presents an adaptive approximation-based design methodology and analytical results for distributed detection and isolation of multiple sensor faults in a class of nonlinear uncertain systems. During the initial stage of the nonlinear system operation, adaptive approximation is used for online learning of the modeling uncertainty. Then, local sensor fault detection and isolation (SFDI) modules are designed using a dedicated nonlinear observer scheme. The multiple sensor fault isolation process is enhanced by deriving a combinatorial decision logic that integrates information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of conditions for ensuring fault detectability and isolability. A simulation example of a single-link robotic arm is used to illustrate the application of the adaptive approximation-based SFDI methodology and its effectiveness in detecting and isolating multiple sensor faults. PMID:24806650

  7. 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.

  8. 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.

  9. 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.

  10. 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. PMID:24439843

  11. 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.

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

    SciTech Connect

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

    1995-07-01

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

  13. A Nitsche-extended finite element method for earthquake rupture on complex fault systems

    E-print Network

    Shaw, Bruce E.

    A Nitsche-extended finite element method for earthquake rupture on complex fault systems E.T. Coon Keywords: XFEM Nitsche's method Earthquakes Fault systems a b s t r a c t The extended finite element complex fault system geometry. Here we focus on Finite Element Methods (FEM), which have the ability

  14. 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.).

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

    NASA Astrophysics Data System (ADS)

    Kopka, Ryszard

    2014-12-01

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

  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. PMID:19084227

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

    NASA Astrophysics Data System (ADS)

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

    2004-05-01

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

  18. 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.

  19. Method for detecting biomolecules

    DOEpatents

    Huo, Qisheng (Albuquerque, NM); Liu, Jun (Albuquerque, NM)

    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

  20. Electromagnetic detection of plate hydration due to bending faults at the Middle America Trench

    E-print Network

    Constable, Steve

    Electromagnetic detection of plate hydration due to bending faults at the Middle America Trench bending faults on the incoming oceanic plate of the Middle America Trench offshore Nicaragua have been crossing the trench offshore Nicaragua. Along the incoming plate our data reveal that crustal resistivity

  1. Electromagnetic detection of plate hydration due to bending faults at the Middle America Trench

    E-print Network

    Key, Kerry

    Electromagnetic detection of plate hydration due to bending faults at the Middle America Trench poorly constrained. Extensional bending faults on the incoming oceanic plate of the Middle America Trench and uppermost mantle along a 220 km profile crossing the trench offshore Nicaragua. Along the incoming plate our

  2. Fault Detection and Identification in a Mobile Robot Using Multiple Model Estimation and Neural Network

    E-print Network

    Roumeliotis, Stergios I.

    Department of Computer Science University of Southern California Los Angeles, CA 90089­0781 Abstract We implemented on a physical robot and results from ex­ periments are discussed. 1 Introduction Fault detection Problem Definition and Algorithm 2.1 Task Definition Fault tolerant behavior refers to automatic detec

  3. Limitations for detecting small-scale faults using the coherency analysis of seismic data 

    E-print Network

    Barnett, David Benjamin

    2006-08-16

    be distinguished from noise. Results from application of the coherency analysis were applied to the characterization of a very deep fault and fracture system imaged by a field seismic data set. A series of reverse and strike-slip faults were detected and mapped...

  4. Neural net application to transmission line fault detection and classification 

    E-print Network

    Rikalo, Igor

    1994-01-01

    Today, in electric power systems, a large amount of data is made readily available at the occurrence of a fault due to the use of advanced communication systems, digital relays and fault recorders. Such systems are intended to obtain data from...

  5. Low-cost motor drive embedded fault diagnosis systems 

    E-print Network

    Akin, Bilal

    2009-05-15

    cost incipient fault detection of inverter-fed driven motors. Basically, low order inverter harmonics contributions to fault diagnosis, a motor drive embedded condition monitoring method, analysis of motor fault signatures in noisy line current, and a...

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

    SciTech Connect

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

    2011-06-01

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

  7. Hypothesis Testing and Decision Theoretic Approach for Fault Detection in Wireless Sensor Networks

    E-print Network

    Nandi, Mrinal; Roy, Bimal; Sarkar, Santanu

    2012-01-01

    Sensor networks aim at monitoring their surroundings for event detection and object tracking. But due to failure or death of sensors, false signal can be transmitted. In this paper, we consider the problem of fault detection in wireless sensor network (WSN), in particular, addressing both the noise-related measurement error and sensor fault simultaneously in fault detection. We assume that the sensors are placed at the center of a square (or hexagonal) cell in region of interest (ROI) and, if the event occurs, it occurs at a particular cell of the ROI. We propose fault detection schemes that take into account error probabilities into the optimal event detection process. We develop the schemes under the consideration of Neyman-Pearson test and Bayes test.

  8. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

  10. 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.

  11. Nucleic Acid Detection Methods

    DOEpatents

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

    1998-05-19

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

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

    SciTech Connect

    Wei Qiao

    2012-05-29

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

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

    DOEpatents

    Gaubatz, Donald C. (Cupertino, CA)

    1990-01-01

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

  14. 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.

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

    NASA Astrophysics Data System (ADS)

    Czajkowski, Andrzej

    2015-11-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

  17. Comparison of Chiller Models for Use in Model-Based Fault Detection 

    E-print Network

    Sreedhara, P.; Haves, P.

    2001-01-01

    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...

  18. A Qualitive Modeling Approach for Fault Detection and Diagnosis on HVAC Systems 

    E-print Network

    Muller, T.; Rehault, N.; Rist, T.

    2013-01-01

    This paper describes the basics and first test results of a model based approach using qualitative modeling to perform Fault Detection and Diagnostics (FDD) on HVAC and R systems. A quantized system describing the qualitative behavior of a...

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

    PubMed

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

    2014-07-01

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

  20. Development of an Automated Fault Detection System Tool for Unitary Air Conditioners at Undustrial Energy Audits 

    E-print Network

    Parikh, P.; Pasmussen, B. P.

    2015-01-01

    . Bryan Rasmussen, Texas A&M University Development of an Automated Fault Detection Device for Unitary HVAC Systems at Industrial Energy Audits ESL-IE-15-06-38a Proceedings of the Thrity-Seventh Industrial Energy Technology Conference New Orleans, LA... Technology Conference New Orleans, LA. June 2-4, 2015 Adjusting setpoint Altering the control algorithm Replacing the actuator Coordinating operations of multiple systems Detecting and eliminating faults Improving the operation of equipment involves...

  1. Detecting and tolerating Byzantine faults in database systems

    E-print Network

    Vandiver, Benjamin Mead, 1978-

    2008-01-01

    This thesis describes the design, implementation, and evaluation of a replication scheme to handle Byzantine faults in transaction processing database systems. The scheme compares answers from queries and updates on multiple ...

  2. Detecting and Tolerating Byzantine Faults in Database Systems

    E-print Network

    Vandiver, Benjamin Mead

    2008-06-30

    This thesis describes the design, implementation, and evaluation of a replication scheme to handle Byzantine faults in transaction processing database systems. The scheme compares answers from queries and updates on multiple ...

  3. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-01-01

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

  5. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-04-01

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

  9. Hideen Markov Models and Neural Networks for Fault Detection in Dynamic Systems

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic

    1994-01-01

    None given. (From conclusion): Neural networks plus Hidden Markov Models(HMM)can provide excellene detection and false alarm rate performance in fault detection applications. Modified models allow for novelty detection. Also covers some key contributions of neural network model, and application status.

  10. Robust fault detection for switched positive linear systems with time-varying delays.

    PubMed

    Xiang, Mei; Xiang, Zhengrong

    2014-01-01

    This paper investigates the problem of robust fault detection for a class of switched positive linear systems with time-varying delays. The fault detection filter is used as the residual generator, in which the filter parameters are dependent on the system mode. Attention is focused on designing the positive filter such that, for model uncertainties, unknown inputs and the control inputs, the error between the residual and fault is minimized. The problem of robust fault detection is converted into a positive L1 filtering problem. Subsequently, by constructing an appropriate multiple co-positive type Lyapunov-Krasovskii functional, as well as using the average dwell time approach, sufficient conditions for the solvability of this problem are established in terms of linear matrix inequalities (LMIs). Two illustrative examples are provided to show the effectiveness and applicability of the proposed results. PMID:24041401

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

    PubMed

    Hao, Li-Ying; Yang, Guang-Hong

    2013-09-01

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

  12. Arc-Fault Detector Algorithm Evaluation Method Utilizing Prerecorded Arcing Signatures

    E-print Network

    Arc-Fault Detector Algorithm Evaluation Method Utilizing Prerecorded Arcing Signatures Jay Johnson1 systems on or penetrating a building to include a DC arc-fault protection device. In order to satisfy this requirement, new Arc-Fault Detectors (AFDs) are being developed by multiple manufacturers including Sensata

  13. 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 0.35?m to 2.5?m with 1nm spectral sampling, of the sampled fault rocks. Subsequently, two different Spectral Angle Mapper (SAM) classifications were applied to ASTER images: the first one based on fault rock spectral signatures resampled at the ASTER sensor resolution; the second one based on spectral signatures retrieved from specific Region of Interest (ROI), which were directly derived from the ASTER image on the analyzed fault zones. The SAM classification based on the spectral signatures of fault rocks gave outstanding results since it was able to classify the analyzed fault zone, both in terms of length and width. Moreover, in some specific cases, this SAM classification identified not only the sampled fault zone, but also other prominent neighboring faults cutting the same host rock. These results define the SAM supervised classification on ASTER images as a tool to identify prominent fault zones directly on the base of fault-rocks spectra.

  14. Waveguide disturbance detection method

    DOEpatents

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

    2000-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2002-12-01

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

  16. 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

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

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

    NASA Astrophysics Data System (ADS)

    Prévost, Jean H.; Sukumar, N.

    2016-01-01

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

  19. Potential of Electrical Resistivity Tomography to Detect Fault Zones in Limestone and Argillaceous Formations in the Experimental Platform of Tournemire, France

    NASA Astrophysics Data System (ADS)

    Gélis, C.; Revil, A.; Cushing, M. E.; Jougnot, D.; Lemeille, F.; Cabrera, J.; de Hoyos, A.; Rocher, M.

    2010-11-01

    The Experimental platform of Tournemire (Aveyron, France) developed by IRSN (French Institute for Radiological Protection and Nuclear Safety) is located in a tunnel excavated in a clay-rock formation interbedded between two limestone formations. A well-identified regional fault crosscuts this subhorizontal sedimentary succession, and a subvertical secondary fault zone is intercepted in the clay-rock by drifts and boreholes in the tunnel at a depth of about 250 m. A 2D electrical resistivity survey was carried out along a 2.5 km baseline, and a takeout of 40 m was used to assess the potential of this method to detect faults from the ground surface. In the 300 m-thick zone investigated by the survey, electrical resistivity images reveal several subvertical low-resistivity discontinuities. One of these discontinuities corresponds to the position of the Cernon fault, a major regional fault. One of the subvertical conductive discontinuities crossing the upper limestone formation is consistent with the prolongation towards the ground surface of the secondary fault zone identified in the clay-rock formation from the tunnel. Moreover, this secondary fault zone corresponds to the upward prolongation of a subvertical fault identified in the lower limestone using a 3D high-resolution seismic reflection survey. This type of large-scale electrical resistivity survey is therefore a useful tool for identifying faults in superficial layers from the ground surface and is complementary to 3D seismic reflection surveys.

  20. 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.

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

    PubMed

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

    2009-01-01

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

  2. 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

  3. 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.

  4. Automatic and Scalable Fault Detection for Mobile Applications

    E-print Network

    @csail.mit.edu M.I.T. & Microsoft Research Cambridge, MA, USA Suman Nath sumann@microsoft.com Microsoft Research Redmond, WA, USA Jitendra Padhye padhye@microsoft.com Microsoft Research Redmond, WA, USA Hari, implementation, and evaluation of VanarSena, an automated fault finder for mobile applications ("apps

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

    SciTech Connect

    Li, J.; Kohler, J.L.

    1999-10-01

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

  6. 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

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

  9. SMART GRID MONITORING FOR INTRUSION AND FAULT DETECTION WITH NEW LOCALLY OPTIMUM TESTING PROCEDURES

    E-print Network

    Blum, Rick

    behavior of the sys- tem. Since small changes are often difficult to detect, we de- velop new locally locally optimum (LO) tests which tend to outperform the GLR test for cases with small changes. PreviousSMART GRID MONITORING FOR INTRUSION AND FAULT DETECTION WITH NEW LOCALLY OPTIMUM TESTING PROCEDURES

  10. 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.

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

    PubMed Central

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

    2014-01-01

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

  12. Test generation algorithm for fault detection of analog circuits based on extreme learning machine.

    PubMed

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

    2014-01-01

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

  13. 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

  14. 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.

  15. 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.

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

    PubMed

    Jiang, Yulian; Liu, Jianchang; Wang, Shenquan

    2014-09-01

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

  17. 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. PMID:24807956

  18. WETLAND DETECTION METHODS INVESTIGATION

    EPA Science Inventory

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

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

    NASA Astrophysics Data System (ADS)

    Qu, Dongfang; Røe, Per; Tveranger, Jan

    2015-08-01

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

  20. 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

  1. 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.

  2. 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. PMID:26233063

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

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

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

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

    NASA Astrophysics Data System (ADS)

    Schlechtingen, Meik; Ferreira Santos, Ilmar

    2011-07-01

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

  5. Abstract-A fault detection and reconfiguration technique for a cascaded H-bridge 11-level inverter drives during faulty condition

    E-print Network

    Tolbert, Leon M.

    Abstract-A fault detection and reconfiguration technique for a cascaded H-bridge 11-level inverter can be used as a diagnostic signal to detect faults and their locations. AI-based techniques are used to perform the fault classification. A neural network (NN) classification is applied to the fault diagnosis

  6. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-08-01

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

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

    PubMed

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

    2015-01-01

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

  9. Efficient Methods for Exploiting Faults Induced at AES Middle Rounds

    E-print Network

    International Association for Cryptologic Research (IACR)

    Faults occurred during the operations in a hardware device cause many problems such as performance deteri hardware devices. Especially faults occurred during the operations cause many problems such as performance daily lives from banking cards to SIM cards for GSM. C. H. Kim is with Information Security Group

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

    DOEpatents

    Reifman, Jaques (Westchester, IL); Wei, Thomas Y. C. (Downers Grove, IL)

    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.

  12. 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.

  13. 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.

  14. Virtual Sensor Based Fault Detection and Classification on a Plasma Etch Reactor

    E-print Network

    Sofge, D A

    2007-01-01

    The SEMATECH sponsored J-88-E project teaming Texas Instruments with NeuroDyne (et al.) focused on Fault Detection and Classification (FDC) on a Lam 9600 aluminum plasma etch reactor, used in the process of semiconductor fabrication. Fault classification was accomplished by implementing a series of virtual sensor models which used data from real sensors (Lam Station sensors, Optical Emission Spectroscopy, and RF Monitoring) to predict recipe setpoints and wafer state characteristics. Fault detection and classification were performed by comparing predicted recipe and wafer state values with expected values. Models utilized include linear PLS, Polynomial PLS, and Neural Network PLS. Prediction of recipe setpoints based upon sensor data provides a capability for cross-checking that the machine is maintaining the desired setpoints. Wafer state characteristics such as Line Width Reduction and Remaining Oxide were estimated on-line using these same process sensors (Lam, OES, RFM). Wafer-to-wafer measurement of thes...

  15. 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.

  16. 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

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

    PubMed

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

    2014-01-01

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

  18. Fault detection and isolation in aircraft gas turbine engines. Part 2: validation on a simulation test bed

    E-print Network

    Ray, Asok

    319 Fault detection and isolation in aircraft gas turbine engines. Part 2: validation of fault detection and isolation (FDI) in aircraft gas turbine engines. The FDI algorithms are built upon,onasimulationtestbed.Thetestbedisbuiltuponanintegratedmodelofageneric two-spool turbofan aircraft gas turbine engine including the engine control system. Keywords: aircraft

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

    NASA Astrophysics Data System (ADS)

    Li, Min; Yang, Jianhong; Wang, Xiaojing

    2015-07-01

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

  20. 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).

  1. Analysis of microseismic activity detected by the WIZARD array, Alpine Fault, New Zealand

    NASA Astrophysics Data System (ADS)

    Feenstra, J. P.; Roecker, S. W.; Thurber, C. H.; Lord, N.; O'Brien, G.; Pesicek, J. D.; Townend, J.; Bannister, S. C.

    2012-12-01

    A primary goal for the UW-Madison-RPI WIZARD array is the characterization of background seismicity around the Deep Fault Drilling Project (DFDP) site on the Alpine Fault, South Island, New Zealand. The WIZARD array consists of 20 stations, half broadband, deployed for a planned 2-year period around the Whataroa Valley DFDP-2 drill site. Two neighboring arrays, SAMBA (Victoria University of Wellington) to the southwest and ALFA'12 (GNS Science) to the northeast, along with several GeoNet permanent stations, provide broad coverage of the region. The earthquakes that are detected will (1) help to define the geometry of the Alpine Fault and other active faults at depth, (2) provide data for seismic imaging, focal mechanisms, and shear-wave splitting analysis, and (3) enable the assessment of possible changes in seismic activity induced by future fault zone drilling. We are currently analyzing data from the first 2 months of the deployment. Dozens of nearby earthquakes (S-P time of up to a few seconds) have been detected, far more than are in the New Zealand GeoNET catalog. This is expected because the magnitude completion level of the GeoNet seismometer network is around 2.5 in the Whataroa region, due to a relatively sparse station coverage. In this presentation, we report on earthquake location results for 8 months of WIZARD data, along with focal mechanisms for selected larger events.

  2. ECE 586 Fault Detection in Digital Circuits Instructor: Professor Jia Wang

    E-print Network

    Wang, Jia

    ECE 586 ­ Fault Detection in Digital Circuits Fall 2013 Instructor: Professor Jia Wang Office: 317 Hrs: TBD Required Textbook: "Digital Systems Testing and Testable Designs" M. Abramovici, M. A. Breuer, A. D. Friedman, IEEE Press, 1990. ISBN: 0-7803-1062-4 (eBook available from http://library

  3. State Aware Test Case Regeneration for Improving Web Application Test Suite Coverage and Fault Detection

    E-print Network

    Harman, Mark

    .74% and fault detection by 9.19%. Categories and Subject Descriptors D.2.5 [Software Engineering]: Testing functions such as Back and Refresh. This can lead to unexpected execution paths that may cause unanticipated state to guide the choice of request orderings that increase effectiveness. Of course, the Hypertext

  4. A CONTROLLER FOR HVAC SYSTEMS WITH FAULT DETECTION CAPABILITIES BASED ON SIMULATION MODELS

    E-print Network

    1 A CONTROLLER FOR HVAC SYSTEMS WITH FAULT DETECTION CAPABILITIES BASED ON SIMULATION MODELS T. I. The scheme uses static simulation models of the system under control to generate feed- forward control action operating system to supplement a conventional PI(D) feedback loop. The model is used as part

  5. Efficient Test Compaction for Combinational Circuits Based on Fault Detection Count-Directed Clustering

    E-print Network

    El-Maleh, Aiman H.

    of Petroleum & Minerals P.O. Box 1063, Dhahran 31261 Saudi Arabia aimane@ccse.kfupm.edu.sa, saqib modification, and (3) Test vector addition and removal. In the first category, compaction is performed by #12 to modify partially specified test vectors to detect additional faults often fail [14]. In addition, dynamic

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

    NASA Technical Reports Server (NTRS)

    Kennedy, J. J.

    1970-01-01

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

  7. Distributed Sensor Analysis for Fault Detection in Tightly-Coupled Multi-Robot Team Tasks

    E-print Network

    Parker, Lynne E.

    Distributed Sensor Analysis for Fault Detection in Tightly-Coupled Multi-Robot Team Tasks Xingyan Li and Lynne E. Parker Proc. of IEEE International Conference on Robotics and Automation, Kobe, Japan multi-robot team tasks. While the centralized version of SAFDetection was shown to be successful

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  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 zones or branched faults. Studying the interplay of stress conditions and angle dependence of neighbouring branches including inelastic material behaviour and its effects on rupture jumps and seismic activation helps to advance our understanding of earthquake source processes. An application is the simulation of a real large-scale subduction zone scenario including plasticity to validate the coupling of our dynamic rupture calculations to a tsunami model in the framework of the ASCETE project (http://www.ascete.de/). Andrews, D. J. (2005): Rupture dynamics with energy loss outside the slip zone, J. Geophys. Res., 110, B01307. Heinecke, A. (2014), A. Breuer, S. Rettenberger, M. Bader, A.-A. Gabriel, C. Pelties, A. Bode, W. Barth, K. Vaidyanathan, M. Smelyanskiy and P. Dubey: Petascale High Order Dynamic Rupture Earthquake Simulations on Heterogeneous Supercomputers. In Supercomputing 2014, The International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, New Orleans, LA, USA, November 2014. Roten, D. (2014), K. B. Olsen, S.M. Day, Y. Cui, and D. Fäh: Expected seismic shaking in Los Angeles reduced by San Andreas fault zone plasticity, Geophys. Res. Lett., 41, 2769-2777.

  10. Test of two methods for faulting on finite-difference calculations

    USGS Publications Warehouse

    Andrews, D.J.

    1999-01-01

    Tests of two fault boundary conditions show that each converges with second order accuracy as the finite-difference grid is refined. The first method uses split nodes so that there are disjoint grids that interact via surface traction. The 3D version described here is a generalization of a method I have used extensively in 2D; it is as accurate as the 2D version. The second method represents fault slip as inelastic strain in a fault zone. Offset of stress from its elastic value is seismic moment density. Implementation of this method is quite simple in a finite-difference scheme using velocity and stress as dependent variables.

  11. Distributed bearing fault diagnosis based on vibration analysis

    NASA Astrophysics Data System (ADS)

    Dolenc, Boštjan; Boškoski, Pavle; Juri?i?, ?ani

    2016-01-01

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

  12. 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.

  13. 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.

  14. 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.

  15. Fault detection for mobile robots using redundant positioning systems

    E-print Network

    Jensfelt, Patric

    ), there is a caster wheel. As the robot drives forward against the table, the weight transfers to the caster wheel not lose track of its position, even if unexpected events like wheel slip and collisions occur of position information. It is shown that the system is able to detect wheel slip in real-time. I

  16. Fault-Tolerant Concept Detection in Information Networks

    E-print Network

    and their properties, how can we automatically discover that Aspirin has blood-thinning properties, and thus prevents, and the other describing their chemical behavior, how can we discover whether, for ex- ample, Aspirin has blood uses of Aspirin (Acetylsalicylic acid) that have been detected by our proposed algorithm. In a nutshell

  17. 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.

  18. New close-region detection method using region growing

    NASA Astrophysics Data System (ADS)

    Tu, Dan; Yan, Hong; Shen, Zhenkang

    1997-10-01

    A new detecting algorithm for close region, which has rectangle or parallelogram shape (e.g. airfield track), is developed in this paper. The traditional detecting algorithms for this kind of close region have some faults: long operating time and high demand for image quality. Mainly based on gray level shifting characteristic and size characteristic of close region, this proposed detecting algorithm for close region is not needed to extract line and search parallel lines. In this algorithm, a new terminology-edge dot couple, which is used as parameter of close region detection, is expressed and the method to extract it is described. A new technology-region growing, which use edge dot couple as growing base, is developed to search out close region. The author applies this algorithm to detect airfield track. The experiment result shows that this method is good for the close region detection.

  19. 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.

  20. Spin-system dynamics and fault detection in threshold networks

    SciTech Connect

    Kirkland, Steve; Severini, Simone

    2011-01-15

    We consider an agent on a fixed but arbitrary node of a known threshold network, with the task of detecting an unknown missing link. We obtain analytic formulas for the probability of success when the agent's tool is the free evolution of a single excitation on an XX spin system paired with the network. We completely characterize the parameters, which allows us to obtain an advantageous solution. From the results emerges an optimal (deterministic) algorithm for quantum search, from which a quadratic speedup with respect to the optimal classical analog and in line with well-known results in quantum computation is gained. When attempting to detect a faulty node, the chosen setting appears to be very fragile and the probability of success too small to be of any direct use.

  1. Fault Detection in Dynamic Systems Using the Largest Lyapunov Exponent 

    E-print Network

    Sun, Yifu

    2012-10-19

    . The Lyapunov exponents can be estimated using the Jacobian matrix and the QR decomposition. The possible applications of this method are then explored for various chaotic systems. Finally, the method is applied to some real world data to demonstrate the general...

  2. 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.

  3. Research of high-resolution vibration signal detection technique and application to mechanical fault diagnosis

    NASA Astrophysics Data System (ADS)

    Fan, Y. S.; Zheng, G. T.

    2007-02-01

    Bilinear time-frequency transformation can possess a simultaneous high resolution both in the time domain and the frequency domain. It can be utilised to detect weak transient vibration signals generated by early mechanical faults in complex background and thus is of great importance to early mechanical fault diagnoses. It has been found that the spectrogram has low resolution, and there is strong cross-terms in Wigner-Ville distribution and frequency aliasing and information loss in Choi-Williams distribution (CWD). Hence, they cannot achieve satisfied transient signal detection results. To enhance the capability of detecting transient vibration signals, based on the analysis of exponent distribution, this paper presents some novel alias-free time-frequency distributions. These distributions can avoid the information loss in CWD while suppressing the cross-terms. Moreover, they have high simultaneous resolutions in both the time and frequency domain. Digital simulation and gearbox fault diagnosis experiments prove that these new distributions can effectively detect transient components from complicated mechanical vibration signals.

  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. The micromechanics of fault gouge and dynamic earthquake triggering: investigation by Discrete Element Method numerical simulations

    E-print Network

    Daub,Eric G.

    The micromechanics of fault gouge and dynamic earthquake triggering: investigation by Discrete Element Method numerical simulations 4. Department of Physics, University of Nevada, Reno DYNAMIC EARTHQUAKE TRIGGERING ARE TRIGGERED EVENTS DIFFERENT? DISCRETE ELEMENT MODEL MODEL DYNAMICS VIBRATION AND NON

  6. Fault detection, isolation, and recovery for autonomous parafoils

    E-print Network

    Stoeckle, Matthew Robert

    2014-01-01

    Autonomous precision airdrop systems are widely used to deliver supplies to remote locations. This aerial delivery method provides a safety and logistical advantage over traditional ground- or helicopter-based payload ...

  7. 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.

  8. Prediction and Fault Detection of Environmental Signals with Uncharacterised Michael A. Osborne

    E-print Network

    Freitas, Nando de

    significant issues that humanity faces. The quality and availability of water directly affects the health. Our method is deployed in the domain of water monitoring and management, where it is able to solve, nonlinearity and many unanticipated types of fault. Introduction Water sustainability is one of the most

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

    PubMed

    Lee, Sanghyuk; Park, Wookje; Jung, Sikhang

    2014-01-01

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

  10. 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

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

    PubMed

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

    2014-01-01

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

  12. 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

  13. A hybrid system for fault detection and sensor fusion based on fuzzy clustering and artificial immune systems 

    E-print Network

    Jaradat, Mohammad Abdel Kareem Rasheed

    2007-04-25

    the sensor signals is generated by the fusion engine. The information provided from the previous two phases is used for fault detection in the third phase based on the Artificial Immune System (AIS) negative selection mechanism. The simulations...

  14. Efficient Detection on Stochastic Faults in PLC Based Automated Assembly Systems With Novel Sensor Deployment and Diagnoser Design 

    E-print Network

    Wu, Zhenhua

    2012-07-16

    In this dissertation, we proposed solutions on novel sensor deployment and diagnoser design to efficiently detect stochastic faults in PLC based automated systems First, a fuzzy quantitative graph based sensor deployment was called upon to model...

  15. Method for detecting biological toxins

    SciTech Connect

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

    1992-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2007-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Goto, Hayato; Uchikawa, Hironori

    2013-06-01

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

  18. 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.

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

    E-print Network

    Jaeyoon Cho

    2007-10-07

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

  20. Fault Diagnosis of Continuous Systems Using Discrete-Event Methods Matthew Daigle, Xenofon Koutsoukos, and Gautam Biswas

    E-print Network

    Daigle, Matthew

    Fault Diagnosis of Continuous Systems Using Discrete-Event Methods Matthew Daigle, Xenofon.j.daigle,xenofon.koutsoukos,gautam.biswas@vanderbilt.edu Abstract-- Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems fault isolation in systems with complex continuous dynamics. This paper presents a novel discrete- event

  1. Fault Current Calculation by The Least Squares Method Natthaphob Nimpitiwan, Student Member, IEEE, and Gerald T. Heydt, Fellow, IEEE

    E-print Network

    1 Fault Current Calculation by The Least Squares Method Natthaphob Nimpitiwan, Student Member, IEEE, and Gerald T. Heydt, Fellow, IEEE Abstract-- This paper contains the analysis of the increase of fault current due to the installation of DGs or merchant plants. An index called the Average Change of Fault

  2. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

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

  4. 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.

  5. 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.

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

    SciTech Connect

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

    2000-05-01

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

  7. Fault finder

    DOEpatents

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

    1986-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Quinn, Todd M.; Walters, Jerry L.

    1991-01-01

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

  9. Method for detecting toxic gases

    DOEpatents

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

    1991-10-08

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

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

    SciTech Connect

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

    2014-10-28

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

  11. 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.

  12. Estimating the detectability of faults in 3D-seismic data - A valuable input to Induced Seismic Hazard Assessment (ISHA)

    NASA Astrophysics Data System (ADS)

    Goertz, A.; Kraft, T.; Wiemer, S.; Spada, M.

    2012-12-01

    In the past several years, some geotechnical operations that inject fluid into the deep subsurface, such as oil and gas development, waste disposal, and geothermal energy development, have been found or suspected to cause small to moderate sized earthquakes. In several cases the largest events occurred on previously unmapped faults, within or in close vicinity to the operated reservoirs. The obvious conclusion drawn from this finding, also expressed in most recently published best practice guidelines and recommendations, is to avoid injecting into faults. Yet, how certain can we be that all faults relevant to induced seismic hazard have been identified, even around well studied sites? Here we present a probabilistic approach to assess the capability of detecting faults by means of 3D seismic imaging. First, we populate a model reservoir with seed faults of random orientation and slip direction. Drawing random samples from a Gutenberg-Richter distribution, each seed fault is assigned a magnitude and corresponding size using standard scaling relations based on a circular rupture model. We then compute the minimum resolution of a 3D seismic survey for given acquisition parameters and frequency bandwidth. Assuming a random distribution of medium properties and distribution of image frequencies, we obtain a probability that a fault of a given size is detected, or respectively overlooked, by the 3D seismic. Weighting the initial Gutenberg-Richter fault size distribution with the probability of imaging a fault, we obtain a modified fault size distribution in the imaged volume from which we can constrain the maximum magnitude to be considered in the seismic hazard assessment of the operation. We can further quantify the value of information associated with the seismic image by comparing the expected insured value loss between the image-weighted and the unweighted hazard estimates.

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

    NASA Astrophysics Data System (ADS)

    Zou, Hui; Huang, Fuzhen

    2015-11-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Smyth, Padhraic

    1994-01-01

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

  19. Method for detecting toxic gases

    DOEpatents

    Stetter, Joseph R. (Naperville, IL); Zaromb, Solomon (Hinsdale, IL); Findlay, Jr., Melvin W. (Bolingbrook, IL)

    1991-01-01

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

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

    DOEpatents

    Andrews, L.B.

    1998-08-18

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

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

    DOEpatents

    Andrews, Lowell B. (2181-13th Ave. SW., Largo, FL 34640)

    1998-01-01

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

  2. Real-Time Building Energy Modeling, fault Detection and Diagnostic for a DoD Building 

    E-print Network

    Dong, B.

    2013-01-01

    -Time Building Energy Modeling and Fault Detection and Diagnostics for a DoD Building Bing Dong1, Zheng O’Neill2 1 University of Texas, San Antonio, TX, USA 2 University of Alabama, AL, USA The work was done at the United Technologies Research Center ESL-KT-13... an accurate baseline model. ESL-KT-13-12-18 CATEE 2013: Clean Air Through Energy Efficiency Conference, San Antonio, Texas Dec. 16-18 • Acknowledgements:– DoD ESTCP program manager: Dr. Jim Galvin– UTRC: Dong Luo, Madhusudana, Shashanka ,Sunil Ahuja, Trevor...

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

    NASA Technical Reports Server (NTRS)

    Feather, Martin S.; Markosian, Lawrence Z.

    2008-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

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

    USGS Publications Warehouse

    Barall, Michael; Harris, Ruth A.

    2012-01-01

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

  6. Bacteria detection instrument and method

    NASA Technical Reports Server (NTRS)

    Renner, W.; Fealey, R. D. (inventors)

    1972-01-01

    A method and apparatus for screening a sample fluid for bacterial presence are disclosed wherein the fluid sample is mixed with culture media of sufficient quantity to permit bacterial growth in order to obtain a test solution. The concentration of oxygen dissolved in the test solution is then monitored using the potential difference between a reference electrode and a noble metal electrode which are in contact with the test solution. The change in oxygen concentration which occurs during a period of time as indicated by the electrode potential difference is compared with a detection criterion which exceeds the change which would occur absent bacteria.

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

    NASA Technical Reports Server (NTRS)

    Malekpour, Mahyar R. (Inventor)

    2012-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

  9. Implementation and Testing of a Fault Detection Software Tool for Improving Control System Performance in a Large Commercial Building

    E-print Network

    that can detect faults in the controlled process and improve control performance over traditional PID control. The tool uses static simulation models of the system under control to generate feed discrepancies between the feedforward models and the controlled process. The scheme facilitates detection

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

    NASA Astrophysics Data System (ADS)

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

    2006-12-01

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

  11. Explosives detection system and method

    DOEpatents

    Reber, Edward L. (Idaho Falls, ID); Jewell, James K. (Idaho Falls, ID); Rohde, Kenneth W. (Idaho Falls, ID); Seabury, Edward H. (Idaho Falls, ID); Blackwood, Larry G. (Idaho Falls, ID); Edwards, Andrew J. (Idaho Falls, ID); Derr, Kurt W. (Idaho Falls, ID)

    2007-12-11

    A method of detecting explosives in a vehicle includes providing a first rack on one side of the vehicle, the rack including a neutron generator and a plurality of gamma ray detectors; providing a second rack on another side of the vehicle, the second rack including a neutron generator and a plurality of gamma ray detectors; providing a control system, remote from the first and second racks, coupled to the neutron generators and gamma ray detectors; using the control system, causing the neutron generators to generate neutrons; and performing gamma ray spectroscopy on spectra read by the gamma ray detectors to look for a signature indicative of presence of an explosive. Various apparatus and other methods are also provided.

  12. Fault Models for Quantum Mechanical Switching Networks

    E-print Network

    Jacob Biamonte; Jeff S. Allen; Marek A. Perkowski

    2010-01-19

    The difference between faults and errors is that, unlike faults, errors can be corrected using control codes. In classical test and verification one develops a test set separating a correct circuit from a circuit containing any considered fault. Classical faults are modelled at the logical level by fault models that act on classical states. The stuck fault model, thought of as a lead connected to a power rail or to a ground, is most typically considered. A classical test set complete for the stuck fault model propagates both binary basis states, 0 and 1, through all nodes in a network and is known to detect many physical faults. A classical test set complete for the stuck fault model allows all circuit nodes to be completely tested and verifies the function of many gates. It is natural to ask if one may adapt any of the known classical methods to test quantum circuits. Of course, classical fault models do not capture all the logical failures found in quantum circuits. The first obstacle faced when using methods from classical test is developing a set of realistic quantum-logical fault models. Developing fault models to abstract the test problem away from the device level motivated our study. Several results are established. First, we describe typical modes of failure present in the physical design of quantum circuits. From this we develop fault models for quantum binary circuits that enable testing at the logical level. The application of these fault models is shown by adapting the classical test set generation technique known as constructing a fault table to generate quantum test sets. A test set developed using this method is shown to detect each of the considered faults.

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

    PubMed Central

    Bustillo, Andres; Correa, Maritza; Reñones, Anibal

    2011-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Oostdyk, Rebecca L.; Perotti, Jose M.

    2011-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1982-11-01

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

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

    PubMed

    Bustillo, Andres; Correa, Maritza; Reñones, Anibal

    2011-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-10-01

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

  18. Electrical Motor Current Signal Analysis using a Dynamic Time Warping Method for Fault Diagnosis

    NASA Astrophysics Data System (ADS)

    Zhen, D.; Alibarbar, A.; Zhou, X.; Gu, F.; Ball, A. D.

    2011-07-01

    This paper presents the analysis of phase current signals to identify and quantify common faults from an electrical motor based on dynamic time warping (DTW) algorithm. In condition monitoring, measurements are often taken when the motor undertakes varying loads and speeds. The signals acquired in these conditions show similar profiles but have phase shifts, which do not line up in the time-axis for adequate comparison to discriminate the small changes in machine health conditions. In this study, DTW algorithms are exploited to align the signals to an ideal current signal constructed based on average operating conditions. In this way, comparisons between the signals can be made directly in the time domain to obtain residual signals. These residual signals are then based on to extract features for detecting and diagnosing the faults of the motor and components operating under different loads and speeds. This study provides a novel approach to the analysis of electrical current signal for diagnosis of motor faults. Experimental data sets of electrical motor current signals have been studied using DTW algorithms. Results show that DTW based residual signals highlights more the modulations due to the compressor process. And hence can obtain better fault detection and diagnosis results.

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Simon, Dan; Simon, Donald L.

    2009-01-01

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

  1. Mine geophysics methods in studying the coal bearing rock mass condition in low magnitude tectonic fault zones

    NASA Astrophysics Data System (ADS)

    Alexeev, A. D.; Zhitlyonok, D. M.; Pitalenko, E. I.

    2003-04-01

    Disjunctive type tectonic faults are quite serious problem at underground coal winning. In the fault adjacent areas both coal seam and coal bearing rocks are usually essentially fractured that makes them less stable in coalfaces at underground mining. Some researchers have pointed out to enhanced stress state in these areas as well provided that loosening zones are absent. Coal seams are mostly inclined to disjunctive faults in Central region of Donets Coal Basin where tectonic processes were very intense. There are a lot of small faults with magnitudes close to seam thickness about 2 m in this region along with large thrust or fault disjunctives with stratigraphic magnitudes over 10 m (Dyleyev, Northern, Brunvald, Bulavin faults and others). Highest disjunctive dislocation is typical for coalfields near mines "Toretskaya" and "Novodzerzhinskaya", Coal Production Co. "Dzerzhinskugol", where dislocation density reaches about 8.5 faults per 1 km across the field. Small disjunctive faults often coincide with sites of sudden coal and gas outbursts, longwall inrushes, and poor support condition in development workings. It is known that affected zones on either side accommodate each disjunctive fault, these zones being distinctive for increased fissuring, higher stresses, coal and rocks differing strength. Affected zone width dependence on the fault parameters was determined using geological approach. Mine electrical survey and acoustical probing methods were used to study rock mass faulted condition in the vicinity of development workings and stopes intercepting low magnitude (below 5 m) disjunctive faults in coal field of mine "Toretskaya". These findings have allowed to establish a new fault magnitude dependence of rupture tectonic dislocation's affected zone width in the form of B = 3.2 H, where B is dislocation's affected zone width (m); H is the dislocation's stratigraphic magnitude (m). It was established as well that stress level in rock mass near disjunctive fault is 2 to 2.5 times higher than in undisturbed rock mass.

  2. Fault detection of a roller-bearing system through the EMD of a wavelet denoised signal.

    PubMed

    Ahn, Jong-Hyo; Kwak, Dae-Ho; Koh, Bong-Hwan

    2014-01-01

    This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matrices from healthy and damaged bearings exhibit different POV profiles, which can be a damage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility of wavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault. PMID:25196008

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

    NASA Technical Reports Server (NTRS)

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

    2003-01-01

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

  4. In-process fault detection for textile fabric production: onloom imaging

    NASA Astrophysics Data System (ADS)

    Neumann, Florian; Holtermann, Timm; Schneider, Dorian; Kulczycki, Ashley; Gries, Thomas; Aach, Til

    2011-05-01

    Constant and traceable high fabric quality is of high importance both for technical and for high-quality conventional fabrics. Usually, quality inspection is carried out by trained personal, whose detection rate and maximum period of concentration are limited. Low resolution automated fabric inspection machines using texture analysis were developed. Since 2003, systems for the in-process inspection on weaving machines ("onloom") are commercially available. With these defects can be detected, but not measured quantitative precisely. Most systems are also prone to inevitable machine vibrations. Feedback loops for fault prevention are not established. Technology has evolved since 2003: Camera and computer prices dropped, resolutions were enhanced, recording speeds increased. These are the preconditions for real-time processing of high-resolution images. So far, these new technological achievements are not used in textile fabric production. For efficient use, a measurement system must be integrated into the weaving process; new algorithms for defect detection and measurement must be developed. The goal of the joint project is the development of a modern machine vision system for nondestructive onloom fabric inspection. The system consists of a vibration-resistant machine integration, a high-resolution machine vision system, and new, reliable, and robust algorithms with quality database for defect documentation. The system is meant to detect, measure, and classify at least 80 % of economically relevant defects. Concepts for feedback loops into the weaving process will be pointed out.

  5. Applied change of mean detection techniques for HVAC fault detection and diagnosis and power monitoring

    E-print Network

    Hill, Roger Owen

    1995-01-01

    A signal processing technique, the detection of abrupt changes in a time-series signal, is implemented with two different applications related to energy use in buildings. The first application is a signal pre-processor for ...

  6. ECE 586 Fault Detection in Digital Circuits Lecture 25 Built-In Self-Test II

    E-print Network

    Wang, Jia

    /PO: PRPG and MISR. State bits: SRSG and SISA. Testing is slow because of scan operations. ECE 586 ­ Fault/PO: PRPG and MISR. State bits: SRSG and SISA. Testing is slow because of scan operations. ECE 586 ­ Fault

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

    PubMed

    Goto, Hayato; Uchikawa, Hironori

    2013-01-01

    Fault-tolerant quantum computation with quantum error-correcting codes has been considerably developed over the past decade. However, there are still difficult issues, particularly on the resource requirement. For further improvement of fault-tolerant quantum computation, here we propose a soft-decision decoder for quantum error correction and detection by teleportation. This decoder can achieve almost optimal performance for the depolarizing channel. Applying this decoder to Knill's C4/C6 scheme for fault-tolerant quantum computation, which is one of the best schemes so far and relies heavily on error correction and detection by teleportation, we dramatically improve its performance. This leads to substantial reduction of resources. PMID:23784512

  8. Real-time material quality prediction, fault detection, and contamination control in AlGaN/GaN high electron mobility transistor metalorganic

    E-print Network

    Rubloff, Gary W.

    Real-time material quality prediction, fault detection, and contamination control in AlGaN/GaN high sensing provides a control on contaminants to assure high material quality and a fault detection for such high performance products is challenged by reproducibility and material quality constraints

  9. Fault Detection and Safety in Closed-Loop Artificial Pancreas Systems

    PubMed Central

    2014-01-01

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

  10. Fault detection in heavy duty wheels by advanced vibration processing techniques and lumped parameter modeling

    NASA Astrophysics Data System (ADS)

    Malago`, M.; Mucchi, E.; Dalpiaz, G.

    2016-03-01

    Heavy duty wheels are used in applications such as automatic vehicles and are mainly composed of a polyurethane tread glued to a cast iron hub. In the manufacturing process, the adhesive application between tread and hub is a critical assembly phase, since it is completely made by an operator and a contamination of the bond area may happen. Furthermore, the presence of rust on the hub surface can contribute to worsen the adherence interface, reducing the operating life. In this scenario, a quality control procedure for fault detection to be used at the end of the manufacturing process has been developed. This procedure is based on vibration processing techniques and takes advantages of the results of a lumped parameter model. Indicators based on cyclostationarity can be considered as key parameters to be adopted in a monitoring test station at the end of the production line due to their not deterministic characteristics.

  11. Evaluation of an expert system for fault detection, isolation, and recovery in the manned maneuvering unit

    NASA Technical Reports Server (NTRS)

    Rushby, John; Crow, Judith

    1990-01-01

    The authors explore issues in the specification, verification, and validation of artificial intelligence (AI) based software, using a prototype fault detection, isolation and recovery (FDIR) system for the Manned Maneuvering Unit (MMU). They use this system as a vehicle for exploring issues in the semantics of C-Language Integrated Production System (CLIPS)-style rule-based languages, the verification of properties relating to safety and reliability, and the static and dynamic analysis of knowledge based systems. This analysis reveals errors and shortcomings in the MMU FDIR system and raises a number of issues concerning software engineering in CLIPs. The authors came to realize that the MMU FDIR system does not conform to conventional definitions of AI software, despite the fact that it was intended and indeed presented as an AI system. The authors discuss this apparent disparity and related questions such as the role of AI techniques in space and aircraft operations and the suitability of CLIPS for critical applications.

  12. Particle Filters for Real-Time Fault Detection in Planetary Rovers

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  13. Fault detection and safety in closed-loop artificial pancreas systems.

    PubMed

    Bequette, B Wayne

    2014-11-01

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

  14. Treanmission Line Fault Location using Interoperability and Integration of Data and Model 

    E-print Network

    Dutta, Papiya

    2014-01-10

    This dissertation proposes selective scheme of transmission line fault location by choosing between two different types of fault location algorithms depending on the availability of measurements. The first type is an accurate method to detect...

  15. Analysis of electrical signatures in synchronous generators characterized by bearing faults 

    E-print Network

    Choi, Jae-Won

    2009-05-15

    Synchronous generators play a vital role in power systems. One of the major mechanical faults in synchronous generators is related to bearings. The popular vibration analysis method has been utilized to detect bearing faults for years. However...

  16. Feature Extraction for Data-Driven Fault Detection in Nuclear Power Plants Xin Jin, Robert M. Edwards and Asok Ray

    E-print Network

    Ray, Asok

    is performed during each nuclear power plant refueling outage, which may not be cost effective [1Feature Extraction for Data-Driven Fault Detection in Nuclear Power Plants Xin Jin, Robert M monitoring of nuclear power plants (NPP) is one of the key issues addressed in nuclear energy safety research

  17. Real-time Fault Detection and Situational Awareness for Rovers: Report on the Mars Technology Program Task

    E-print Network

    Thrun, Sebastian

    successfully on diagnosis problems including the K-9 rover at NASA Ames Research Center and the Hyperion roverReal-time Fault Detection and Situational Awareness for Rovers: Report on the Mars Technology Program Task Richard Dearden, Thomas Willeke Frank Hutter {RIACS, QSS Group Inc.} / NASA Ames Research

  18. Methods for locating ground faults and insulation degradation condition in energy conversion systems

    DOEpatents

    Agamy, Mohamed; Elasser, Ahmed; Galbraith, Anthony William; Harfman Todorovic, Maja

    2015-08-11

    Methods for determining a ground fault or insulation degradation condition within energy conversion systems are described. A method for determining a ground fault within an energy conversion system may include, in part, a comparison of baseline waveform of differential current to a waveform of differential current during operation for a plurality of DC current carrying conductors in an energy conversion system. A method for determining insulation degradation within an energy conversion system may include, in part, a comparison of baseline frequency spectra of differential current to a frequency spectra of differential current transient at start-up for a plurality of DC current carrying conductors in an energy conversion system. In one embodiment, the energy conversion system may be a photovoltaic system.

  19. Active methods of early forest fire detection

    NASA Astrophysics Data System (ADS)

    Utkin, Andrei B.; Lavrov, Alexander; Vilar, Rui

    2011-02-01

    A method of automated early fire detection based on the light detection and ranging (lidar) technology is presented. Specific lidar configurations and their application to forest and industrial-environment fire surveillance are discussed.

  20. Particle detection systems and methods

    DOEpatents

    Morris, Christopher L.; Makela, Mark F.

    2010-05-11

    Techniques, apparatus and systems for detecting particles such as muons and neutrons. In one implementation, a particle detection system employs a plurality of drift cells, which can be for example sealed gas-filled drift tubes, arranged on sides of a volume to be scanned to track incoming and outgoing charged particles, such as cosmic ray-produced muons. The drift cells can include a neutron sensitive medium to enable concurrent counting of neutrons. The system can selectively detect devices or materials, such as iron, lead, gold, uranium, plutonium, and/or tungsten, occupying the volume from multiple scattering of the charged particles passing through the volume and can concurrently detect any unshielded neutron sources occupying the volume from neutrons emitted therefrom. If necessary, the drift cells can be used to also detect gamma rays. The system can be employed to inspect occupied vehicles at border crossings for nuclear threat objects.

  1. Leak detection method and device

    SciTech Connect

    Castor, T.P.

    1989-03-21

    An apparatus is described for detecting and measuring leaks in underground storage tanks comprising: a rigid support means capable of being maintained in a fixed position, a substantially rigid mechanical force transducer means having two ends, one of the ends rigidly connected to the support means and the other of the ends adapted to contact a fluid and act as a fluid force sensor, strain detection and measurement means capable of detecting and measuring the micro strain elongation and contraction in the force transducer when a change in force resulting from a fluid level change is applied to the free end.

  2. Method For Detecting Biological Agents

    DOEpatents

    Chen, Liaohai (Los Alamos, NM); McBranch, Duncan W. (Santa Fe, NM); Wang, Hsing-Lin (Los Alamos, NM); Whitten, David G. (Santa Fe, NM)

    2005-12-27

    A sensor is provided including a polymer capable of having an alterable measurable property from the group of luminescence and electrical conductivity, the polymer having an intermediate combination of a recognition element, a tethering element and a property-altering element bound thereto and capable of altering the measurable property, the intermediate combination adapted for subsequent separation from the polymer upon exposure to an agent having an affinity for binding to the recognition element whereupon the separation of the intermediate combination from the polymer results in a detectable change in the alterable measurable property, and, detecting said detectable change in the alterable measurable property.

  3. Modeling of a latent fault detector in a digital system

    NASA Technical Reports Server (NTRS)

    Nagel, P. M.

    1978-01-01

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

  4. A decentralized fault detection and isolation scheme for spacecraft: bridging the gap between model-based fault detection and isolation research and practice

    NASA Astrophysics Data System (ADS)

    Indra, S.; Travé-Massuyès, L.; Chanthery, E.

    2013-12-01

    This paper introduces a decentralized fault diagnosis and isolation (FDI) architecture for spacecraft and applies it to the attitude determination and control system (ADCS) of a satellite. A system is decomposed into functional subsystems. The architecture is composed of local diagnosers for subsystems which work with local models. Fault ambiguities due to interactions between subsystems are resolved at a higher level by a supervisor, which combines the partial view of the local diagnosers and performs isolation on request. The architecture is hierarchically scalable. The structure of the ADCS is modeled as constraints and variables and used to demonstrate the decentralized architecture.

  5. Sensor Fault Diagnosis Using Principal Component Analysis 

    E-print Network

    Sharifi, Mahmoudreza

    2010-07-14

    The purpose of this research is to address the problem of fault diagnosis of sensors which measure a set of direct redundant variables. This study proposes: 1. A method for linear senor fault diagnosis 2. An analysis of isolability and detectability...

  6. An approach to model-based fault detection in industrial measurement systems with application to engine test benches

    NASA Astrophysics Data System (ADS)

    Angelov, P.; Giglio, V.; Guardiola, C.; Lughofer, E.; Luján, J. M.

    2006-07-01

    An approach to fault detection (FD) in industrial measurement systems is proposed in this paper which includes an identification strategy for early detection of the appearance of a fault. This approach is model based, i.e. nominal models are used which represent the fault-free state of the on-line measured process. This approach is also suitable for off-line FD. The framework that combines FD with isolation and correction (FDIC) is outlined in this paper. The proposed approach is characterized by automatic threshold determination, ability to analyse local properties of the models, and aggregation of different fault detection statements. The nominal models are built using data-driven and hybrid approaches, combining first principle models with on-line data-driven techniques. At the same time the models are transparent and interpretable. This novel approach is then verified on a number of real and simulated data sets of car engine test benches (both gasoline—Alfa Romeo JTS, and diesel—Caterpillar). It is demonstrated that the approach can work effectively in real industrial measurement systems with data of large dimensions in both on-line and off-line modes.

  7. A Normal-faulting Paleostress in the Vicinity of Up-dip Limit of Seismogenic Zone Detected by Meso-scale Fault Analysis in a Tectonic Mélange

    NASA Astrophysics Data System (ADS)

    Sato, K.; Ikesawa, E.; Kimura, G.

    2003-12-01

    The Mugi mélange in the Shimanto Belt, SW Japan, is a mixture of terrigenous and oceanic materials of late Cretaceous to Paleocene. Intermittent bedding planes trend ENE-WSW to E-W (subparallel to the Nankai trough axis) and dip steeply northward. The Mugi mélange consists of several duplex units accompanied by shear zones of basalt layers at their boundaries. Systematic shear fabrics and P-T conditions estimated from analyses of vitrinite reflectance and fluid inclusions indicate that the Mugi mélange had once been subducted to a significant depth (6-7 km below sea floor, which appears to coincide with the up-dip limit of the seismogenic zone), then underplated to the Shimanto accretionary prism, and is now exhumed on ground surface. In this study, for the purpose of determining paleostress fields related to the processes in which subducted materials were deformed, underplated and uplifted to surface, orientations of meso-scale faults and striations were analyzed. Stress inversion techniques including Angelier's Inversion, Multiple Inversion and Ginkgo Method were applied to fault-slip data obtained in each duplex unit of the Mugi mélange, and the results were almost consistent with each other. Most of the resultant ? 1 axes trend N-S horizontally, and are parallel to poles of shale cleavages, which are roughly parallel to bedding planes. Although the cleavages slightly vary their orientations according to later rotation, ? 1 axis changes together with them. This cleavage-controlled paleostress has a low Bishop's stress ratio (i.e. low magnitude of ? 2), therefore is an axial compressional stress normal to cleavages. The restored paleostress was probably exerted just before or at the same time of the formation of duplex structure and the rotation of bedding planes. The meso-scale faults appear to have been formed as normal ones due to overburden. P-T conditions estimated by analysis of fluid inclusions, which occur in the mineral veins sealing measured faults, and cross-cutting relationships between the faults and unit boundary shear zones indicate the simultaneity of these faulting and duplexing. The duplex structure is thought to be formed at the moment of underplating and be caused by stepdown of the décollement. A great variety of drastic changes in properties of material and circumstance such as stress field may occur at the very point of the stepdown, underplating of subducted material, and the up-dip limit of the seismogenic zone.

  8. [The application of atomic absorption spectrometry in automatic transmission fault detection].

    PubMed

    Chen, Li-dan; Chen, Kai-kao

    2012-01-01

    The authors studied the innovative applications of atomic absorption spectrometry in the automatic transmission fault detection. After the authors have determined Fe, Cu and Cr contents in the five groups of Audi A6 main metal in automatic transmission fluid whose travel course is respectively 10-15 thousand kilometers, 20-26 thousand kilometers, 32-38 thousand kilometers, 43-49 thousand kilometers, and 52-58 thousand kilometers by atomic absorption spectrometry, the authors founded the database of primary metal content in the Audi A6 different mileage automatic transmission fluid (ATF). The research discovered that the main metal content in the automatic transmission fluid increased with the vehicles mileage and its normal metal content level in the automatic transmission fluid is between the two trend lines. The authors determined the main metal content of automatic transmission fluid which had faulty symptoms and compared it with its database value. Those can not only judge the wear condition of the automatic transmission which had faulty symptoms but also help the automobile detection and maintenance personnel to diagnose automatic transmission failure reasons without disintegration. This reduced automobile maintenance costs, and improved the quality of automobile maintenance. PMID:22497168

  9. Fault detection of roller-bearings using signal processing and optimization algorithms.

    PubMed

    Kwak, Dae-Ho; Lee, Dong-Han; Ahn, Jong-Hyo; Koh, Bong-Hwan

    2013-01-01

    This study presents a fault detection of roller bearings through signal processing and optimization techniques. After the occurrence of scratch-type defects on the inner race of bearings, variations of kurtosis values are investigated in terms of two different data processing techniques: minimum entropy deconvolution (MED), and the Teager-Kaiser Energy Operator (TKEO). MED and the TKEO are employed to qualitatively enhance the discrimination of defect-induced repeating peaks on bearing vibration data with measurement noise. Given the perspective of the execution sequence of MED and the TKEO, the study found that the kurtosis sensitivity towards a defect on bearings could be highly improved. Also, the vibration signal from both healthy and damaged bearings is decomposed into multiple intrinsic mode functions (IMFs), through empirical mode decomposition (EMD). The weight vectors of IMFs become design variables for a genetic algorithm (GA). The weights of each IMF can be optimized through the genetic algorithm, to enhance the sensitivity of kurtosis on damaged bearing signals. Experimental results show that the EMD-GA approach successfully improved the resolution of detectability between a roller bearing with defect, and an intact system. PMID:24368701

  10. The correlation of 2D-resistivity and magnetic methods in fault verification at northern Sumatra, Indonesia

    NASA Astrophysics Data System (ADS)

    Kamaruddin, Nur Aminuda; Saad, Rosli; Nordiana, M. M.; Azwin, I. N.

    2015-04-01

    The Great Sumatra Fault system was split into two sub-parallel lines or segments at the Northern Sumatra. This event is one of the impacts of powerful earthquakes that hit Sumatra Island especially one that occurred in 2004. These two sub-parallel segments known as Aceh and Seulimeum fault. The study is focused on the Seulimeum fault and two geophysical methods chosen aimed to compare and verified the result obtained respectively. 2-D resistivity method is a common geophysical method used in determination of near surface structures such as faults, cavities, voids and sinkholes. Meanwhile, the magnetic method often chosen to delineate subsurface structures, determine depth of magnetic source bodies and possibly sediment thickness. Three survey lines of resistivity method and randomly magnetic stations were carried out covering Krueng district. The resistivity data processed using Res2Dinv and result presented using Surfer software. The fault identified by the contrast of low and high resistivity value. Meanwhile, the magnetic data were presented in magnetic residual contour map and the extended fault system is suspected represent by the contrast value of the magnetic anomalies. Within suspected fault zone, the results of resistivity are tally with magnetic result.

  11. Application of an inverse method for calculating three-dimensional fault geometries and clip vectors, Nun River Field, Nigeria

    SciTech Connect

    Kerr, H.G.; White, N.

    1996-03-01

    A general, automatic method for determining the three-dimensional geometry of a normal fault of any shape and size is applied to a three-dimensional seismic reflection data set from the Nun River field, Nigeria. In addition to calculating fault geometry, the method also automatically retrieves the extension direction without requiring any previous information about either the fault shape or the extension direction. Solutions are found by minimizing the misfit between sets of faults that are calculated from the observed geometries of two or more hanging-wall beds. In the example discussed here, the predicted fault surface is in excellent agreement with the shape of the seismically imaged fault. Although the calculated extension direction is oblique to the average strike of the fault, the value of this parameter is not well resolved. Our approach differs markedly from standard section-balancing models in two important ways. First, we do not assume that the extension direction is known, and second, the use of inverse theory ensures that formal confidence bounds can be determined for calculated fault geometries. This ability has important implications for a range of geological problems encountered at both exploration and production scales. In particular, once the three-dimensional displacement field has been constrained, the difficult but important problem of three-dimensional palinspastic restoration of hanging-wall structures becomes tractable.

  12. A model-based solution for fault diagnosis of thruster faults: application to the rendezvous phase of the mars sample return mission

    NASA Astrophysics Data System (ADS)

    Henry, D.; Bornschlegl, E.; Olive, X.; Charbonnel, C.

    2013-12-01

    This paper addresses the design of model-based fault diagnosis schemes to detect and isolate faults occurring in the orbiter thrusters of the Mars Sample Return (MSR) mission. The proposed fault diagnosis method is based on a H(0) filter with robust poles assignment to detect quickly any kind of thruster faults and a cross-correlation test to isolate them. Simulation results from the MSR "high-fidelity" nonlinear simulator provided by Thales Alenia Space demonstrate that the proposed method is able to diagnose thruster faults with a detection and isolation delay less than 1.1 s.

  13. Evaluation of various boar taint detection methods.

    PubMed

    Aluwé, M; Tuyttens, F A M; Bekaert, K M; De Smet, S; De Brabander, D L; Millet, S

    2012-11-01

    The aim of this study was to evaluate the performance of various boar taint detection methods, measure the relationship between them and identify possible points of improvement for boar taint detection. The methods used to evaluate boar taint in the carcasses of 448 entire male pigs and 17 barrows were the hot iron method (n = 442), a standardised (n = 323) and home (n = 58) consumer meat-evaluation panel, an expert panel assessment of meat and fat (n = 464) and laboratory analysis of skatole, androstenone and indole in fat (n = 464). The axillary odour of a number of slaughtered entire male pigs was also investigated (n = 231). As correlation coefficients were generally weak, a positive result for one of these detection methods did not per se result in a positive result for all other methods. Results of one detection method could not be generalised. The choice to use one or more detection methods deserves consideration depending on the aim of the study. In this paper, we suggest some possible improvements for evaluating boar taint with a consumer panel based on our results and experience. The home consumer evaluation was correlated with the concentration of indole (r = 0.27) but not with skatole or androstenone. We therefore recommend that lab analyses include indole testing. The hot iron method seems to be an easy and fast detection method, which yields comparable or better correlation coefficients with the other detection methods than an expert panel evaluating fat samples. However, the reliability of the hot iron method depends on the training and reliability of one or two assessors. Efforts should be made to further optimise this method by evaluating the effect of testing conditions. The axillary odour score was moderately correlated with the other detection methods (up to 0.32). More research is needed to evaluate the possibilities of axillary odour as a boar taint detection method. PMID:22717070

  14. Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System.

    PubMed

    Ouyang, Kesai; Lu, Siliang; Zhang, Shangbin; Zhang, Haibin; He, Qingbo; Kong, Fanrang

    2015-01-01

    The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy. PMID:26343657

  15. Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System

    PubMed Central

    Ouyang, Kesai; Lu, Siliang; Zhang, Shangbin; Zhang, Haibin; He, Qingbo; Kong, Fanrang

    2015-01-01

    The railway occupies a fairly important position in transportation due to its high speed and strong transportation capability. As a consequence, it is a key issue to guarantee continuous running and transportation safety of trains. Meanwhile, time consumption of the diagnosis procedure is of extreme importance for the detecting system. However, most of the current adopted techniques in the wayside acoustic defective bearing detector system (ADBD) are offline strategies, which means that the signal is analyzed after the sampling process. This would result in unavoidable time latency. Besides, the acquired acoustic signal would be corrupted by the Doppler effect because of high relative speed between the train and the data acquisition system (DAS). Thus, it is difficult to effectively diagnose the bearing defects immediately. In this paper, a new strategy called online Doppler effect elimination (ODEE) is proposed to remove the Doppler distortion online by the introduced unequal interval sampling scheme. The steps of proposed strategy are as follows: The essential parameters are acquired in advance. Then, the introduced unequal time interval sampling strategy is used to restore the Doppler distortion signal, and the amplitude of the signal is demodulated as well. Thus, the restored Doppler-free signal is obtained online. The proposed ODEE method has been employed in simulation analysis. Ultimately, the ODEE method is implemented in the embedded system for fault diagnosis of the train bearing. The results are in good accordance with the bearing defects, which verifies the good performance of the proposed strategy. PMID:26343657

  16. Robust statistical methods for automated outlier detection

    NASA Technical Reports Server (NTRS)

    Jee, J. R.

    1987-01-01

    The computational challenge of automating outlier, or blunder point, detection in radio metric data requires the use of nonstandard statistical methods because the outliers have a deleterious effect on standard least squares methods. The particular nonstandard methods most applicable to the task are the robust statistical techniques that have undergone intense development since the 1960s. These new methods are by design more resistant to the effects of outliers than standard methods. Because the topic may be unfamiliar, a brief introduction to the philosophy and methods of robust statistics is presented. Then the application of these methods to the automated outlier detection problem is detailed for some specific examples encountered in practice.

  17. Revised and Improved Fault Maps of Washoe County, Nevada using Light Detecting and Ranging (LiDAR) Imagery

    NASA Astrophysics Data System (ADS)

    Brailo, C.; Kent, G.; Wesnousky, S. G.; Kell, A. M.; Pierce, I.; Ruhl, C. J.; Smith, K. D.

    2014-12-01

    A new Light Detection and Ranging (LiDAR) survey images the fault network of Truckee Meadows region of western Nevada, including the Reno/Sparks metropolitan area in Washoe County. The airborne LiDAR imagery (1485 sq. km) is being used to create high quality bare-earth digital elevation models that were previously unattainable in vegetated, populated or alpine terrain. LiDAR gives us an opportunity to improve fault maps that may be outdated or incomplete in the area. Here we show LiDAR imagery of a large section of Washoe County and highlight areas where this imagery may be useful in revising current fault maps. Conflicting stress regimes, with strike-slip regions overlapping extensional domains in the Walker Lane Deformation Belt, complicate regional tectonics of Washoe County. In this region east of the Sierra Nevada batholith, approximately 20-25% of Pacific-North American plate motion (mostly right-lateral shear) is accommodated along the Walker Lane. There is ample evidence of Magnitude 6-7 earthquakes in or surrounding the Truckee Meadows region as recently as the late 1800s and it is possible that earthquakes of this size may occur here in the near future. Accurate mapping of faults and associated earthquake hazards in populated areas is critically important for earthquake mitigation and preparedness, and furthers our understanding of regional tectonics. The new LiDAR data confirms the presence of many previously mapped faults, simplifies areas that may be presently over-complicated by current maps, and identifies faults that were previously unmapped. Current and future research will also focus on dating of glacial outwash terraces and alluvial fans, particularly in the Mogul area and Mt. Rose pediment. Coupled with comprehensive fault maps and displacement measurements improved by this new LiDAR dataset, these data may allow researchers to get more accurate slip rate estimates on faults in this region, and may support the hypothesis that some faults in the Washoe County region are more active than previously reported.

  18. Detection and Classification of Transformer Winding Mechanical Faults Using UWB Sensors and Bayesian Classifier

    NASA Astrophysics Data System (ADS)

    Alehosseini, Ali; A. Hejazi, Maryam; Mokhtari, Ghassem; B. Gharehpetian, Gevork; Mohammadi, Mohammad

    2015-06-01

    In this paper, the Bayesian classifier is used to detect and classify the radial deformation and axial displacement of transformer windings. The proposed method is tested on a model of transformer for different volumes of radial deformation and axial displacement. In this method, ultra-wideband (UWB) signal is sent to the simplified model of the transformer winding. The received signal from the winding model is recorded and used for training and testing of Bayesian classifier in different axial displacement and radial deformation states of the winding. It is shown that the proposed method has a good accuracy to detect and classify the axial displacement and radial deformation of the winding.

  19. Test-Wrapper Designs for the Detection of Signal-Integrity Faults on Core-External Interconnects of SoCs

    E-print Network

    Chakrabarty, Krishnendu

    - sistors, and they can operate at Gigahertz frequencies. How- ever, test problems are greatly exacerbatedTest-Wrapper Designs for the Detection of Signal-Integrity Faults on Core-External Interconnects-based system-on-a-chip (SoC) integrated circuits. To ef- fectively test SI faults on core

  20. Analytical Error Analysis of Clifford Gates by the Fault-Path Tracer Method

    E-print Network

    Smitha Janardan; Yu Tomita; Mauricio Gutierrez; Kenneth R. Brown

    2015-12-19

    We estimate the success probability of quantum protocols composed of Clifford operations in the presence of Pauli errors. Our method is derived from the fault-point formalism previously used to determine the success rate of low-distance error correction codes. Here we apply it to a wider range of quantum protocols and identify circuit structures that allow for efficient calculation of the exact success probability and even the final distribution of output states. As examples, we apply our method to the Bernstein-Vazirani algorithm and the Steane [[7,1,3

  1. Accurate monitoring and fault detection in wind measuring devices through wireless sensor networks.

    PubMed

    Khan, Komal Saifullah; Tariq, Muhammad

    2014-01-01

    Many wind energy projects report poor performance as low as 60% of the predicted performance. The reason for this is poor resource assessment and the use of new untested technologies and systems in remote locations. Predictions about the potential of an area for wind energy projects (through simulated models) may vary from the actual potential of the area. Hence, introducing accurate site assessment techniques will lead to accurate predictions of energy production from a particular area. We solve this problem by installing a Wireless Sensor Network (WSN) to periodically analyze the data from anemometers installed in that area. After comparative analysis of the acquired data, the anemometers transmit their readings through a WSN to the sink node for analysis. The sink node uses an iterative algorithm which sequentially detects any faulty anemometer and passes the details of the fault to the central system or main station. We apply the proposed technique in simulation as well as in practical implementation and study its accuracy by comparing the simulation results with experimental results to analyze the variation in the results obtained from both simulation model and implemented model. Simulation results show that the algorithm indicates faulty anemometers with high accuracy and low false alarm rate when as many as 25% of the anemometers become faulty. Experimental analysis shows that anemometers incorporating this solution are better assessed and performance level of implemented projects is increased above 86% of the simulated models. PMID:25421739

  2. User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program

    NASA Technical Reports Server (NTRS)

    Caglayan, A. K.; Godiwala, P. M.; Satz, H. S.

    1988-01-01

    Described are the operation and internal structure of the computer program FINDS (Fault Inferring Nonlinear Detection System). The FINDS algorithm is designed to provide reliable estimates for aircraft position, velocity, attitude, and horizontal winds to be used for guidance and control laws in the presence of possible failures in the avionics sensors. The FINDS algorithm was developed with the use of a digital simulation of a commercial transport aircraft and tested with flight recorded data. The algorithm was then modified to meet the size constraints and real-time execution requirements on a flight computer. For the real-time operation, a multi-rate implementation of the FINDS algorithm has been partitioned to execute on a dual parallel processor configuration: one based on the translational dynamics and the other on the rotational kinematics. The report presents an overview of the FINDS algorithm, the implemented equations, the flow charts for the key subprograms, the input and output files, program variable indexing convention, subprogram descriptions, and the common block descriptions used in the program.

  3. Accurate Monitoring and Fault Detection in Wind Measuring Devices through Wireless Sensor Networks

    PubMed Central

    Khan, Komal Saifullah; Tariq, Muhammad

    2014-01-01

    Many wind energy projects report poor performance as low as 60% of the predicted performance. The reason for this is poor resource assessment and the use of new untested technologies and systems in remote locations. Predictions about the potential of an area for wind energy projects (through simulated models) may vary from the actual potential of the area. Hence, introducing accurate site assessment techniques will lead to accurate predictions of energy production from a particular area. We solve this problem by installing a Wireless Sensor Network (WSN) to periodically analyze the data from anemometers installed in that area. After comparative analysis of the acquired data, the anemometers transmit their readings through a WSN to the sink node for analysis. The sink node uses an iterative algorithm which sequentially detects any faulty anemometer and passes the details of the fault to the central system or main station. We apply the proposed technique in simulation as well as in practical implementation and study its accuracy by comparing the simulation results with experimental results to analyze the variation in the results obtained from both simulation model and implemented model. Simulation results show that the algorithm indicates faulty anemometers with high accuracy and low false alarm rate when as many as 25% of the anemometers become faulty. Experimental analysis shows that anemometers incorporating this solution are better assessed and performance level of implemented projects is increased above 86% of the simulated models. PMID:25421739

  4. LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information

    PubMed Central

    Liu, Zhiwen; Chen, Xuefeng; He, Zhengjia; Shen, Zhongjie

    2013-01-01

    Timely and accurate condition monitoring and fault diagnosis of rotating machinery are very important to maintain a high degree of availability, reliability and operational safety. This paper presents a novel intelligent method based on local mean decomposition (LMD) and multi-class reproducing wavelet support vector machines (RWSVM), which is applied to diagnose rotating machinery faults. First, the sensor-based vibration signals measured from the rotating machinery are preprocessed by the LMD method and product functions (PFs) are produced. Second, statistic features are extracted to acquire more fault characteristic information from the sensitive PF. Finally, these features are fed into a multi-class RWSVM to identify the rotating machinery health conditions. The experimental results validate the effectiveness of the proposed RWSVM method in identifying rotating machinery fault patterns accurately and effectively and its superiority over that based on the general SVM. PMID:23881133

  5. Fault model development for fault tolerant VLSI design

    NASA Astrophysics Data System (ADS)

    Hartmann, C. R.; Lala, P. K.; Ali, A. M.; Visweswaran, G. S.; Ganguly, S.

    1988-05-01

    Fault models provide systematic and precise representations of physical defects in microcircuits in a form suitable for simulation and test generation. The current difficulty in testing VLSI circuits can be attributed to the tremendous increase in design complexity and the inappropriateness of traditional stuck-at fault models. This report develops fault models for three different types of common defects that are not accurately represented by the stuck-at fault model. The faults examined in this report are: bridging faults, transistor stuck-open faults, and transient faults caused by alpha particle radiation. A generalized fault model could not be developed for the three fault types. However, microcircuit behavior and fault detection strategies are described for the bridging, transistor stuck-open, and transient (alpha particle strike) faults. The results of this study can be applied to the simulation and analysis of faults in fault tolerant VLSI circuits.

  6. Spectral analysis method for detecting an element

    DOEpatents

    Blackwood, Larry G [Idaho Falls, ID; Edwards, Andrew J [Idaho Falls, ID; Jewell, James K [Idaho Falls, ID; Reber, Edward L [Idaho Falls, ID; Seabury, Edward H [Idaho Falls, ID

    2008-02-12

    A method for detecting an element is described and which includes the steps of providing a gamma-ray spectrum which has a region of interest which corresponds with a small amount of an element to be detected; providing nonparametric assumptions about a shape of the gamma-ray spectrum in the region of interest, and which would indicate the presence of the element to be detected; and applying a statistical test to the shape of the gamma-ray spectrum based upon the nonparametric assumptions to detect the small amount of the element to be detected.

  7. An adaptive disturbance accommodation approach for robust control and fault detection in uncertain stochastic systems

    NASA Astrophysics Data System (ADS)

    George, Jemin

    The detailed formulation and analysis of a robust control scheme for multi-input multi-output uncertain stochastic systems known as the disturbance accommodating controller is presented. Instead of dealing with system uncertainties and external disturbances separately, the disturbance accommodating control scheme lumps the overall effects of these errors in a to-be-determined model-error vector, and then utilizes a Kalman filter in the feedback loop for simultaneously estimating the system states and the model-error vector from noisy measurements. The estimated states are then used to develop a nominal control law while the estimated model-error vector is used as a signal synthesis adaptive correction to the nominal control input to minimize the adverse effects of system uncertainties and the external disturbances. Since the model-error dynamics is unknown, the process noise covariance associated with the model-error dynamics is used to empirically tune the Kalman filter to yield accurate estimates. This dissertation presents a detailed stability analysis, which examines the explicit dependency of the controlled system's closed-loop performance on the assumed process noise covariance. Development of a robust adaptive disturbance accommodating controller for multi-input multi-output uncertain stochastic systems based on a stochastic adaptive scheme for selecting the appropriate process noise covariance that would guarantee closed-loop stability is presented. The presented approach concurrently tackles the problem of designing robust controllers and estimators for uncertain stochastic systems by indirectly adapting for the estimator gain though updating the estimator parameters such as the process noise covariance matrix in real-time. As presented here, the proposed adaptive disturbance accommodating control scheme can be easily extended to develop robust controllers for saturating systems by adapting for the controller gains along with the estimator parameters. In nonlinear stochastic systems, the proposed adaptive disturbance accommodating control scheme can be exploited for complexity mitigation as well as disturbance attenuation. Since the disturbance accommodating controller is extensively used for fault accommodation, robust fault detection and identification scheme based on the disturbance accommodating control theory is also presented. Though the results presented in this dissertation are supported by detailed mathematical proofs, several numerical simulations are also presented here to further validate the efficiency and applicability of the proposed approaches.

  8. CURRENT METHODS FOR DETECTION OF CRYPTOSPORIDIUM SPECIES

    EPA Science Inventory

    Current methods for detecting protozoa in water produce results that are highly variable. It is difficult to determine if the methods themselves, or the procedures for testing these methods, are the source of the variability. If testing procedures are responsible for high varia...

  9. Bioluminescent bioreporter integrated circuit detection methods

    DOEpatents

    Simpson, Michael L.; Paulus, Michael J.; Sayler, Gary S.; Applegate, Bruce M.; Ripp, Steven A.

    2005-06-14

    Disclosed are monolithic bioelectronic devices comprising a bioreporter and an OASIC. These bioluminescent bioreporter integrated circuit are useful in detecting substances such as pollutants, explosives, and heavy-metals residing in inhospitable areas such as groundwater, industrial process vessels, and battlefields. Also disclosed are methods and apparatus for detection of particular analytes, including ammonia and estrogen compounds.

  10. Molecular methods for pathogen detection and quantification

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Ongoing interest in convenient, inexpensive, fast, sensitive and accurate techniques for detecting and/or quantifying the presence of soybean pathogens has resulted in increased usage of molecular tools. The method of extracting a molecular target (usually DNA or RNA) for detection depends wholly up...

  11. Method of detecting sulfur dioxide

    DOEpatents

    Spicer, Leonard D. (Salt Lake City, UT); Bennett, Dennis W. (Clemson, SC); Davis, Jon F. (Salt Lake City, UT)

    1985-01-01

    (CH.sub.3).sub.3 SiNSO is produced by the reaction of ((CH.sub.3).sub.3 Si).sub.2 NH with SO.sub.2. Also produced in the reaction are ((CH.sub.3).sub.3 Si).sub.2 O and a new solid compound [NH.sub.4 ][(CH.sub.3).sub.3 SiOSO.sub.2 ]. Both (CH.sub.3).sub.3 SiNSO and [NH.sub.4 ][(CH.sub.3).sub.3 SiOSO.sub.2 ] have fluorescent properties. The reaction of the subject invention is used in a method of measuring the concentration of SO.sub.2 pollutants in gases. By the method, a sample of gas is bubbled through a solution of ((CH.sub.3).sub.3 Si).sub.2 NH, whereby any SO.sub.2 present in the gas will react to produce the two fluorescent products. The measured fluorescence of these products can then be used to calculate the concentration of SO.sub.2 in the original gas sample. The solid product [NH.sub.4][(CH.sub.3).sub.3 SiOSO.sub.2 ] may be used as a standard in solid state NMR spectroscopy.

  12. Characterization of the Highway 95 Fault in lower Fortymile Wash using electrical and electromagnetic methods, Nye County, Nevada

    USGS Publications Warehouse

    Macy, Jamie P.; Kryder, Levi; Walker, Jamieson

    2012-01-01

    Coordinated application of electrical and electromagnetic geophysical methods provided better characterization of the Highway 95 Fault. The comparison of dipole-dipole resistivity, TEM, and CSAMT data confirm faulting of an uplifted block of resistive Paleozoic Carbonate that lies beneath a more conductive sandstone unit. A more resistive alluvial basin-fill unit is found above the sandstone unit, and it constitutes only about 150 m of the uppermost subsurface.

  13. Method for Predicting and Detecting Tumor Metastasis

    Cancer.gov

    The National Institute of Child Health and Human Development's Laboratory of Development Neurobiology is seeking statements of capability or interest from parties interested in collaborative research to further develop, evaluate, or commercialize methods for predicting and detecting tumor metastasis.

  14. Optimization of Fault Detection/Diagnosis Model for Thermal Storage System Using AIC 

    E-print Network

    Pan, S.; Zheng, M.; Nakahara, N.

    2006-01-01

    -distance was used, have been reported. In the present paper, a new method for optimal model selection based on AIC(Akaike In-formation Criteria) is examined. In addition, by exam-ining the influence to the distinction and diagnosis rate of the optimal detection...

  15. Method for Trace Oxygen Detection

    NASA Technical Reports Server (NTRS)

    Man, Kim Fung (Inventor); Boumsellek, Said (Inventor); Chutjian, Ara (Inventor)

    1997-01-01

    Trace levels of molecular oxygen are measured by introducing a gas containing the molecular oxygen into a target zone, and impacting the molecular oxygen in the target zone with electrons at the O(-) resonant energy level for dissociative electron attachment to produce O(-) ions. Preferably, the electrons have an energy of about 4 to about 10 eV. The amount of O(-) ions produced is measured, and is correlated with the molecular oxygen content in the target zone. The technique is effective for measuring levels of oxygen below 50 ppb. and even less than 1 ppb. The amount of O(-) can be measured in a quadrupole mass analyzer. Best results are obtained when the electrons have an energy of about 6 to about 8 eV. and preferably about 6.8 eV. The method can be used for other species by selecting the appropriate electron energy level.

  16. Method for detecting coliform organisms

    NASA Technical Reports Server (NTRS)

    Nishioka, K.; Nibley, D. A.; Jeffers, E. L.; Brooks, R. L. (inventors)

    1983-01-01

    A method and apparatus are disclosed for determining the concentration of coliform bacteria in a sample. The sample containing the coliform bacteria is cultured in a liquid growth medium. The cultured bacteria produce hydrogen and the hydrogen is vented to a second cell containing a buffer solution in which the hydrogen dissolves. By measuring the potential change in the buffer solution caused by the hydrogen, as a function of time, the initial concentration of bacteria in the sample is determined. Alternatively, the potential change in the buffer solution can be compared with the potential change in the liquid growth medium to verify that the potential change in the liquid growth medium is produced primarily by the hydrogen gas produced by the coliform bacteria.

  17. Analytic sequential methods for detecting network intrusions

    NASA Astrophysics Data System (ADS)

    Chen, Xinjia; Walker, Ernest

    2014-05-01

    In this paper, we propose an analytic sequential methods for detecting port-scan attackers which routinely perform random "portscans" of IP addresses to find vulnerable servers to compromise. In addition to rigorously control the probability of falsely implicating benign remote hosts as malicious, our method performs significantly faster than other current solutions. We have developed explicit formulae for quick determination of the parameters of the new detection algorithm.

  18. Automated Methods for Multiplexed Pathogen Detection

    SciTech Connect

    Straub, Tim M.; Dockendorff, Brian P.; Quinonez-Diaz, Maria D.; Valdez, Catherine O.; Shutthanandan, Janani I.; Tarasevich, Barbara J.; Grate, Jay W.; Bruckner-Lea, Cindy J.

    2005-09-01

    Detection of pathogenic microorganisms in environmental samples is a difficult process. Concentration of the organisms of interest also co-concentrates inhibitors of many end-point detection methods, notably, nucleic acid methods. In addition, sensitive, highly multiplexed pathogen detection continues to be problematic. The primary function of the BEADS (Biodetection Enabling Analyte Delivery System) platform is the automated concentration and purification of target analytes from interfering substances, often present in these samples, via a renewable surface column. In one version of BEADS, automated immunomagnetic separation (IMS) is used to separate cells from their samples. Captured cells are transferred to a flow-through thermal cycler where PCR, using labeled primers, is performed. PCR products are then detected by hybridization to a DNA suspension array. In another version of BEADS, cell lysis is performed, and community RNA is purified and directly labeled. Multiplexed detection is accomplished by direct hybridization of the RNA to a planar microarray. The integrated IMS/PCR version of BEADS can successfully purify and amplify 10 E. coli O157:H7 cells from river water samples. Multiplexed PCR assays for the simultaneous detection of E. coli O157:H7, Salmonella, and Shigella on bead suspension arrays was demonstrated for the detection of as few as 100 cells for each organism. Results for the RNA version of BEADS are also showing promising results. Automation yields highly purified RNA, suitable for multiplexed detection on microarrays, with microarray detection specificity equivalent to PCR. Both versions of the BEADS platform show great promise for automated pathogen detection from environmental samples. Highly multiplexed pathogen detection using PCR continues to be problematic, but may be required for trace detection in large volume samples. The RNA approach solves the issues of highly multiplexed PCR and provides ''live vs. dead'' capabilities. However, sensitivity of the method will need to be improved for RNA analysis to replace PCR.

  19. A Double-difference Earthquake location algorithm: Method and application to the Northern Hayward Fault, California

    USGS Publications Warehouse

    Waldhauser, F.; Ellsworth, W.L.

    2000-01-01

    We have developed an efficient method to determine high-resolution hypocenter locations over large distances. The location method incorporates ordinary absolute travel-time measurements and/or cross-correlation P-and S-wave differential travel-time measurements. Residuals between observed and theoretical travel-time differences (or double-differences) are minimized for pairs of earthquakes at each station while linking together all observed event-station pairs. A least-squares solution is found by iteratively adjusting the vector difference between hypocentral pairs. The double-difference algorithm minimizes errors due to unmodeled velocity structure without the use of station corrections. Because catalog and cross-correlation data are combined into one system of equations, interevent distances within multiplets are determined to the accuracy of the cross-correlation data, while the relative locations between multiplets and uncorrelated events are simultaneously determined to the accuracy of the absolute travel-time data. Statistical resampling methods are used to estimate data accuracy and location errors. Uncertainties in double-difference locations are improved by more than an order of magnitude compared to catalog locations. The algorithm is tested, and its performance is demonstrated on two clusters of earthquakes located on the northern Hayward fault, California. There it colapses the diffuse catalog locations into sharp images of seismicity and reveals horizontal lineations of hypocenter that define the narrow regions on the fault where stress is released by brittle failure.

  20. Radionuclide detection devices and associated methods

    DOEpatents

    Mann, Nicholas R. (Rigby, ID); Lister, Tedd E. (Idaho Falls, ID); Tranter, Troy J. (Idaho Falls, ID)

    2011-03-08

    Radionuclide detection devices comprise a fluid cell comprising a flow channel for a fluid stream. A radionuclide collector is positioned within the flow channel and configured to concentrate one or more radionuclides from the fluid stream onto at least a portion of the radionuclide collector. A scintillator for generating scintillation pulses responsive to an occurrence of a decay event is positioned proximate at least a portion of the radionuclide collector and adjacent to a detection system for detecting the scintillation pulses. Methods of selectively detecting a radionuclide are also provided.

  1. High sensitivity leak detection method and apparatus

    DOEpatents

    Myneni, Ganapatic R. (Grafton, VA)

    1994-01-01

    An improved leak detection method is provided that utilizes the cyclic adsorption and desorption of accumulated helium on a non-porous metallic surface. The method provides reliable leak detection at superfluid helium temperatures. The zero drift that is associated with residual gas analyzers in common leak detectors is virtually eliminated by utilizing a time integration technique. The sensitivity of the apparatus of this disclosure is capable of detecting leaks as small as 1.times.10.sup.-18 atm cc sec.sup.-1.

  2. High sensitivity leak detection method and apparatus

    DOEpatents

    Myneni, G.R.

    1994-09-06

    An improved leak detection method is provided that utilizes the cyclic adsorption and desorption of accumulated helium on a non-porous metallic surface. The method provides reliable leak detection at superfluid helium temperatures. The zero drift that is associated with residual gas analyzers in common leak detectors is virtually eliminated by utilizing a time integration technique. The sensitivity of the apparatus of this disclosure is capable of detecting leaks as small as 1 [times] 10[sup [minus]18] atm cc sec[sup [minus]1]. 2 figs.

  3. Method for remote detection of trace contaminants

    DOEpatents

    Simonson, Robert J.; Hance, Bradley G.

    2003-09-09

    A method for remote detection of trace contaminants in a target area comprises applying sensor particles that preconcentrate the trace contaminant to the target area and detecting the contaminant-sensitive fluorescence from the sensor particles. The sensor particles can have contaminant-sensitive and contaminant-insensitive fluorescent compounds to enable the determination of the amount of trace contaminant present in the target are by relative comparison of the emission of the fluorescent compounds by a local or remote fluorescence detector. The method can be used to remotely detect buried minefields.

  4. A method to determine fault vectors in 4H-SiC from stacking sequences observed on high resolution transmission electron microscopy images

    SciTech Connect

    Wu, Fangzhen; Wang, Huanhuan; Raghothamachar, Balaji; Dudley, Michael; Mueller, Stephan G.; Chung, Gil; Sanchez, Edward K.; Hansen, Darren; Loboda, Mark J.; Zhang, Lihua; Su, Dong; Kisslinger, Kim; Stach, Eric

    2014-09-14

    A new method has been developed to determine the fault vectors associated with stacking faults in 4H-SiC from their stacking sequences observed on high resolution TEM images. This method, analogous to the Burgers circuit technique for determination of dislocation Burgers vector, involves determination of the vectors required in the projection of the perfect lattice to correct the deviated path constructed in the faulted material. Results for several different stacking faults were compared with fault vectors determined from X-ray topographic contrast analysis and were found to be consistent. This technique is expected to applicable to all structures comprising corner shared tetrahedra.

  5. Linear versus non-linear earthquake location and seismogenic fault detection in the southern Tyrrhenian Sea, Italy

    NASA Astrophysics Data System (ADS)

    Presti, D.; Orecchio, B.; Falcone, G.; Neri, G.

    2008-02-01

    We compare the performances of linear and non-linear hypocentre location methods working in 3-D velocity structures, a not-fully explored subject of main interest in the regions where the location problem is ill-conditioned. Comparisons are made between the linear location method known as SIMUL and the non-linear probabilistic algorithm named BAYLOC, using the data sets of the two main seismic sequences which occurred in the last decade in the southern Tyrrhenian Sea. We find that in the suboptimal network conditions of these sequences the SIMUL and BAYLOC algorithms furnish hypocentre coordinates of comparable accuracy leading to similar hypocentre spatial trends, while the location error estimates from SIMUL are, in general, less accurate than BAYLOC's. These findings are further supported by locations of synthetic events performed in the same network-model conditions of the real sequences. We conclude that linearized methods produce lower quality location error estimates but no overall bias in the hypocentral coordinates compared to non-linear methods. Therefore, we extend to 3-D location a conclusion drawn by previous investigators for 1-D location. Because location error estimates may be crucial to establish whether the hypocentre trend of a sequence does really mark the seismogenic structure or simply reflects ill-conditioning of the location process, we based on the BAYLOC probabilistic algorithm our approach to hypocentre trend evaluation for seismogenic fault detection. This procedure, named ISO-TEST, works through isotropic generation of synthetic hypocentres inside the sequence volume (simulations) and comparison by misfit variables of the location probability function of the sequence with probability functions from simulations. The application of ISO-TEST showed that while the NE-SW trend of one of the study sequences can only in minor part be ascribed to ill-conditioning of the location process, and then it may reasonably be proposed as the signature of the source, the NW-SE trend of the other is contaminated in a greater percentage by the location process, and we are led to conclude that source detection is doubtful in this case.

  6. Simulating Large-Scale Earthquake Dynamic Rupture Scenarios On Natural Fault Zones Using the ADER-DG Method

    NASA Astrophysics Data System (ADS)

    Gabriel, Alice; Pelties, Christian

    2014-05-01

    In this presentation we will demonstrate the benefits of using modern numerical methods to support physic-based ground motion modeling and research. For this purpose, we utilize SeisSol an arbitrary high-order derivative Discontinuous Galerkin (ADER-DG) scheme to solve the spontaneous rupture problem with high-order accuracy in space and time using three-dimensional unstructured tetrahedral meshes. We recently verified the method in various advanced test cases of the 'SCEC/USGS Dynamic Earthquake Rupture Code Verification Exercise' benchmark suite, including branching and dipping fault systems, heterogeneous background stresses, bi-material faults and rate-and-state friction constitutive formulations. Now, we study the dynamic rupture process using 3D meshes of fault systems constructed from geological and geophysical constraints, such as high-resolution topography, 3D velocity models and fault geometries. Our starting point is a large scale earthquake dynamic rupture scenario based on the 1994 Northridge blind thrust event in Southern California. Starting from this well documented and extensively studied event, we intend to understand the ground-motion, including the relevant high frequency content, generated from complex fault systems and its variation arising from various physical constraints. For example, our results imply that the Northridge fault geometry favors a pulse-like rupture behavior.

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

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

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

  8. Detection of postseismic fault-zone collapse following the Landers earthquake

    USGS Publications Warehouse

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

    1996-01-01

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

  9. A Novel Method of Line Detection using Image Integration Method

    NASA Astrophysics Data System (ADS)

    Lin, Daniel; Sun, Bo

    2015-03-01

    We developed a novel line detection algorithm based on image integration method. Hough Transformation uses spatial image gradient method to detect lines on an image. This is problematic because if the image has a region of high noise intensity, the gradient would point towards the noisy region . Denoising the noisy image requires an application of sophisticated noise reduction algorithm which increases computation complexity. Our algorithm can remedy this problem by averaging the pixels around the image region of interest. We were able to detect collagen fiber lines on an image produced by confocal microscope.

  10. Is low-angle normal fault slip aided by local stress rotations?: Assessment of paleostress inversion methods

    NASA Astrophysics Data System (ADS)

    Luther, A. L.; Axen, G. J.; Selverstone, J.; Khalsa, N.

    2009-12-01

    Classical fault mechanic theory does not adequately explain slip on “weak” faults oriented at high angles to the regional maximum stress direction, such as the San Andreas Fault and low-angle normal faults. One hypothesis is that stress rotation due to fault-weakening mechanisms allows slip, which may be testable using detailed paleostress analyses of minor faults and tensile fractures. Preliminary data from the footwalls of the Whipple detachment (WD) and the West Salton detachment (WSD) suggest lateral and/or vertical stress rotations. Three inversion programs that use different fault-slip datasets are compared. 1) FaultKin (Marrett and Allmendinger ‘90; Cladouhos and Allmendinger ‘93) determines the principal strain directions using only faults with striae and known slip senses; principal stress orientations are determined assuming coaxiality. To date, FaultKin results appear to be the most reproducible, but it is difficult to find enough faults with striae and slip sense in the small outcrop areas of our study. 2) Slick.bas (Ramsey and Lisle ‘00) uses a grid search to find the best-fit stress tensor from fault and striae orientations, but does not accept slip sense. This program can yield erroneous stress fields that predict slip senses opposite those known for some faults (particularly faults at a high angle to sigma 1). 3) T-TECTO 2.0 (Zalohar and Vrabec ‘07) applies a Gaussian approach, using orientations of faults and striae, the slip senses of any faults for which it is known, plus tensile fractures. We expect that this flexibility of input data types will be best, but testing is preliminary. Paleostress analyses assume that minor faults slipped in response to constant, homogeneous stress fields. We use shear and tensile fractures and cross-cutting relationships from the upper ~25 m of both footwalls to test for spatial and temporal changes to the paleostress field. Paleostress analysis of fractures ~0.3 - 2 m below the WSD on the N limb of an antiform suggests that sigma 3 plunges moderately (~45 degrees) W, sigma 1 plunges gently S, and sigma 2 is steep, consistent with wrench-related folding about E-W trends during WSD slip. However, tensile fractures in the immediately overlying ultracataclasite yield sigma 3 with a shallow W plunge (~4 degrees). In a synformal trough, Reidel shears in the upper 1-2 m of the WSD footwall suggest a moderately (~50 degrees) E plunging sigma 1. Deeper (2-10 m) in the footwall, shear fractures have different but consistent orientations, suggesting a change in the stress field. Preliminary results from several sets of shear fractures in the WD footwall suggest that sigma 1 is steep (~75-90 degrees) in the chlorite breccia zone (implying low shear traction) but is shallower (~45 degrees) in the deeper damage zone. Prior work (Axen & Selverstone ‘94) found that sigma 1 becomes steep again at greater depths. Continued testing of paleostress analysis methods and several other datasets are in progress to confirm our results.

  11. Linear vs. Non-Linear Earthquake Location and Seismogenic Fault Detection in the Southern Tyrrhenian Sea, Italy

    NASA Astrophysics Data System (ADS)

    Presti, D.; Orecchio, B.; Falcone, G.; Neri, G.

    2006-12-01

    We report a comparison between the performances of linearized and non-linear hypocenter location algorithms working in 3D velocity structures. For this purpose, we used the SIMUL linearized location method by Evans et al. (1994) and the BAYLOC non-linear grid-search probabilistic algorithm by Presti et al. (2004). Comparisons are made using the datasets of P and S readings relative to the two main seismic sequences occurring in the last ten years in the southern Tyrrhenian sea, i.e. the 1998 sequence of maximum magnitude 5.2 near Ustica island and the 2002 sequence of max. magnitude 5.9 offshore Palermo city. We find that in the relatively poor network conditions of both sequences the SIMUL and BAYLOC algorithms produce hypocenter locations of comparable accuracy, while location error estimates from SIMUL are generally less accurate than BAYLOC's. This result is a confirmation in a 3D velocity structure of a finding already reported by previous investigators who compared the performances of linear vs. non-linear location algorithms in 1D structures (Lomax et al., 1998; Lomax et al., 2000; Lippitsch et al., 2005) and further underlines the implications of the linearization process. Also, referring to the problem of detecting seismogenic faults from hypocenter trends delineated in poor network conditions, we introduced a procedure based on BAYLOC's location probability concept with the purpose of establishing when hypocenter trends really mark seismogenic structures and when they simply reflect ill-conditionning of the location process. This procedure (ISO-TEST) showed that while the NE-SW trend of the 2002 sequence can only in minor part be ascribed to ill-conditioning of the location process (what basically means that it effectively marks the orientation of the source), the NW-SE trend of the 1998 sequence is strongly contamined by the location process and source detection is therefore doubtful in this case. Although ISO-TEST is shown to be already capable to bring benefits to seismogenic fault detection in areas where the location problem ill-conditionned, improvements can be expected from wider testing of synthetic earthquake generators and deeper evaluation of misfits between synthetic and real location probability distributions in the space domain. Efforts are currently made in this connection. References Evans, J. R., Eberhart-Phillips, D. & Thurber, C. H., 1994. User's manual for simulps12 for imaging Vp and Vp/Vs: a derivative of the "Thurber" tomographic inversion simul3 for local earthquakes and explosions, U.S. Geol. Surv. Open-file Rept., 94-431. Lippitsch, R., White, R., & Soosalu, H. 2005. Precise hypocentre relocation of microearthquakes in a high- temperature geothermal field: the Torfajökull central volcano, Iceland, Geophysical Journal International 160 - 371-388. Lomax, A., Cattaneo, M., Bethoux, N., Deschamps, A., Courboulex, F., Deverchère, J., & Virieux, J., 1998. Comparison of linear and non-linear earthquake locations for the 1995 Ventimiglia sequence, Poster presentation at: European Geophysical Society, XXII General Assembly, http://alomax.free.fr/posters/vintimiglia. Lomax A., Virieux, J., Volant, P., & Berge-Thierry C., 2000. Probabilistic earthquake location in 3D and layered model, in Advances in Seismic Event Location, 101-134, Kluwer Academic Publishers, Netherlands. Presti, D., Troise, C. & De Natale G., 2004. Probabilistic location of seismic sequences in heterogeneous media, Bull. Seism. Soc. Am., 94, 6, 2239 2253.

  12. Fault diagnosis

    NASA Technical Reports Server (NTRS)

    Abbott, Kathy

    1990-01-01

    The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

  13. Automated macromolecular crystal detection system and method

    DOEpatents

    Christian, Allen T. (Tracy, CA); Segelke, Brent (San Ramon, CA); Rupp, Bernard (Livermore, CA); Toppani, Dominique (Fontainebleau, FR)

    2007-06-05

    An automated macromolecular method and system for detecting crystals in two-dimensional images, such as light microscopy images obtained from an array of crystallization screens. Edges are detected from the images by identifying local maxima of a phase congruency-based function associated with each image. The detected edges are segmented into discrete line segments, which are subsequently geometrically evaluated with respect to each other to identify any crystal-like qualities such as, for example, parallel lines, facing each other, similarity in length, and relative proximity. And from the evaluation a determination is made as to whether crystals are present in each image.

  14. Earthquake disaster mitigation of Lembang Fault West Java with electromagnetic method

    SciTech Connect

    Widodo

    2015-04-24

    The Lembang fault is located around eight kilometers from Bandung City, West Java, Indonesia. The existence of this fault runs through densely populated settlement and tourism area. It is an active fault structure with increasing seismic activity where the 28 August 2011 earthquake occurred. The seismic response at the site is strongly influenced by local geological conditions. The ambient noise measurements from the western part of this fault give strong implication for a complex 3-D tectonic setting. Hence, near surface Electromagnetic (EM) measurements are carried out to understand the location of the local active fault of the research area. Hence, near surface EM measurements are carried out to understand the location of the local active fault and the top of the basement structure of the research area. The Transientelectromagnetic (TEM) measurements are carried out along three profiles, which include 35 TEM soundings. The results indicate that TEM data give detailed conductivity distribution of fault structure in the study area.

  15. Earthquake disaster mitigation of Lembang Fault West Java with electromagnetic method

    NASA Astrophysics Data System (ADS)

    Widodo

    2015-04-01

    The Lembang fault is located around eight kilometers from Bandung City, West Java, Indonesia. The existence of this fault runs through densely populated settlement and tourism area. It is an active fault structure with increasing seismic activity where the 28 August 2011 earthquake occurred. The seismic response at the site is strongly influenced by local geological conditions. The ambient noise measurements from the western part of this fault give strong implication for a complex 3-D tectonic setting. Hence, near surface Electromagnetic (EM) measurements are carried out to understand the location of the local active fault of the research area. Hence, near surface EM measurements are carried out to understand the location of the local active fault and the top of the basement structure of the research area. The Transientelectromagnetic (TEM) measurements are carried out along three profiles, which include 35 TEM soundings. The results indicate that TEM data give detailed conductivity distribution of fault structure in the study area.

  16. Performance Study of Enhanced Auto-Associative Neural Networks For Sensor Fault Detection 

    E-print Network

    Najafi, M.; Culp, C.; Langari, R.

    2004-01-01

    -based approach relies on the correct input data. It assumes that the input to the real system and the input to the model are correct (fault free). When there is a notable difference between the output of the real system and the output of the model, a problem... the E-AANN provides reconstructed data. If the input data is fault free, then the E-AANN output will be the same as the input and the difference between the output and the input will be zero. When one of the inputs drifts or varies from the normal...

  17. Three Methods of Detection of Hydrazines

    NASA Technical Reports Server (NTRS)

    Griffin, Timothy; Berger, Cristina

    2010-01-01

    Three proposed methods for measuring trace quantities of hydrazines involve ionization and detection of hydrazine derivatives. These methods are intended to overcome the limitations of prior hydrazine- detection methods. Hydrazine (Hz), monomethylhydrazine (MMH), and unsymmetrical dimethylhydrazine (UDMH) are hypergolic fuels and are highly reactive, toxic, and corrosive. A capability to measure concentrations of hydrazines is desirable for detecting leaks and ensuring safety in aerospace settings and in some industrial settings in which these compounds are used. One of the properties (high reactivity) that make it desirable to detect trace amounts of hydrazines also makes it difficult to detect hydrazines and measure their concentrations accurately using prior methods: significant amounts are lost to thermal and catalytic decomposition prior to detection. Further complications arise from the sticky nature of hydrazines: Sample hydrazine molecules tend to become irreversibly adsorbed onto solid surfaces with which they come into contact during transport to detectors, giving rise to drift in detector responses. In each proposed method, the reactive, sticky nature of hydrazines would be turned to advantage by providing a suitably doped substrate surface with which the hydrazines would react. The resulting hydrazine derivatives would be sufficiently less sticky and sufficiently more stable so that fewer molecules would be lost to decomposition or adsorption during transport. Consequently, it would be possible to measure concentration with more sensitivity and less error than in prior techniques. The first proposed method calls for the use of a recently developed technique known as desorption electrospray ionization (DESI), in which a pneumatically assisted micro -electrospray at ambient pressure is directed at a surface of interest. In this case, the surface of interest would be that of a substrate described above.

  18. [Super-low-frequency spectrum analysis for buried faults in coalfield].

    PubMed

    Chen, Li; Qin, Qi-Ming; Zhen, Guang-Wei; Wang, Nan; Bai, Yan-Bing; Chen, Chao

    2013-08-01

    Based on the super-low-frequency (SLF) electromagnetic detection technology, the advanced detection for the buried fault in the coalfield is still at the exploratory stage, while the technology has a strong practical significance for production and design of the coal mine. Firstly, in this paper, the SLF electromagnetic detection signals were collected in study area. Spectrum analysis of SLF signal by wavelet transform can remove high-frequency noise. Secondly, the profile of the measuring line across the fault was analyzed and interpreted geologically. Accordingly SLF spectrum characteristics of the buried fault could be researched. Finally, combined with the geological and seismic data, the characteristics and distribution of fault structures can be verified in the mining area. The results show that: the buried fault could be detected quickly and effectively by SLF electromagnetic detection Hence, SLF electromagnetic detection technology is an effective method for buried fault detection. PMID:24159862

  19. A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform.

    PubMed

    Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

    2015-01-01

    Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert-Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500-800 and a m range of 50-300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction. PMID:26540059

  20. Seismic Investigation of the Pointer Ridge offshore southwestern Taiwan: Detection of fluid migration pathways and fault seal analysis

    NASA Astrophysics Data System (ADS)

    Han, W. C.; Liu, C. S.; Lin, C. C.; Wang, Y.

    2014-12-01

    This study analyzes both 2D and 3D seismic images in the Pointer Ridge area for gas hydrate investigation. Pointer Ridge is a ridge situated on the passive China continental margin formed by down slope erosion of the continental slope material on either side of the ridge. High methane flux rate and several seismic chimneys were observed in this area from previous studies, which may imply active ongoing fluid migration processes. To find the possible fluid migration pathways and understand the fluid migration processes, we firstly use both 2D and 3D seismic images to map the spatial distribution of the BSRs, and to identify the structural and sedimentary features in our study area. Secondly, seismic attribute analyses are carried out for fluid migration pathways detection and fault seal analysis. Finally, we propose a conceptual model to illustrate how fluids migrate along those pathways to the seafloor. The results show that the fluid migration pathways obtained from seismic attribute analysis results correlate well with the chimney and fault structures recognized from conventional seismic amplitude sections. We suggest that high angle normal faults may play an important role for fluid migrating upward, and the ongoing fluid migration processes will increase the seafloor instabilities. Since the Pointer Ridge is a gas hydrate leaking site, our results could provide useful information for further risk evaluation.