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Sample records for active fault diagnosis

  1. Active Fault Diagnosis in Sampled-data Systems

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2015-01-01

    The focus in this paper is on active fault diagnosis (AFD) in closed-loop sampleddata systems. Applying the same AFD architecture as for continuous-time systems does not directly result in the same set of closed-loop matrix transfer functions. For continuous-time systems, the LFT (linear fractional...

  2. Information Based Fault Diagnosis

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2008-01-01

    Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems. These...... inputs are disturbance inputs, reference inputs and auxilary inputs. The diagnosis of the system is derived by an evaluation of the signature from the inputs in the residual outputs. The changes of the signatures form the external inputs are used for detection and isolation of the parametric faults....

  3. Information Based Fault Diagnosis

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    Fault detection and isolation, (FDI) of parametric faults in dynamic systems will be considered in this paper. An active fault diagnosis (AFD) approach is applied. The fault diagnosis will be investigated with respect to different information levels from the external inputs to the systems. These...... inputs are disturbance inputs, reference inputs and auxilary inputs. The diagnosis of the system is derived by an evaluation of the signature from the inputs in the residual outputs. The changes of the signatures form the external inputs are used for detection and isolation of the parametric faults....

  4. Fault Diagnosis in a Centrifugal Pump Using Active Magnetic Bearings

    Nordmann Rainer; Aenis Martin

    2004-01-01

    The number of rotors running in active magnetic bearings (AMBs) has increased over the last few years. These systems offer a great variety of advantages compared to conventional systems. The aim of this article is to use the AMBs together with a developed built-in software for identification, fault detection, and diagnosis in a centrifugal pump. A single-stage pump representing the turbomachines is investigated. During full operation of the pump, the AMBs are used as actuators to generate def...

  5. Active Fault Diagnosis and Assessment for Aircraft Health Management Project

    National Aeronautics and Space Administration — To address the NASA LaRC need for innovative methods and tools for the diagnosis of aircraft faults and failures, Physical Optics Corporation (POC) proposes to...

  6. Performance based fault diagnosis

    Niemann, Hans Henrik

    2002-01-01

    Different aspects of fault detection and fault isolation in closed-loop systems are considered. It is shown that using the standard setup known from feedback control, it is possible to formulate fault diagnosis problems based on a performance index in this general standard setup. It is also shown...

  7. Active Fault Diagnosis for Hybrid Systems Based on Sensitivity Analysis and EKF

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas

    2011-01-01

    An active fault diagnosis approach for different kinds of faults is proposed. The input of the approach is designed off-line based on sensitivity analysis such that the maximum sensitivity for each individual system parameter is obtained. Using maximum sensitivity, results in a better precision in...... the estimation of the corresponding parameter. The fault detection and isolation is done by comparing the nominal parameters with those estimated by Extended Kalman Filter (EKF). In study, Gaussian noise is used as the input disturbance as well as the measurement noise for simulation. The method is...

  8. On infinite horizon active fault diagnosis for a class of non-linear non-Gaussian systems

    Punčochár Ivo

    2014-12-01

    Full Text Available The paper considers the problem of active fault diagnosis for discrete-time stochastic systems over an infinite time horizon. It is assumed that the switching between a fault-free and finitely many faulty conditions can be modelled by a finite-state Markov chain and the continuous dynamics of the observed system can be described for the fault-free and each faulty condition by non-linear non-Gaussian models with a fully observed continuous state. The design of an optimal active fault detector that generates decisions and inputs improving the quality of detection is formulated as a dynamic optimization problem. As the optimal solution obtained by dynamic programming requires solving the Bellman functional equation, approximate techniques are employed to obtain a suboptimal active fault detector.

  9. Modeling fault diagnosis as the activation and use of a frame system. [for pilot problem-solving rating

    Smith, Philip J.; Giffin, Walter C.; Rockwell, Thomas H.; Thomas, Mark

    1986-01-01

    Twenty pilots with instrument flight ratings were asked to perform a fault-diagnosis task for which they had relevant domain knowledge. The pilots were asked to think out loud as they requested and interpreted information. Performances were then modeled as the activation and use of a frame system. Cognitive biases, memory distortions and losses, and failures to correctly diagnose the problem were studied in the context of this frame system model.

  10. A study on transient enhancement for fault diagnosis based on an active noise control system

    Tian, X.; Gu, Fengshou; Zhen, Dong; Tran, Tung; Ball, Andrew

    2012-01-01

    Active noise control (ANC) is a more effective technique used for acoustic noise cancelation in comparison with passive approaches which are difficult and expensive to implement, especially for cancelling the noise in the low frequency range. In the ANC system, an anti-noise signal is introduced to suppress the primary noise to produce a residual which is used for updating the adaptive filter coefficients. In this paper, a method of transient content enhancement for fault detection and diagno...

  11. Diagnosis and fault-tolerant control

    Blanke, Mogens; Lunze, Jan; Staroswiecki, Marcel

    2016-01-01

    Fault-tolerant control aims at a gradual shutdown response in automated systems when faults occur. It satisfies the industrial demand for enhanced availability and safety, in contrast to traditional reactions to faults, which bring about sudden shutdowns and loss of availability. The book presents effective model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault through the process, to test the fault detectability and to find the redundancies in the process that can be used to ensure fault tolerance. It also introduces design methods suitable for diagnostic systems and fault-tolerant controllers for continuous processes that are described by analytical models of discrete-event systems represented by automata. The book is suitable for engineering students, engineers in industry and researchers who wish to get an overview of the variety of approaches to process diagnosis and fault-tolerant contro...

  12. Quantitative Diagnosis of Fault Severity Trend of Rolling Element Bearings

    CUI Lingli; MA Chunqing; ZHANG Feibin; WANG Huaqing

    2015-01-01

    The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy(WPCE) is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.

  13. Quantitative diagnosis of fault severity trend of rolling element bearings

    Cui, Lingli; Ma, Chunqing; Zhang, Feibin; Wang, Huaqing

    2015-11-01

    The condition monitoring and fault diagnosis of rolling element bearings are particularly crucial in rotating mechanical applications in industry. A bearing fault signal contains information not only about fault condition and fault type but also the severity of the fault. This means fault severity quantitative analysis is one of most active and valid ways to realize proper maintenance decision. Aiming at the deficiency of the research in bearing single point pitting fault quantitative diagnosis, a new back-propagation neural network method based on wavelet packet decomposition coefficient entropy is proposed. The three levels of wavelet packet coefficient entropy(WPCE) is introduced as a characteristic input vector to the BPNN. Compared with the wavelet packet decomposition energy ratio input vector, WPCE shows more sensitive in distinguishing from the different fault severity degree of the measured signal. The engineering application results show that the quantitative trend fault diagnosis is realized in the different fault degree of the single point bearing pitting fault. The breakthrough attempt from quantitative to qualitative on the pattern recognition of rolling element bearings fault diagnosis is realized.

  14. Diagnosis and Fault-tolerant Control

    Blanke, Mogens; Kinnaert, Michel; Lunze, Jan; Staroswiecki, Marcel

    The book presents effective model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault through the process, to test the fault detectability and to find the redundancies in the process...... applicability of the presented methods. The theoretical results are illustrated by two running examples which are used throughout the book. The book addresses engineering students, engineers in industry and researchers who wish to get a survey over the variety of approaches to process diagnosis and fault...

  15. Aluminium Process Fault Detection and Diagnosis

    Nazatul Aini Abd Majid; Taylor, Mark P; Chen, John J. J.; Brent R. Young

    2015-01-01

    The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and propo...

  16. Aluminium Process Fault Detection and Diagnosis

    Nazatul Aini Abd Majid

    2015-01-01

    Full Text Available The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments.

  17. Fault Diagnosis in Deaerator Using Fuzzy Logic

    S Srinivasan

    2007-01-01

    Full Text Available In this paper a fuzzy logic based fault diagnosis system for a deaerator in a power plant unit is presented. The system parameters are obtained using the linearised state space deaerator model. The fuzzy inference system is created and rule base are evaluated relating the parameters to the type and severity of the faults. These rules are fired for specific changes in system parameters and the faults are diagnosed.

  18. Navigation System Fault Diagnosis for Underwater Vehicle

    Falkenberg, Thomas; Gregersen, Rene Tavs; Blanke, Mogens

    This paper demonstrates fault diagnosis on unmanned underwater vehicles (UUV) based on analysis of structure of the nonlinear dynamics. Residuals are generated using dierent approaches in structural analysis followed by statistical change detection. Hypothesis testing thresholds are made signal...

  19. Transformer fault diagnosis using continuous sparse autoencoder.

    Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou

    2016-01-01

    This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis. PMID:27119052

  20. Development of fault diagnosis using statistical method

    This paper presents the development of fault diagnosis using statistical method. We apply the fault diagnosis to the process data of the uranium enrichment demonstration plant. A uranium enrichment plant is structurally stable and there is a little change in the process data. It is necessary to detect the microscopic fluctuation in the process data before the serious fault. First we apply the auto-regressive model (AR model) to the process data, and estimate the validity of the AR model. Next we attend the cross-correlation in the process data, and construct the physical model using the simple law of the physics in the plant. We estimate the validity of the physical model. As a result we confirm that the fault diagnosis using statistical method is valid in the stable plant. (author)

  1. Novel Fault Diagnosis Scheme for HVDC System via ESO

    YAN Bing-yong; TIAN Zuo-hua; SHI Song-jiao

    2007-01-01

    A novel fault detection and identification (FDI) scheme for HVDC (High Voltage Direct Current Transmission) system was presented. It was based on the unique active disturbance rejection concept, where the HVDC system faults were estimated using an extended states observer (ESO). Firstly, the mathematical model of HVDC system was constructed, where the system states and disturbance were treated as an extended state. An augment HVDC system was established by using the extended state in rectify side and converter side, respectively. Then, a fault diagnosis filter was established to diagnose the HVDC system faults via the ESO theory. The evolution of the extended state in the augment HVDC system can reflect the actual system faults and disturbances, which can be used for the fault diagnosis purpose. A novel feature of this approach is that it can simultaneously detect and identify the shape and magnitude of the HVDC faults and disturbance. Finally, different kinds of HVDC faults were simulated to illustrate the feasibility and effectiveness of the proposed ESO based FDI approach. Compared with the neural network based or support vector machine based FDI approach, the ESO based FDI scheme can reduce the fault detection time dramatically and track the actual system fault accurately. What's more important, it needs not do complex online calculations and the training of neural network so that it can be applied into practice.

  2. Fault Diagnosis and Accommodation of LTI systems by modified Youla parameterization

    Minupriya A

    2012-06-01

    Full Text Available In this paper an Active Fault Tolerant Control (FTC scheme is proposed for Linear Time Invariant (LTI systems, which achieves fault diagnosis followed by fault accommodation. The fault diagnosis scheme is carried out in two steps; Fault detection followed by Fault isolation. Fault detection filter use the sensor measurements to generate residuals, which have a unique static pattern in response to each fault. Distortion in these static patterns generates the probability of the presence of fault. The fault accommodation scheme is carried out using the Generalized Internal Model Control (GIMC architecture, also known as modified Youla parameterization. In addition, performance indices are also evaluated to indicate that the resulting fault tolerant scheme can detect, identify and accommodate actuator and sensor faults under additive faults. The DC motor example is considered for the demonstration of the proposed scheme.

  3. Fault Diagnosis in HVAC Chillers

    Choi, Kihoon; Namuru, Setu M.; Azam, Mohammad S.; Luo, Jianhui; Pattipati, Krishna R.; Patterson-Hine, Ann

    2005-01-01

    Modern buildings are being equipped with increasingly sophisticated power and control systems with substantial capabilities for monitoring and controlling the amenities. Operational problems associated with heating, ventilation, and air-conditioning (HVAC) systems plague many commercial buildings, often the result of degraded equipment, failed sensors, improper installation, poor maintenance, and improperly implemented controls. Most existing HVAC fault-diagnostic schemes are based on analytical models and knowledge bases. These schemes are adequate for generic systems. However, real-world systems significantly differ from the generic ones and necessitate modifications of the models and/or customization of the standard knowledge bases, which can be labor intensive. Data-driven techniques for fault detection and isolation (FDI) have a close relationship with pattern recognition, wherein one seeks to categorize the input-output data into normal or faulty classes. Owing to the simplicity and adaptability, customization of a data-driven FDI approach does not require in-depth knowledge of the HVAC system. It enables the building system operators to improve energy efficiency and maintain the desired comfort level at a reduced cost. In this article, we consider a data-driven approach for FDI of chillers in HVAC systems. To diagnose the faults of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various fault conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller faults.

  4. Bearing fault diagnosis based on vibration signals.

    Abdusslam, S.A.; Gu, Fengshou; Ball, Andrew

    2009-01-01

    The vibration signal obtained from operating machines contains information relating to machine condition as well as noise. Further processing of the signal is necessary to elicit information particularly relevant to bearing faults. Many techniques have been employed to process the vibration signals in bearing faults detection and diagnosis. Two common techniques, time domain techniques and frequency domain techniques are used in this paper to investigate bearings condition.

  5. Fault Diagnosis of Autonomous Underwater Vehicles

    Xiao Liang

    2013-04-01

    Full Text Available In this study, we propose the least disturbance algorithm adding scale factor and shift factor. The dynamic learning ratio can be calculated to minimize the scale factor and shift factor of wavelet function and the variation of net weights and the algorithm improve the stability and the convergence of wavelet neural network. It was applied to build the dynamical model of autonomous underwater vehicles and the residuals are generated by comparing the outputs of the dynamical model with the real state values in the condition of thruster fault. Fault detection rules are distilled by residual analysis to execute thruster fault diagnosis. The results of simulation prove the effectiveness.

  6. Residual Generation Methods for Fault Diagnosis with Automotive Applications

    Svärd, Carl

    2009-01-01

    The problem of fault diagnosis consists of detecting and isolating faults present in a system. As technical systems become more and more complex and the demands for safety, reliability and environmental friendliness are rising, fault diagnosis is becoming increasingly important. One example is automotive systems, where fault diagnosis is a necessity for low emissions, high safety, high vehicle uptime, and efficient repair and maintenance. One approach to fault diagnosis, providing potentially...

  7. Sequential Testing Algorithms for Multiple Fault Diagnosis

    Shakeri, Mojdeh; Raghavan, Vijaya; Pattipati, Krishna R.; Patterson-Hine, Ann

    1997-01-01

    In this paper, we consider the problem of constructing optimal and near-optimal test sequencing algorithms for multiple fault diagnosis. The computational complexity of solving the optimal multiple-fault isolation problem is super-exponential, that is, it is much more difficult than the single-fault isolation problem, which, by itself, is NP-hard. By employing concepts from information theory and AND/OR graph search, we present several test sequencing algorithms for the multiple fault isolation problem. These algorithms provide a trade-off between the degree of suboptimality and computational complexity. Furthermore, we present novel diagnostic strategies that generate a diagnostic directed graph (digraph), instead of a diagnostic tree, for multiple fault diagnosis. Using this approach, the storage complexity of the overall diagnostic strategy reduces substantially. The algorithms developed herein have been successfully applied to several real-world systems. Computational results indicate that the size of a multiple fault strategy is strictly related to the structure of the system.

  8. NETWORK FAULT DIAGNOSIS USING DATA MINING CLASSIFIERS

    Eleni Rozaki

    2015-04-01

    Full Text Available Mobile networks are under more pressure than ever before because of the increasing number of smartphone users and the number of people relying on mobile data networks. With larger numbers of users, the issue of service quality has become more important for network operators. Identifying faults in mobile networks that reduce the quality of service must be found within minutes so that problems can be addressed and networks returned to optimised performance. In this paper, a method of automated fault diagnosis is presented using decision trees, rules and Bayesian classifiers for visualization of network faults. Using data mining techniques the model classifies optimisation criteria based on the key performance indicators metrics to identify network faults supporting the most efficient optimisation decisions. The goal is to help wireless providers to localize the key performance indicator alarms and determine which Quality of Service factors should be addressed first and at which locations.

  9. Fault Diagnosis in Induction Machines for Internal Fault Identification Scheme

    K. Vinoth Kumar; Dr. S.Suresh Kumar; Ashish Sam Geo; Jomon Yohannan; Toji Thomas; Sreekanth P.G

    2012-01-01

    In this paper, a mathematical model of the three-phase induction motor drives in abc reference frame is described. A computer simulation of the motor drive is provided which utilized Lab VIEW software. This simulation can be conveniently used to study the level of the ‘Fault Tolerant System’ parameters like current, voltage, torque, speed and also simulate the three phase Induction Motor for diagnosis of the short circuit and normal case using Laboratory virtual Instrumentation Engineering Wo...

  10. Bearing fault diagnosis based on spectrum images of vibration signals

    Bearing fault diagnosis has been a challenge in the monitoring activities of rotating machinery, and it’s receiving more and more attention. The conventional fault diagnosis methods usually extract features from the waveforms or spectrums of vibration signals in order to correctly classify faults. In this paper, a novel feature in the form of images is presented, namely analysis of the spectrum images of vibration signals. The spectrum images are simply obtained by doing fast Fourier transformation. Such images are processed with two-dimensional principal component analysis (2DPCA) to reduce the dimensions, and then a minimum distance method is applied to classify the faults of bearings. The effectiveness of the proposed method is verified with experimental data. (paper)

  11. Soft Fault Diagnosis for Analog Circuits Based on Slope Fault Feature and BP Neural Networks

    HU Mei; WANG Hong; HU Geng; YANG Shiyuan

    2007-01-01

    Fault diagnosis is very important for development and maintenance of safe and reliable electronic circuits and systems. This paper describes an approach of soft fault diagnosis for analog circuits based on slope fault feature and back propagation neural networks (BPNN). The reported approach uses the voltage relation function between two nodes as fault features; and for linear analog circuits, the voltage relation function is a linear function, thus the slope is invariant as fault feature. Therefore, a unified fault feature for both hard fault (open or short fault) and soft fault (parametric fault) is extracted. Unlike other NN-based diagnosis methods which utilize node voltages or frequency response as fault features, the reported BPNN is trained by the extracted feature vectors, the slope features are calculated by just simulating once for each component, and the trained BPNN can achieve all the soft faults diagnosis of the component. Experiments show that our approach is promising.

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

    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

  13. Study on Knowledge -based Intelligent Fault Diagnosis of Hydraulic System

    Xuexia Liu

    2012-12-01

    Full Text Available A general framework of hydraulic fault diagnosis system was studied. It consisted of equipment knowledge bases, real-time databases, fusion reasoning module, knowledge acquisition module and so on. A tree-structure model of fault knowledge was established. Fault nodes knowledge was encapsulated by object-oriented technique. Complete knowledge bases were made including fault bases and diagnosis bases. It could describe the fault positions, the structure of fault, cause-symptom relationships, diagnosis principles and other knowledge. Taking the fault of left and right lifting oil cylinder out of sync for example, the diagnostic results show that the methods were effective.

  14. Combination of Fault Tree and Neural Networks in Excavator Diagnosis

    Li Guoping

    2013-04-01

    Full Text Available By using the theory of artificial intelligence fault diagnosis of hydraulic excavator of several basic problems are discussed in this paper, the artificial intelligence neural network model is established for the fault diagnosis of hydraulic system; the combined application of fault diagnosis analysis (FTA and artificial neural network is evaluated. In view of the hydraulic excavator failure symptom of dispersion and fuzziness, the fault diagnosis method was presented based on the fault tree and fuzzy neural network. On the basis of analysis of the hydraulic excavator system works, the fault tree model of hydraulic excavator was built by using fault diagnosis tree. And then, utilizing the example of hydraulic excavator fault diagnosis, the method of building neural network, obtaining training samples and neural network learning in the process of intelligent fault diagnosis are expounded. And the status monitoring data of hydraulic excavator was used as the sample data source. Using fuzzy logic methods the samples were blurred. The fault diagnosis of hydraulic excavator was achieved with BP neural network. The experimental result demonstrated that the information of sign failure was fully used through the algorithm. The algorithm was feasible and effective to fault diagnosis of hydraulic excavator. A new diagnosis method was proposed for fault diagnosis of other similar device.

  15. Fault Diagnosis in Process Control Valve Using Artificial Neural Network

    K. Prabakaran; T. Uma Mageshwari; Prakash, D.; A. Suguna

    2013-01-01

    As modern process industries become more complex, the importance to detect and identify the faulty operation of pneumatic process control valves is increasing rapidly. The prior detection of faults leads to avoiding the system shutdown, breakdown, raw material damage and etc. The proposed approach for fault diagnosis comprises of two processes such as fault detection and fault isolation. In fault diagnosis, the difference between the system outputs and model outputs called as residuals are us...

  16. Causal digraph reasoning for fault diagnosis in paper making applications

    Cheng, Hui

    2009-01-01

    Fault detection and diagnosis systems are required by the process industries because of tightening global competition and the increasing complexity of the processes, which results in the difficulty for operators to perform the diagnosis tasks. Academic research in the field of fault diagnosis has expanded rapidly to meet this demand and successful applications with economic benefits have been reported extensively. As a fault diagnosis method, the causal directed graph method has proved to hav...

  17. Diagnosis and Fault-tolerant Control, 3rd Edition

    Blanke, Mogens; Kinnaert, Michel; Lunze, Jan; Staroswiecki, Marcel

    The book presents effective model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault through the process, to test the fault detectability and to find the redundancies in the process...

  18. Study of fault diagnosis software design for complex system based on fault tree

    Complex systems always have high-level reliability and safety requirements, and same does their diagnosis work. As a great deal of fault tree models have been acquired during the design and operation phases, a fault diagnosis method which combines fault tree analysis with knowledge-based technology has been proposed. The prototype of fault diagnosis software has been realized and applied to mobile LIDAR system. (authors)

  19. Rectifier Fault Diagnosis and Fault Tolerance of a Doubly Fed Brushless Starter Generator

    Liwei Shi; Zhou Bo

    2015-01-01

    This paper presents a rectifier fault diagnosis method with wavelet packet analysis to improve the fault tolerant four-phase doubly fed brushless starter generator (DFBLSG) system reliability. The system components and fault tolerant principle of the high reliable DFBLSG are given. And the common fault of the rectifier is analyzed. The process of wavelet packet transforms fault detection/identification algorithm is introduced in detail. The fault tolerant performance and output voltage experi...

  20. Construction of classification function in Diesel engine fault diagnosis

    Using multi statistical analysis of the pattern recognition, we construct a classification function in the study of diesel engine fault diagnosis. The technique reported in this paper makes it precise and easy to diagnose the diesel engine fault

  1. Robust fault diagnosis for a class of nonlinear systems

    Zhanshan WANG; Huaguang ZHANG

    2006-01-01

    Robust fault diagnosis based on adaptive observer is studied for a class of nonlinear systems up to output injection. Adaptive fault updating laws are designed to guarantee the stability of the diagnosis system. The upper bounds of the state estimation error and fault estimation error of the adaptive observer are given respectively and the effects of parameter in the adaptive updating laws on fault estimation accuracy are also discussed. Simulation example demonstrates the effectiveness of the proposed methods and the analysis results.

  2. Similarity Matching Techniques for Fault Diagnosis in Automotive Infotainment Electronics

    Kabir, Mashud

    2009-01-01

    Fault diagnosis has become a very important area of research during the last decade due to the advancement of mechanical and electrical systems in industries. The automobile is a crucial field where fault diagnosis is given a special attention. Due to the increasing complexity and newly added features in vehicles, a comprehensive study has to be performed in order to achieve an appropriate diagnosis model. A diagnosis system is capable of identifying the faults of a system by investigating the observable effects (or symptoms). The system categorizes the fault into a diagnosis class and identifies a probable cause based on the supplied fault symptoms. Fault categorization and identification are done using similarity matching techniques. The development of diagnosis classes is done by making use of previous experience, knowledge or information within an application area. The necessary information used may come from several sources of knowledge, such as from system analysis. In this paper similarity matching tec...

  3. Intelligent Fault Diagnosis in Lead-zinc Smelting Process

    Wei-Hua Gui; Chun-Hua Yang; Jing Teng

    2007-01-01

    According to the fault characteristic of the imperial smelting process (ISP), a novel intelligent integrated fault diagnostic system is developed. In the system fuzzy neural networks are utilized to extract fault symptom and expert system is employed for effective fault diagnosis of the process. Furthermore, fuzzy abductive inference is introduced to diagnose multiple faults. Feasibility of the proposed system is demonstrated through a pilot plant case study.

  4. Structural Analysis Extended with Active Fault Isolation - Methods and Algorithms

    Gelso, Esteban R.; Blanke, Mogens

    2009-01-01

    Isolability of faults is a key issue in fault diagnosis whether the aim is maintenance or active fault-tolerant control. It is often encountered that while faults are detectable, they are only group-wise isolable from a usual diagnostic point of view. However, active injection of test signals on...... system inputs can considerably enhance fault isolability. This paper investigates this possibility of active fault isolation from a structural point of view. While such extension of the structural analysis approach was suggested earlier, algorithms and case studies were needed to explore this theory. The...

  5. Transformer fault diagnosis using continuous sparse autoencoder

    Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou

    2016-01-01

    This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity ...

  6. Active Fault Isolation in MIMO Systems

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    isolation is based directly on the input/output s ignals applied for the fault detection. It is guaranteed that the fault group includes the fault that had occurred in the system. The second step is individual fault isolation in the fault group . Both types of isolation are obtained by applying dedicated......Active fault isolation of parametric faults in closed-loop MIMO system s are considered in this paper. The fault isolation consists of two steps. T he first step is group- wise fault isolation. Here, a group of faults is isolated from other pos sible faults in the system. The group-wise fault...

  7. Diagnosis and Fault-tolerant Control, 2nd edition

    Blanke, Mogens; Kinnaert, Michel; Lunze, Jan; Starosweicki, Marcel

    Fault-tolerant control aims at a graceful degradation of the behaviour of automated systems in case of faults. It satisfies the industrial demand for enhanced availability and safety, in contrast to traditional reactions to faults that bring about sudden shutdowns and loss of availability. The book...... presents effective model-based analysis and design methods for fault diagnosis and fault-tolerant control. Architectural and structural models are used to analyse the propagation of the fault throught the process, to test the fault detectability and to find the redundancies in the process that can be used...... to ensure fault tolerance. Design methods for diagnostic systems and fault-tolerant controllers are presented for processes that are described by analytical models, by discrete-event models or that can be dealt with as quantised systems. Five case studies on pilot processes show the applicability of...

  8. Graphical User Interface Aided Online Fault Diagnosis of Electric Motor - DC motor case study

    POSTALCIOGLU OZGEN, S.

    2009-01-01

    This paper contains graphical user interface (GUI) aided online fault diagnosis for DC motor. The aim of the research is to prevent system faults. Online fault diagnosis has been studied. Design of fault diagnosis has two main levels: Level 1 comprises a traditional control loop; Level 2 contains knowledge based fault diagnosis. Fault diagnosis technique contains feature extraction module, feature cluster module and fault decision module. Wavelet analysis has been used for the feature extract...

  9. Fault Diagnosis of Nonlinear Systems Using Structured Augmented State Models

    Jochen Aβfalg; Frank Allg(o)wer

    2007-01-01

    This paper presents an internal model approach for modeling and diagnostic functionality design for nonlinear systems operating subject to single- and multiple-faults. We therefore provide the framework of structured augmented state models. Fault characteristics are considered to be generated by dynamical exosystems that are switched via equality constraints to overcome the augmented state observability limiting the number of diagnosable faults. Based on the proposed model, the fault diagnosis problem is specified as an optimal hybrid augmented state estimation problem. Sub-optimal solutions are motivated and exemplified for the fault diagnosis of the well-known three-tank benchmark. As the considered class of fault diagnosis problems is large, the suggested approach is not only of theoretical interest but also of high practical relevance.

  10. Bond graph model-based fault diagnosis of hybrid systems

    Borutzky, Wolfgang

    2015-01-01

    This book presents a bond graph model-based approach to fault diagnosis in mechatronic systems appropriately represented by a hybrid model. The book begins by giving a survey of the fundamentals of fault diagnosis and failure prognosis, then recalls state-of-art developments referring to latest publications, and goes on to discuss various bond graph representations of hybrid system models, equations formulation for switched systems, and simulation of their dynamic behavior. The structured text: • focuses on bond graph model-based fault detection and isolation in hybrid systems; • addresses isolation of multiple parametric faults in hybrid systems; • considers system mode identification; • provides a number of elaborated case studies that consider fault scenarios for switched power electronic systems commonly used in a variety of applications; and • indicates that bond graph modelling can also be used for failure prognosis. In order to facilitate the understanding of fault diagnosis and the presented...

  11. Fault Diagnosis in Process Control Valve Using Artificial Neural Network

    K. Prabakaran

    2013-05-01

    Full Text Available As modern process industries become more complex, the importance to detect and identify the faulty operation of pneumatic process control valves is increasing rapidly. The prior detection of faults leads to avoiding the system shutdown, breakdown, raw material damage and etc. The proposed approach for fault diagnosis comprises of two processes such as fault detection and fault isolation. In fault diagnosis, the difference between the system outputs and model outputs called as residuals are used to detect and isolate the faults. But in the control valve it is not an easy process due to inherent nonlinearity. The particular values of five measurable quantities from the valve are depend on the commonly occurring faults such as Incorrect supply pressure, Diaphragm leakage and Actuator vent blockage. The correlations between these parameters from the fault values for each operating condition are learned by a multilayer BP Neural Network. The parameter consideration is done through the committee of Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS. The simulation results using MATLab prove that BP neural network has the ability to detect and identify various magnitudes of the faults and can isolate multiple faults. In addition, it is observed that the network has the ability to estimate fault levels not seen by the network during training.

  12. Intelligent System for Fault Diagnosis in Automotive Applications

    Kabir, Mashud

    2008-01-01

    This work presents an intelligent system for fault diagnosis in automotive applications. The system is proposed to handle faults in a running car as well as in a car which is in the process of being developed. The main goal of this work is to use the vast knowledge previously acquired by system experts, to visualize, localize and fix a fault in any of the life phases of a car. The existing techniques and systems which are in use for fault diagnosis are investigated. There is no system which c...

  13. Similarity Matching Techniques For Fault Diagnosis In Automotive Infotainment Electronics

    Mashud Kabir

    2009-08-01

    Full Text Available Fault diagnosis has become a very important area of research during the last decade due to the advancement of mechanical and electrical systems in industries. The automobile is a crucial field where fault diagnosis is given a special attention. Due to the increasing complexity and newly added features in vehicles, a comprehensive study has to be performed in order to achieve an appropriate diagnosis model. A diagnosis system is capable of identifying the faults of a system by investigating the observable effects (or symptoms. The system categorizes the fault into a diagnosis class and identifies a probable cause based on the supplied fault symptoms. Fault categorization and identification are done using similarity matching techniques. The development of diagnosis classes is done by making use of previous experience, knowledge or information within an application area. The necessary information used may come from several sources of knowledge, such as from system analysis. In this paper similarity matching techniques for fault diagnosis in automotive infotainment applications are discussed.

  14. Application of General fractal Dimension to Coupling Fault Diagnosis

    2002-01-01

    This paper presents the coucept of general and sensitive dimension, and also proposes the calculation formula of the general dimension least squares method. By calculating and analyzing the power spectrum and general dimension from the fault sample, the relationship is achieved between sample status and the general dimension from vibration signals function of general dimension is proposed, and calculations are carried out for a monitor signal and samples signal. The diagnosis method based on fractal theory is effective through the concrete examples of the steam-electric generating set fault diagnosis, and the correlation coefficient of general dimension between a monitor signal and samples signal can improve the accuracy for fault diagnosis.

  15. Fault Diagnosis and Reliability Analysis Using Fuzzy Logic Method

    Miao Zhinong; Xu Yang; Zhao Xiangyu

    2006-01-01

    A new fuzzy logic fault diagnosis method is proposed. In this method, fuzzy equations are employed to estimate the component state of a system based on the measured system performance and the relationship between component state and system performance which is called as "performance-parameter" knowledge base and constructed by expert. Compared with the traditional fault diagnosis method, this fuzzy logic method can use humans intuitive knowledge and dose not need a precise mapping between system performance and component state. Simulation proves its effectiveness in fault diagnosis. Then, the reliability analysis is performed based on the fuzzy logic method.

  16. Data-driven design of fault diagnosis and fault-tolerant control systems

    Ding, Steven X

    2014-01-01

    Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems presents basic statistical process monitoring, fault diagnosis, and control methods, and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering to the needs of dynamic industrial processes. With ever increasing demands for reliability, availability and safety in technical processes and assets, process monitoring and fault-tolerance have become important issues surrounding the design of automatic control systems. This text shows the reader how, thanks to the rapid development of information technology, key techniques of data-driven and statistical process monitoring and control can now become widely used in industrial practice to address these issues. To allow for self-contained study and facilitate implementation in real applications, important mathematical and control theoretical knowledge and tools are included in this book. Major schemes are presented in algorithm form and...

  17. Model-based fault diagnosis in PEM fuel cell systems

    Escobet, T.; de Lira, S.; Puig, V.; Quevedo, J. [Automatic Control Department (ESAII), Universitat Politecnica de Catalunya (UPC), Rambla Sant Nebridi 10, 08222 Terrassa (Spain); Feroldi, D.; Riera, J.; Serra, M. [Institut de Robotica i Informatica Industrial (IRI), Consejo Superior de Investigaciones Cientificas (CSIC), Universitat Politecnica de Catalunya (UPC) Parc Tecnologic de Barcelona, Edifici U, Carrer Llorens i Artigas, 4-6, Planta 2, 08028 Barcelona (Spain)

    2009-07-01

    In this work, a model-based fault diagnosis methodology for PEM fuel cell systems is presented. The methodology is based on computing residuals, indicators that are obtained comparing measured inputs and outputs with analytical relationships, which are obtained by system modelling. The innovation of this methodology is based on the characterization of the relative residual fault sensitivity. To illustrate the results, a non-linear fuel cell simulator proposed in the literature is used, with modifications, to include a set of fault scenarios proposed in this work. Finally, it is presented the diagnosis results corresponding to these fault scenarios. It is remarkable that with this methodology it is possible to diagnose and isolate all the faults in the proposed set in contrast with other well known methodologies which use the binary signature matrix of analytical residuals and faults. (author)

  18. Failure characteristics analysis and fault diagnosis for liquid rocket engines

    Zhang, Wei

    2016-01-01

    This book concentrates on the subject of health monitoring technology of Liquid Rocket Engine (LRE), including its failure analysis, fault diagnosis and fault prediction. Since no similar issue has been published, the failure pattern and mechanism analysis of the LRE from the system stage are of particular interest to the readers. Furthermore, application cases used to validate the efficacy of the fault diagnosis and prediction methods of the LRE are different from the others. The readers can learn the system stage modeling, analyzing and testing methods of the LRE system as well as corresponding fault diagnosis and prediction methods. This book will benefit researchers and students who are pursuing aerospace technology, fault detection, diagnostics and corresponding applications.

  19. Fault diagnosis of nuclear equipment based on artificial immune system

    As the nuclear equipment is complicate and special, this paper put forward a novel fault diagnosis method for nuclear equipment based on artificial immune system and the principle to model with negative-selection algorithm and further identify the fault with clone-variation algorithm. Features are extracted with the signal that was sampled in a rotary machinery, then the result is input to the AIS model. Simulation result shows that the model can identify each fault type successfully. (authors)

  20. Diagnosis of airspeed measurement faults for unmanned aerial vehicles

    Hansen, Søren; Blanke, Mogens

    2014-01-01

    Airspeed sensor faults are common causes for incidents with unmanned aerial vehicles with pitot tube clogging or icing being the most common causes. Timely diagnosis of such faults or other artifacts in signals from airspeed sensing systems could potentially prevent crashes. This paper employs pa...

  1. Fuzzy classifier for fault diagnosis in analog electronic circuits.

    Kumar, Ashwani; Singh, A P

    2013-11-01

    Many studies have presented different approaches for the fault diagnosis with fault models having ± 50% variation in the component values in analog electronic circuits. There is still a need of the approaches which provide the fault diagnosis with the variation in the component value below ± 50%. A new single and multiple fault diagnosis technique for soft faults in analog electronic circuit using fuzzy classifier has been proposed in this paper. This technique uses the simulation before test (SBT) approach by analyzing the frequency response of the analog circuit under faulty and fault free conditions. Three signature parameters peak gain, frequency and phase associated with peak gain, of the frequency response of the analog circuit are observed and extracted such that they give unique values for faulty and fault free configuration of the circuit. The single and double fault models with the component variations from ± 10% to ± 50% are considered. The fuzzy classifier along the classification of faults gives the estimated component value under faulty and faultfree conditions. The proposed method is validated using simulated data and the real time data for a benchmark analog circuit. The comparative analysis is also presented for both the validations. PMID:23849881

  2. APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN PROCESS FAULT DIAGNOSIS

    M.A. HUSSAIN

    2007-12-01

    Full Text Available Chemical processes are systems that include complicated network of material, energy and process flow. As time passes, the performance of chemical process gradually degrades due to the deterioration of process equipments and components. The early detection and diagnosis of faults in chemical processes is very important both from the viewpoint of plant safety as well as reduced manufacturing costs. The conventional way used in fault detection and diagnosis is through the use of models of the process, which is not easy to be achieved in many cases. In recent years, an artificial intelligence technique such as neural network has been successfully used for pattern recognition and as such it can be suitable for use in fault diagnosis of processes [1]. The application of neural network methods in process fault detection and diagnosis is demonstrated in this work in two case studies using simulated chemical plant systems. Both systems were successfully diagnosed of the faults introduced in them. The neural networks were able to generalise to successfully diagnosed fault combinations it was not explicitly trained upon. Thus, neural network can be fully applied in industries as it has shown several advantages over the conventional way in fault diagnosis.

  3. Fault diagnosis system for the Outokumpu flash smelting process

    Fault diagnosis systems have attracted the growing interest of researchers in a number of engineering areas. The number of applications has increased and successful results are reported widely. This paper presents the results of principal component analysis carried out on the Outokumpu flash smelting process the waste heat boiler being analysed in more detail. The PCA results are evaluated and the configuration of a fault diagnosis system is proposed. (author)

  4. APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN PROCESS FAULT DIAGNOSIS

    M A Hussain; C.R. CHE HASSAN; K. S. LOH

    2007-01-01

    Chemical processes are systems that include complicated network of material, energy and process flow. As time passes, the performance of chemical process gradually degrades due to the deterioration of process equipments and components. The early detection and diagnosis of faults in chemical processes is very important both from the viewpoint of plant safety as well as reduced manufacturing costs. The conventional way used in fault detection and diagnosis is through the use of models of the pr...

  5. Application of fault tree analysis to fuel cell diagnosis

    Yousfi Steiner, N.; Mocoteguy, P. [European Institute for Energy Research (EIFER), Karlsruhe (Germany); Hissel, D. [FEMTO-ST/ENISYS/FC LAB, UMR CNRS 6174, University of Franche-Comte, Belfort (France); Candusso, D. [IFSTTAR/FC LAB, Institute of Science and Technology for Transport, Development and Networks, Belfort (France); Marra, D.; Pianese, C.; Sorrentino, M. [Department of Industrial Engineering, University of Salerno, Fisciano (Italy)

    2012-04-15

    Reliability and lifetime are common issues for the development and commercialization of fuel cells technologies'. As a consequence, their improvement is a major challenge and the last decade has experienced a growing interest in activities that aims at understanding the degradation mechanisms and at developing fuel cell systems diagnosis tools. Fault Tree Analysis (FTA) is one of the deductive tools that allow ''linking'' an undesired state to a combination of lower-level events via a ''top-down'' approach which is mainly used in safety and reliability engineering. The objective of this paper is to give an overview of the use and the contribution of FTA to both SOFC and PEFC diagnosis. (Copyright copyright 2012 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

  6. Nonlinear fault diagnosis method based on kernel principal component analysis

    Yan Weiwu; Zhang Chunkai; Shao Huihe

    2005-01-01

    To ensure the system run under working order, detection and diagnosis of faults play an important role in industrial process. This paper proposed a nonlinear fault diagnosis method based on kernel principal component analysis (KPCA). In proposed method, using essential information of nonlinear system extracted by KPCA, we constructed KPCA model of nonlinear system under normal working condition. Then new data were projected onto the KPCA model. When new data are incompatible with the KPCA model, it can be concluded that the nonlinear system isout of normal working condition. Proposed method was applied to fault diagnosison rolling bearings. Simulation results show proposed method provides an effective method for fault detection and diagnosis of nonlinear system.

  7. Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

    Weigang Wen

    2016-01-01

    Full Text Available The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability.

  8. Fault detection and diagnosis of diesel engine valve trains

    Flett, Justin; Bone, Gary M.

    2016-05-01

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

  9. Electric machines modeling, condition monitoring, and fault diagnosis

    Toliyat, Hamid A; Choi, Seungdeog; Meshgin-Kelk, Homayoun

    2012-01-01

    With countless electric motors being used in daily life, in everything from transportation and medical treatment to military operation and communication, unexpected failures can lead to the loss of valuable human life or a costly standstill in industry. To prevent this, it is important to precisely detect or continuously monitor the working condition of a motor. Electric Machines: Modeling, Condition Monitoring, and Fault Diagnosis reviews diagnosis technologies and provides an application guide for readers who want to research, develop, and implement a more effective fault diagnosis and condi

  10. Fault diagnosis of nuclear facilities based on Hidden Markov Model

    Due to the complex structure of nuclear facilities in a high irradiation environment, people are hard to approach it. In view of these situations, a fault diagnosis method based on HMM (Hidden Markov Model) of capturing the audio signal while the nuclear facilities are operating is proposed. With the strong modeling ability, HMM can be applied to analyzing such as audio signal non-stationary time signal. By using this method, the original mechanical structures of nuclear facilities are not destroyed. The proposed sensors are needed as few as possible by the whole diagnosis system and which has a simple structure, low cost structure. The fault diagnosis rate is high. (authors)

  11. Nuclear Power Plants Fault Diagnosis Method Based on Data Fusion

    The data fusion is a method suit for complex system fault diagnosis such as nuclear power plants, which is multisource information processing technology. This paper uses data fusion information hierarchical thinking and divides nuclear power plants fault diagnosis into three levels. Data level adopts data mining method to handle data and reduction attributes. Feature level uses three parallel neural networks to deal with attributes of data level reduction and the outputs of three networks are as the basic probability assignment of Dempster-Shafer (D-S) evidence theory. The improved D-S evidence theory synthesizes the outputs of neural networks in decision level, which conquer the traditional D-S evidence theory limitation which can't dispose conflict information. The diagnosis method was tested using correlation data of literature. The test results indicate that the data fusion diagnosis system can diagnose nuclear power plants faults accurately and the method has application value. (authors)

  12. Multisensor fusion for induction motor aging analysis and fault diagnosis

    Erbay, Ali Seyfettin

    Induction motors are the most commonly used electrical drives, ranging in power from fractional horsepower to several thousand horsepowers. Several studies have been conducted to identify the cause of failure of induction motors in industrial applications. Recent activities indicate a focus towards building intelligence into the motors, so that a continuous on-line fault diagnosis and prognosis may be performed. The purpose of this research and development was to perform aging studies of three-phase, squirrel-cage induction motors; establish a database of mechanical, electrical and thermal measurements from load testing of the motors; develop a sensor-fusion method for on-line motor diagnosis; and use the accelerated aging models to extrapolate to the normal aging regimes. A new laboratory was established at The University of Tennessee to meet the goals of the project. The accelerated aging and motor performance tests constitute a unique database, containing information about the trend characteristics of measured signatures as a function of motor faults. The various measurements facilitate enhanced fault diagnosis of motors and may be effectively utilized to increase the reliability of decision making and for the development of life prediction techniques. One of these signatures is the use of Multi-Resolution Analysis (MRA) using wavelets. Using MRA in trending different frequency bands has revealed that higher frequencies show a characteristic increase when the condition of a bearing is in question. This study effectively showed that the use of MRA in vibration signatures can identify a thermal degradation or degradation via electrical charge of the bearing, whereas other failure mechanisms, such as winding insulation failure, do not exhibit such characteristics. A motor diagnostic system, called the Intelligent Motor Monitoring System (IMMS) was developed in this research. The IMMS integrated the various mechanical, electrical and thermal signatures, and

  13. Fault diagnosis and prognostic of solid oxide fuel cells

    Wu, XiaoJuan; Ye, Qianwen

    2016-07-01

    One of the major hurdles for solid oxide fuel cell (SOFC) commercialization is poor long-term performance and durability. Accurate fault diagnostic and prognostic technologies are two important tools to improve SOFC durability. In literature, plenty of diagnosis techniques for SOFC systems have been successfully designed. However, no literature studies SOFC fault prognosis approaches. In this paper a unified fault diagnosis and prognosis strategy is presented to identify faults (anode poisoning, cathode humidification or normal) and predict the remaining useful life for SOFC systems. Using a squares support vector machine (LS-SVM) classifier, a diagnosis model is built to identify SOFC different types of faults. After fault detection, two hidden semi-Mark models (HSMMs) are respectively employed to estimate SOFC remaining useful life in the case of anode poisoning and cathode humidification. The simulation results show that the fault recognition rates with the LS-SVM model are at best 97%, and the predicted error of the remaining useful life is within ±20%.

  14. A fault diagnosis prototype for a bioreactor for bioinsecticide production

    The objective of this work is to develop an algorithm for fault diagnosis in a process of animal cell cultivation, for bioinsecticide production. Generally, these processes are batch processes. It is a fact that the diagnosis for a batch process involves a division of the process evolution (time horizon) into partial processes, which are defined as pseudocontinuous blocks. Therefore, a PCB represents the evolution of the system in a time interval where it has a qualitative behavior similar to a continuous one. Thus, each PCB, in which the process is divided, can be handled in a conventional way (like continuous processes). The process model, for each PCB, is a Signed Directed Graph (SDG). To achieve generality and to allow the computational implementation, the modular approach was used in the synthesis of the bioreactor digraph. After that, the SDGs were used to carry out qualitative simulations of faults. The achieved results are the fault patterns. A special fault symptom dictionary - SM - has been adopted as data base organization for fault patterns storage. An effective algorithm is presented for the searching process of fault patterns. The system studied, as a particular application, is a bioreactor for cell cultivation for bioinsecticide production. During this work, we concentrate on the SDG construction, and 3btaining real fault patterns by the elimination of spurious patterns. The algorithm has proved to be effective in both senses, resolution and accuracy, to diagnose different kinds of simulated faults

  15. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster–Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  16. Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory.

    Yuan, Kaijuan; Xiao, Fuyuan; Fei, Liguo; Kang, Bingyi; Deng, Yong

    2016-01-01

    Sensor data fusion plays an important role in fault diagnosis. Dempster-Shafer (D-R) evidence theory is widely used in fault diagnosis, since it is efficient to combine evidence from different sensors. However, under the situation where the evidence highly conflicts, it may obtain a counterintuitive result. To address the issue, a new method is proposed in this paper. Not only the statistic sensor reliability, but also the dynamic sensor reliability are taken into consideration. The evidence distance function and the belief entropy are combined to obtain the dynamic reliability of each sensor report. A weighted averaging method is adopted to modify the conflict evidence by assigning different weights to evidence according to sensor reliability. The proposed method has better performance in conflict management and fault diagnosis due to the fact that the information volume of each sensor report is taken into consideration. An application in fault diagnosis based on sensor fusion is illustrated to show the efficiency of the proposed method. The results show that the proposed method improves the accuracy of fault diagnosis from 81.19% to 89.48% compared to the existing methods. PMID:26797611

  17. Faults and Diagnosis Systems in Power Converters

    Lee, Kyo-Beum; Choi, Uimin

    2014-01-01

    efforts have been put into making these systems better in terms of reliability in order to achieve high power source availability, reduce the cost of energy and also increase the reliability of overall systems. Among the components used in power converters, a power device and a capacitor fault occurs most...

  18. Robust Fault Diagnosis for Systems with Electronic Induced Delays

    Fonod, Robert; Henry, David; Bornschlegl, Eric; Charbonnel, Catherine

    2012-01-01

    A problem of robust fault diagnosis of digital controlled continuous-time systems with uncertain time-varying input delay is studied in this paper. Two residual-based fault detection and isolation (FDI) schemes are proposed that are robust in terms of time-varying delays induced by the electronic devices and disturbances. The idea of both proposed methods is to transform the uncertainty caused by delays into unknown inputs and decouple them by means of eigenstructure assignment (EA) technique...

  19. Fault Diagnosis of Batch Reactor Using Machine Learning Methods

    2014-01-01

    Fault diagnosis of a batch reactor gives the early detection of fault and minimizes the risk of thermal runaway. It provides superior performance and helps to improve safety and consistency. It has become more vital in this technical era. In this paper, support vector machine (SVM) is used to estimate the heat release (Qr) of the batch reactor both normal and faulty conditions. The signature of the residual, which is obtained from the difference between nominal and estimated faulty Qr values,...

  20. Fault diagnosis of monoblock centrifugal pump using SVM

    V. Muralidharan

    2014-09-01

    Full Text Available Monoblock centrifugal pumps are employed in variety of critical engineering applications. Continuous monitoring of such machine component becomes essential in order to reduce the unnecessary break downs. At the outset, vibration based approaches are widely used to carry out the condition monitoring tasks. Particularly fuzzy logic, support vector machine (SVM and artificial neural networks were employed for continuous monitoring and fault diagnosis. In the present study, the application of SVM algorithm in the field of fault diagnosis and condition monitoring is discussed. The continuous wavelet transforms were calculated for different families and at different levels. The computed transformation coefficients form the feature set for the classification of good and faulty conditions of the components of centrifugal pump. The classification accuracies of different continuous wavelet families at different levels were calculated and compared to find the best wavelet for the fault diagnosis of the monoblock centrifugal pump.

  1. Research into a distributed fault diagnosis system and its application

    Qian, Suxiang; Jiao, Weidong; Lou, Yongjian; Shen, Xiaomei

    2005-12-01

    CORBA (Common Object Request Broker Architecture) is a solution to distributed computing methods over heterogeneity systems, which establishes a communication protocol between distributed objects. It takes great emphasis on realizing the interoperation between distributed objects. However, only after developing some application approaches and some practical technology in monitoring and diagnosis, can the customers share the monitoring and diagnosis information, so that the purpose of realizing remote multi-expert cooperation diagnosis online can be achieved. This paper aims at building an open fault monitoring and diagnosis platform combining CORBA, Web and agent. Heterogeneity diagnosis object interoperate in independent thread through the CORBA (soft-bus), realizing sharing resource and multi-expert cooperation diagnosis online, solving the disadvantage such as lack of diagnosis knowledge, oneness of diagnosis technique and imperfectness of analysis function, so that more complicated and further diagnosis can be carried on. Take high-speed centrifugal air compressor set for example, we demonstrate a distributed diagnosis based on CORBA. It proves that we can find out more efficient approaches to settle the problems such as real-time monitoring and diagnosis on the net and the break-up of complicated tasks, inosculating CORBA, Web technique and agent frame model to carry on complemental research. In this system, Multi-diagnosis Intelligent Agent helps improve diagnosis efficiency. Besides, this system offers an open circumstances, which is easy for the diagnosis objects to upgrade and for new diagnosis server objects to join in.

  2. Robust fault diagnosis for a class of nonlinear systems with time delay

    2007-01-01

    Robust fault diagnosis problems based on adaptive observer technique are studied for a class of time delayed nonlinear system with external disturbance. Adaptive fault updating laws were designed to estimate the fault and to guarantee the stability of the diagnosis system. The effects of adjusting parameters in adaptive fault updating laws on the fault estimation accuracy were analyzed. For a designed fault diagnosis system, the super bounds of the state estimation error and fault estimation error of the adaptive observer were discussed, which further showed how the parameters in the adaptive fault updating laws influenced the fault estimation accuracy.Simulation example demonstrates the effectiveness of the proposed methods and the analysis results.

  3. Analog fault diagnosis by inverse problem technique

    Ahmed, Rania F.

    2011-12-01

    A novel algorithm for detecting soft faults in linear analog circuits based on the inverse problem concept is proposed. The proposed approach utilizes optimization techniques with the aid of sensitivity analysis. The main contribution of this work is to apply the inverse problem technique to estimate the actual parameter values of the tested circuit and so, to detect and diagnose single fault in analog circuits. The validation of the algorithm is illustrated through applying it to Sallen-Key second order band pass filter and the results show that the detecting percentage efficiency was 100% and also, the maximum error percentage of estimating the parameter values is 0.7%. This technique can be applied to any other linear circuit and it also can be extended to be applied to non-linear circuits. © 2011 IEEE.

  4. NETWORK FAULT DIAGNOSIS USING DATA MINING CLASSIFIERS

    Eleni Rozaki

    2015-01-01

    Mobile networks are under more pressure than ever before because of the increasing number of smartphone users and the number of people relying on mobile data networks. With larger numbers of users, the issue of service quality has become more important for network operators. Identifying faults in mobile networks that reduce the quality of service must be found within minutes so that problems can be addressed and networks returned to optimised performance. In this paper, a metho...

  5. Fault Diagnosis in Dynamic Systems Using Fuzzy Interacting Observers

    N. V. Kolesov

    2013-01-01

    Full Text Available A method of fault diagnosis in dynamic systems based on a fuzzy approach is proposed. The new method possesses two basic specific features which distinguish it from the other known fuzzy methods based on the application of fuzzy logic and a bank of state observers. First, this method uses a bank of interacting observers instead of traditional independent observers. The second specific feature of the proposed method is the assumption that there is no strict boundary between the serviceable and disabled technical states of the system, which makes it possible to specify a decision making rule for fault diagnosis.

  6. Inter Processor Communication for Fault Diagnosis in Multiprocessor Systems

    C. D. Malleswar

    1994-04-01

    Full Text Available In the preseJlt paper a simple technique is proposed for fault diagnosis for multiprocessor and multiple system environments, wherein all microprocessors in the system are used in part to check the health of their neighbouring processors. It involves building simple fail-safe serial communication links between processors. Processors communicate with each other over these links and each processor is made to go through certain sequences of actions intended for diagnosis, under the observation of another processor .With limited overheads, fault detection can be done by this method. Also outlined are some of the popular techniques used for health check of processor-based systems.

  7. Vibration signal models for fault diagnosis of planet bearings

    Feng, Zhipeng; Ma, Haoqun; Zuo, Ming J.

    2016-05-01

    Rolling element bearings are key components of planetary gearboxes. Among them, the motion of planet bearings is very complex, encompassing spinning and revolution. Therefore, planet bearing vibrations are highly intricate and their fault characteristics are completely different from those of fixed-axis case, making planet bearing fault diagnosis a difficult topic. In order to address this issue, we derive the explicit equations for calculating the characteristic frequency of outer race, rolling element and inner race fault, considering the complex motion of planet bearings. We also develop the planet bearing vibration signal model for each fault case, considering the modulation effects of load zone passing, time-varying angle between the gear pair mesh and fault induced impact force, as well as the time-varying vibration transfer path. Based on the developed signal models, we derive the explicit equations of Fourier spectrum in each fault case, and summarize the vibration spectral characteristics respectively. The theoretical derivations are illustrated by numerical simulation, and further validated experimentally and all the three fault cases (i.e. outer race, rolling element and inner race localized fault) are diagnosed.

  8. Sensor fault diagnosis using Bayesian belief networks

    This paper describes a method based on Bayesian belief networks (BBNs) sensor fault detection, isolation, classification, and accommodation (SFDIA). For this purpose, a BBN uses three basic types of nodes to represent the information associated with each sensor: (1) sensor-reading nodes that represent the mechanisms by which the information is communicated to the BBN, (2) sensor-status nodes that convey the status of the corresponding sensors at any given time, and (3) process-variable nodes that are a conceptual representation of the actual values of the process variables, which are unknown

  9. Review of fault diagnosis and fault-tolerant control for modular multilevel converter of HVDC

    Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede

    This review focuses on faults in Modular Multilevel Converter (MMC) for use in high voltage direct current (HVDC) systems by analyzing the vulnerable spots and failure mechanism from device to system and illustrating the control & protection methods under failure condition. At the beginning......, several typical topologies of MMC-HVDC systems are presented. Then fault types such as capacitor voltage unbalance, unbalance between upper and lower arm voltage are analyzed and the corresponding fault detection and diagnosis approaches are explained. In addition, more attention is dedicated to control...

  10. Application of artificial neural network for NHR fault diagnosis

    The author makes researches on 200 MW nuclear heating reactor (NHR) fault diagnosis system using artificial neural network, and use the tendency value and real value of the data under the accidents to train and test two BP networks respectively. The final diagnostic result is the combination of the results of the two networks. The compound system can enhance the accuracy and adaptability of the diagnosis comparing to the single network system

  11. Optimized Neural Network for Fault Diagnosis and Classification

    This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

  12. Fault Diagnosis of Batch Reactor Using Machine Learning Methods

    Sujatha Subramanian

    2014-01-01

    Full Text Available Fault diagnosis of a batch reactor gives the early detection of fault and minimizes the risk of thermal runaway. It provides superior performance and helps to improve safety and consistency. It has become more vital in this technical era. In this paper, support vector machine (SVM is used to estimate the heat release (Qr of the batch reactor both normal and faulty conditions. The signature of the residual, which is obtained from the difference between nominal and estimated faulty Qr values, characterizes the different natures of faults occurring in the batch reactor. Appropriate statistical and geometric features are extracted from the residual signature and the total numbers of features are reduced using SVM attribute selection filter and principle component analysis (PCA techniques. artificial neural network (ANN classifiers like multilayer perceptron (MLP, radial basis function (RBF, and Bayes net are used to classify the different types of faults from the reduced features. It is observed from the result of the comparative study that the proposed method for fault diagnosis with limited number of features extracted from only one estimated parameter (Qr shows that it is more efficient and fast for diagnosing the typical faults.

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

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

    2013-07-01

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

  14. Vibration signal models for fault diagnosis of planetary gearboxes

    Feng, Zhipeng; Zuo, Ming J.

    2012-10-01

    A thorough understanding of the spectral structure of planetary gear system vibration signals is helpful to fault diagnosis of planetary gearboxes. Considering both the amplitude modulation and the frequency modulation effects due to gear damage and periodically time variant working condition, as well as the effect of vibration transfer path, signal models of gear damage for fault diagnosis of planetary gearboxes are given and the spectral characteristics are summarized in closed form. Meanwhile, explicit equations for calculating the characteristic frequency of local and distributed gear fault are deduced. The theoretical derivations are validated using both experimental and industrial signals. According to the theoretical basis derived, manually created local gear damage of different levels and naturally developed gear damage in a planetary gearbox can be detected and located.

  15. Quantitative Diagnosis of Rotor Vibration Fault Using Process Power Spectrum Entropy and Support Vector Machine Method

    Cheng-Wei Fei; Guang-Chen Bai; Wen-Zhong Tang; Shuang Ma

    2014-01-01

    To improve the diagnosis capacity of rotor vibration fault in stochastic process, an effective fault diagnosis method (named Process Power Spectrum Entropy (PPSE) and Support Vector Machine (SVM) (PPSE-SVM, for short) method) was proposed. The fault diagnosis model of PPSE-SVM was established by fusing PPSE method and SVM theory. Based on the simulation experiment of rotor vibration fault, process data for four typical vibration faults (rotor imbalance, shaft misalignment, rotor-stator rubbin...

  16. Study on Missile Intelligent Fault Diagnosis System Based on Fuzzy NN Expert System

    2001-01-01

    In order to study intelligent fault diagnosis methods based on fuzzy neural network (NN) expert systemand build up intelligent fault diagnosis for a type of mis-sile weapon system, the concrete implementation of a fuzzyNN fault diagnosis expert system is given in this paper. Based on thorough research of knowledge presentation, theintelligent fault diagnosis system is implemented with artificial intelligence for a large-scale missile weapon equipment.The method is an effective way to perform fuzzy fault diagnosis. Moreover, it provides a new way of the fault diagnosisfor large-scale missile weapon equipment.

  17. Improving Robustness of Network Fault Diagnosis to Uncertainty in Observations

    Grønbæk, Lars Jesper; Schwefel, Hans-Peter; Ceccarelli, Andrea;

    2010-01-01

    Performing decentralized network fault diagnosis based on network traffic is challenging. Besides inherent stochastic behaviour of observations, measurements may be subject to errors degrading diagnosis timeliness and accuracy. In this paper we present a novel approach in which we aim to mitigate...... issues of measurement errors by quantifying uncertainty. The uncertainty information is applied in the diagnostic component to improve its robustness. Three diagnosis components have been proposed based on the Hidden Markov Model formalism: (H0) representing a classical approach, (H1) a static...

  18. Active Fault Tolerant Control of Livestock Stable Ventilation System

    Gholami, Mehdi

    2011-01-01

    of the hybrid model are estimated by a recursive estimation algorithm, the Extended Kalman Filter (EKF), using experimental data which was provided by an equipped laboratory. Two methods for active fault diagnosis are proposed. The AFD methods excite the system by injecting a so-called excitation...... degraded performance even in the faulty case. In this thesis, we have designed such controllers for climate control systems for livestock buildings in three steps: Deriving a model for the climate control system of a pig-stable. Designing a active fault diagnosis (AFD) algorithm for different kinds of...... fault. Designing a fault tolerant control scheme for the climate control system. In the first step, a conceptual multi-zone model for climate control of a live-stock building is derived. The model is a nonlinear hybrid model. Hybrid systems contain both discrete and continuous components. The parameters...

  19. Adaptive fault diagnosis in rotating machines using indicators selection

    Khelf, Ilyes; Laouar, Lakhdar; Bouchelaghem, Abdelaziz M.; Rémond, Didier; Saad, Salah

    2013-11-01

    Over the past two decades, condition monitoring and faults diagnosis in rotating machinery have been widely studied and reported. In the present paper an algorithm for fault diagnosis in industrial rotating machines facing new operating conditions emergence is developed on the basis of input indicators, extracted from vibrations spectrums. Indicators selection is used to improve diagnosis performances by the help of a hybrid approach using several selection criteria and different classifiers. To validate the performances of this algorithm, experimental tests were conducted on two industrial systems with various operating conditions. The results have proved the effectiveness of the developed algorithm compared to the "J48 decision tree" and also reveal the need to re-select the indicators for reliable monitoring of working conditions.

  20. Fault diagnosis and isolation of the componentand sensor for aircraft engine

    QIU Xiao-jie; HUANG Jin-quan; LU Feng; LIU Nan

    2012-01-01

    Aircraft engine component and sensor fault detection and isolation approach was proposed,which included fault type detection module and component-sensor simultaneous fault isolation module.The approach can not only distinguish among sensor fault,component fault and component-sensor simultaneous fault,but also isolate and locate sensor fault and the type of engine component fault when the engine component fault and the sensor faults occur simultaneously.The double-threshold mechanism has been proposed,in which the fault diagnostic threshold changed with the sensor type and the engine condition,and it greatly improved the accuracy and robustness of sensor fault diagnosis system.Simulation results show that the approach proposed can diagnose and isolate the sensor and engine component fault with improved accuracy.It effectively improves the fault diagnosis ability of aircraft engine.

  1. A Novel Framework for Real-Time Fault Diagnosis Based on Dynamic Fault Tree Analysis

    Rongxing Duan

    2013-02-01

    Full Text Available To meet the real-time diagnosis requirements of the complex system, this study proposes a novel framework for real-time fault diagnosis using dynamic fault tree analysis. It pays special attention to meeting two challenges: model development and real-time reasoning. In terms of the challenge of model development, we use a dynamic fault tree model to capture the dynamic behavior of system failure mechanisms and calculate some reliability results by mapping a dynamic fault tree into an equivalent Bayesian Network (BN in order to avoid the infamous state space explosion problem. In terms of the real-time reasoning challenge, we adopt a logic compilation based inference algorithm, which compiles the BN into an arithmetic circuit and retrieves answers to probabilistic queries by evaluating and differentiating the arithmetic circuit. Furthermore, we incorporate sensors data into fault diagnosis, cope with the sensors reliability and propose the schemes on how to update the Diagnostic Importance Factor (DIF and the minimal cut sets. Finally, a case study is given to validate the efficiency of this method.

  2. A Comparative Study of Genetic Algorithm Parameters for the Inverse Problem-based Fault Diagnosis of Liquid Rocket Propulsion Systems

    Erfu Yang; Hongjun Xiang; Dongbing Gu; Zhenpeng Zhang

    2007-01-01

    Fault diagnosis of liquid rocket propulsion systems (LRPSs) is a very important issue in space launch activities particularly when manned space missions are accompanied, since the safety and reliability can be significantly enhanced by exploiting an efficient fault diagnosis system. Currently, inverse problem-based diagnosis has attracted a great deal of research attention in fault diagnosis domain. This methodology provides a new strategy to model-based fault diagnosis for monitoring the health of propulsion systems. To solve the inverse problems arising from the fault diagnosis of LRPSs, GAs have been adopted in recent years as the first and effective choice of available numerical optimization tools. However, the GA has many control parameters to be chosen in advance and there still lack sound theoretical tools to analyze the effects of these parameters on diagnostic performance analytically. In this paper a comparative study of the influence of GA parameters on diagnostic results is conducted by performing a series of numerical experiments. The objective of this study is to investigate the contribution of individual algorithm parameter to final diagnostic result and provide reasonable estimates for choosing GA parameters in the inverse problem-based fault diagnosis of LRPSs. Some constructive remarks are made in conclusion and will be helpful for the implementation of GA to the fault diagnosis practice of LRPSs in the future.

  3. Fan Fault Diagnosis Based on Wavelet Packet and Sample Entropy

    Xiaogang Xu

    2013-06-01

    Full Text Available To accurately diagnose the mechanical failure of the fan, two diagnostic methods based on the wavelet packet energy feature and sample entropy feature are proposed. Vibration signals acquisition of 13 kinds of running states are achieved on the 4-73 No.8D centrifugal fan test bench. The wavelet packet energy feature vector of each vibration signal is rapidly extracted through the wavelet packet denoising, decomposition and reconstruction. The vibration signal wavelet packet energy feature vector of the five measuring points in the same instantaneous running state are fused into the fan fault feature vector. Finally, the fault diagnosis of the fan is achieved by using improved SVM (Support Vector Machine classifier, and the accuracy rate is 94.6%. A new fan fault feature vector is put forward, which is the integration of the vibration signal sample entropy of the five measuring points in the same instantaneous running state, and then the fault diagnosis of the fan is achieved by using improved BP (Back Propagation neural network, and the accuracy rate is 99.23%. The diagnostic results show that these two methods are able to effectively diagnose the category, severity and site of the fan mechanical failures, and suitable for online diagnosis.

  4. Fault diagnosis and fault-tolerant control and guidance for aerospace vehicles from theory to application

    Zolghadri, Ali; Cieslak, Jerome; Efimov, Denis; Goupil, Philippe

    2014-01-01

    Fault Diagnosis and Fault-Tolerant Control and Guidance for Aerospace demonstrates the attractive potential of recent developments in control for resolving such issues as improved flight performance, self-protection and extended life of structures. Importantly, the text deals with a number of practically significant considerations: tuning, complexity of design, real-time capability, evaluation of worst-case performance, robustness in harsh environments, and extensibility when development or adaptation is required. Coverage of such issues helps to draw the advanced concepts arising from academic research back towards the technological concerns of industry. Initial coverage of basic definitions and ideas and a literature review gives way to a treatment of important electrical flight control system failures: the oscillatory failure case, runaway, and jamming. Advanced fault detection and diagnosis for linear and nonlinear systems are described. Lastly recovery strategies appropriate to remaining acuator/sensor/c...

  5. A Fault Dictionary-Based Fault Diagnosis Approach for CMOS Analog Integrated Circuits

    Mouna Karmani

    2011-10-01

    Full Text Available In this paper, we propose a simulation-before-test (SBT fault diagnosis methodology based on the use of afault dictionary approach. This technique allows the detection and localization of the most likely defects ofopen-circuit type occurring in Complementary Metal–Oxide–Semiconductor (CMOS analog integratedcircuits (ICs interconnects. The fault dictionary is built by simulating the most likely defects causing thefaults to be detected at the layout level. Then, for each injected fault, the spectre’s frequency responses andthe power consumption obtained by simulation are stored in a table which constitutes the fault dictionary.In fact, each line in the fault dictionary constitutes a fault signature used to identify and locate aconsidered defect. When testing, the circuit under test is excited with the same stimulus, and the responsesobtained are compared to the stored ones. To prove the efficiency of the proposed technique, a full customCMOS operational amplifier is implemented in 0.25 μm technology and the most likely faults of opencircuittype are deliberately injected and simulated at the layout level.

  6. Graphical User Interface Aided Online Fault Diagnosis of Electric Motor - DC motor case study

    POSTALCIOGLU OZGEN, S.

    2009-10-01

    Full Text Available This paper contains graphical user interface (GUI aided online fault diagnosis for DC motor. The aim of the research is to prevent system faults. Online fault diagnosis has been studied. Design of fault diagnosis has two main levels: Level 1 comprises a traditional control loop; Level 2 contains knowledge based fault diagnosis. Fault diagnosis technique contains feature extraction module, feature cluster module and fault decision module. Wavelet analysis has been used for the feature extraction module. For the feature cluster module, fuzzy cluster has been applied. Faults effects are examined on the system using statistical analysis. In this study Fault Diagnosis technique obtains fault detection, identification and halting the system. In the meantime graphical user interface (GUI is opened when fault is detected. GUI shows the measurement value, fault time and fault type. This property gives some information about the system to the personnel. As seen from the simulation results, faults can be detected and identified as soon as fault appears. In summary, if the system has a fault diagnosis structure, system dangerous situations can be avoided.

  7. A fault diagnosis system for nuclear power plant operation

    A fault diagnosis system has been developed to support operators in nuclear power plants. In the system various methods are combined to get a diagnosis result which provides better detection sensitivity and result reliability. The system is composed of an anomaly detection part with diagnosis modules, an integration part which obtains the diagnosis result by combining results from each diagnosis module, and a prediction part with state prediction and estimation modules. For the anomaly detection part, three kinds of modules are prepared: plant signal processing, early fault detection and event identification modules. The plant signal processing module uses wavelet transform and chaos technologies as well as fast Fourier transform (FFT) to analyze vibration sensor signals and to detect signal anomaly. The early fault detection module uses the neural network model of a plant subprocess to estimate the process variable values assuming normal conditions, and to detect an anomaly by comparing the measured and estimated values. The event identification module identifies the kind of occurring event by using the neural network and knowledge processing. In the integration part the diagnosis is performed by using knowledge processing. The knowledge for diagnosis is structured based on the means-ends abstraction hierarchy to simplify knowledge input and maintenance. In the prediction part, the prediction module predicts the future changes of process variables and plant interlock statuses and the estimation module estimates the values of unmeasurable variables. A prototype system has been developed and the system performance was evaluated. The evaluation results show that the developed technologies are effective to improve the human-machine system for plant operation. (author)

  8. Monitoring and diagnosis for sensor fault detection using GMDH methodology

    The fault detection and diagnosis system is an Operator Support System dedicated to specific functions that alerts operators to sensors and actuators fault problems, and guide them in the diagnosis before the normal alarm limits are reached. Operator Support Systems appears to reduce panels complexity caused by the increase of the available information in nuclear power plants control room. In this work a Monitoring and Diagnosis System was developed based on the GMDH (Group Method of Data Handling) methodology. The methodology was applied to the IPEN research reactor IEA-R1. The system performs the monitoring, comparing GMDH model calculated values with measured values. The methodology developed was firstly applied in theoretical models: a heat exchanger model and an IPEN reactor theoretical model. The results obtained with theoretical models gave a base to methodology application to the actual reactor operation data. Three GMDH models were developed for actual operation data monitoring: the first one using just the thermal process variables, the second one was developed considering also some nuclear variables, and the third GMDH model considered all the reactor variables. The three models presented excellent results, showing the methodology utilization viability in monitoring the operation data. The comparison between the three developed models results also shows the methodology capacity to choose by itself the best set of input variables for the model optimization. For the system diagnosis implementation, faults were simulated in the actual temperature variable values by adding a step change. The fault values correspond to a typical temperature descalibration and the result of monitoring faulty data was then used to build a simple diagnosis system based on fuzzy logic. (author)

  9. Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System

    2007-01-01

    Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic element is the test-diagnosis fault unit. Every underlying fault cause's membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples,the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.

  10. Design and implementation of an expert system for remote fault diagnosis in ship lift

    2007-01-01

    In this paper an expert system for remote fault diagnosis in the ship lift was developed by analysis of the fault tree and combination with VPN. The fault tree was constructed based on the operation condition of the ship lift. The diagnosis model was constructed by hierarchical classification of the fault tree structure, and the inference mechanism was given. Logical structure of the fault diagnosis in the ship lift was proposed. The implementation of the expert system for remote fault diagnosis in the ship...

  11. Vehicle gearbox fault diagnosis using noise measurements

    Metwalley, Sameh M.; Hammad, Nabil; Abouel-Seoud, Shawki A. [Engineering, Helwan University, Cairo (Egypt)

    2011-07-01

    Noise measurement is one of many technologies for health monitoring and diagnosis of rotating machines such as gearboxes. Although significant research has been undertaken in understanding the potential of noise measurement in monitoring gearboxes this has been solely applied on any types of gears (spur, helical, ..etc.). The condition monitoring of a lab-scale, single stage, gearbox, represents the vehicle real gearbox, using non-destructive inspection methodology and the processing of the acquired waveform with advanced signal processing techniques is the aim of the present work. Acoustic emission was utilized for this purpose. The experimental setup and the instrumentation are present in detail. Emphasis is given on the signal processing of the acquired noise measurement signal in order to extract conventional as well as novel parameters potential diagnostic value from the monitoring waveform. The evolution of selected parameters/features versus test time is provided, evaluated and the parameters with most interesting diagnostic behavior are highlighted. The present work also reports the results concluded by long term ({approx} 6.0 h) experiments to a defected gear system, with a transverse cuts ranged from 0.75 mm to 3.0 mm to simulate the tooth crack. Different parameters, related by the analysis of the recording signals coming from acoustic emission are presented and their diagnostic value is discussed for the development of a condition monitoring system.

  12. Sensor Fault Detection and Diagnosis for autonomous vehicles

    Realpe Miguel

    2015-01-01

    Full Text Available In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed architecture is designed to detect obstacles in an autonomous vehicle’s environment while detecting a faulty sensor using SVM models for fault detection and diagnosis. Experimental results using sensor information from the KITTI dataset confirm the feasibility of the proposed architecture to detect soft and hard faults from a particular sensor.

  13. Rolling bearing fault diagnosis using an optimization deep belief network

    The vibration signals measured from a rolling bearing are usually affected by the variable operating conditions and background noise which lead to the diversity and complexity of the vibration signal characteristics, and it is a challenge to effectively identify the rolling bearing faults from such vibration signals with no further fault information. In this paper, a novel optimization deep belief network (DBN) is proposed for rolling bearing fault diagnosis. Stochastic gradient descent is used to efficiently fine-tune all the connection weights after the pre-training of restricted Boltzmann machines (RBMs) based on the energy functions, and the classification accuracy of the DBN is improved. Particle swarm is further used to decide the optimal structure of the trained DBN, and the optimization DBN is designed. The proposed method is applied to analyze the simulation signal and experimental signal of a rolling bearing. The results confirm that the proposed method is more accurate and robust than other intelligent methods. (paper)

  14. Rolling bearing fault diagnosis using an optimization deep belief network

    Shao, Haidong; Jiang, Hongkai; Zhang, Xun; Niu, Maogui

    2015-11-01

    The vibration signals measured from a rolling bearing are usually affected by the variable operating conditions and background noise which lead to the diversity and complexity of the vibration signal characteristics, and it is a challenge to effectively identify the rolling bearing faults from such vibration signals with no further fault information. In this paper, a novel optimization deep belief network (DBN) is proposed for rolling bearing fault diagnosis. Stochastic gradient descent is used to efficiently fine-tune all the connection weights after the pre-training of restricted Boltzmann machines (RBMs) based on the energy functions, and the classification accuracy of the DBN is improved. Particle swarm is further used to decide the optimal structure of the trained DBN, and the optimization DBN is designed. The proposed method is applied to analyze the simulation signal and experimental signal of a rolling bearing. The results confirm that the proposed method is more accurate and robust than other intelligent methods.

  15. Segmented infrared image analysis for rotating machinery fault diagnosis

    Duan, Lixiang; Yao, Mingchao; Wang, Jinjiang; Bai, Tangbo; Zhang, Laibin

    2016-07-01

    As a noncontact and non-intrusive technique, infrared image analysis becomes promising for machinery defect diagnosis. However, the insignificant information and strong noise in infrared image limit its performance. To address this issue, this paper presents an image segmentation approach to enhance the feature extraction in infrared image analysis. A region selection criterion named dispersion degree is also formulated to discriminate fault representative regions from unrelated background information. Feature extraction and fusion methods are then applied to obtain features from selected regions for further diagnosis. Experimental studies on a rotor fault simulator demonstrate that the presented segmented feature enhancement approach outperforms the one from the original image using both Naïve Bayes classifier and support vector machine.

  16. Multisensor Data Fusion for Automotive Engine Fault Diagnosis

    王赟松; 褚福磊; 何永勇; 郭丹

    2004-01-01

    This paper describes mainly a decision-level data fusion technique for fault diagnosis for electronically controlled engines.Experiments on a SANTANA AJR engine show that the data fusion method provides good engine fault diagnosis.In data fusion methods, the data level fusion has small data preprocessing loads and high accuracy, but requires commensurate sensor data and has poor operational performance.The decision-level fusion based on Dempster-Shafer evidence theory can process noncommensurate data and has robust operational performance, reduces ambiguity, increases confidence, and improves system reliability, but has low fusion accuracy and high data preprocessing cost.The feature-level fusion provides good compromise between the above two methods, which becomes gradually mature.In addition, acquiring raw data is a precondition to perform data fusion, so the system for signal acquisition and processing for an automotive engine test is also designed by the virtual instrument technology.

  17. FDSAC-SPICE: fault diagnosis software for analog circuit based on SPICE simulation

    Cao, Yiqin; Cen, Zhao-Hui; Wei, Jiao-Long

    2009-12-01

    This paper presents a novel fault diagnosis software (called FDSAC-SPICE) based on SPICE simulator for analog circuits. Four important techniques in AFDS-SPICE, including visual user-interface(VUI), component modeling and fault modeling (CMFM), fault injection and fault simulation (FIFS), fault dictionary and fault diagnosis (FDFD), greatly increase design-for-test and diagnosis efficiency of analog circuit by building a fault modeling-injection-simulationdiagnosis environment to get prior fault knowledge of target circuit. AFDS-SPICE also generates accurate fault coverage statistics that are tied to the circuit specifications. With employing a dictionary diagnosis method based on node-signalcharacters and regular BPNN algorithm, more accurate and effective diagnosis results are available for analog circuit with tolerance.

  18. A Fuzzy Mathematics Based Fault Auto-diagnosis System for Vacuum Resin Shot Dosing Equipment

    2001-01-01

    On the basis of the analysis of faults and their causes of vacuum resin shot dosing equipment, the fuzzy model of fault diagnosis for the equipment is constructed, and the fuzzy relationship matrix, the symptom fuzzy vector, the fuzzy compound arithmetic operator, and the diagnosis principle of the model are determined. Then the fault auto-diagnosis system for the equipment is designed, and the functions for real-time monitoring its operation condition and for fault auto-diagnosis are realized. Finally, the experiments of fault auto-diagnosis are conducted in practical production and the veracity of the system is verified.

  19. Estimation and fault diagnosis strategies for networked control systems

    Dolz Algaba, Daniel

    2014-01-01

    Communication networks increase flexibility of industrial monitoring, supervisory and control systems. However, they introduce delays or even dropouts on the transmitted information that affect the performance and robustness on the decision and control mechanisms in the system. This thesis contributes theoretically to the state estimation and fault diagnosis problem over networks. First, we study the state estimation problem. Motivated by reducing the implementation computational load of L...

  20. Rotor blade online monitoring and fault diagnosis technology research

    Tesauro, Angelo; Pavese, Christian; Branner, Kim

    Rotor blade online monitoring and fault diagnosis technology is an important way to find blade failure mechanisms and thereby improve the blade design. Condition monitoring of rotor blades is necessary in order to ensure the safe operation of the wind turbine, make the maintenance more economical......, unbalancing of the rotor, icing and lightning. Research is done throughout the world in order to develop and improve such measurement systems. Commercial hardware and software available for the described purpose is presented in the report....

  1. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Jinde Zheng; Junsheng Cheng; Yu Yang

    2014-01-01

    A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper. Permutation entropy (PE) was recently proposed and defined to measure the randomicity and detect dynamical changes of time series. However, for the complexity of mechanical systems, the randomicity and dynamic changes of the vibration signal will exist in different scales. Thus, the definition of MPE is introduced and...

  2. Fault Diagnosis in a Fully Distributed Local Computer Network.

    Kwag, Hye Keun

    Local computer networks are being installed in diverse application areas. Many of the networks employ a distributed control scheme, which has advantages in performance and reliability over a centralized one. However, distribution of control increases the difficulty in locating faulty hardware elements. Consequently, advantages may not be fully realized unless measures are taken to account for the difficulties of fault diagnosis; yet, not much work has been done in this area. A hardcore is defined as a node or a part of a node which is fault-free and which can diagnose other elements in a system. Faults are diagnosed in most existing distributed local computer networks by assuming that every node, or a part of every node, is a fixed hardcore: a fixed node or a part of a fixed node is always a hardcore. Maintaining such high reliability may not be possible or cost-effective for some systems. A distributed network contains dynamically redundant elements, and it is reasonable to assume that fewer nodes are simultaneously faulty than are fault-free at any point in the life cycle of the network. A diagnostic model is proposed herein which determines bindary evaluation results according to the status of the testing and tested nodes, and which leads the network to dynamically locate a fault-free node (a hardcore). This diagnostic model is, in most cases, simpler to implement and more cost-effective than the fixed hardcore. The selected hardcore can diagnose the other elements and can locate permanent faults. In a hop-by-hop test, the destination node and every intermediate node in a path test the transmitted data. This dissertation presents another method to locate an element with frequent transient faults; it checks data only at the destination, thereby, eliminating the need for a hop-by-hop test.

  3. Illuminating Northern California's Active Faults

    Prentice, Carol S.; Crosby, Christopher J.; Whitehill, Caroline S.; Arrowsmith, J. Ramón; Furlong, Kevin P.; Phillips, David A.

    2009-02-01

    Newly acquired light detection and ranging (lidar) topographic data provide a powerful community resource for the study of landforms associated with the plate boundary faults of northern California (Figure 1). In the spring of 2007, GeoEarthScope, a component of the EarthScope Facility construction project funded by the U.S. National Science Foundation, acquired approximately 2000 square kilometers of airborne lidar topographic data along major active fault zones of northern California. These data are now freely available in point cloud (x, y, z coordinate data for every laser return), digital elevation model (DEM), and KMZ (zipped Keyhole Markup Language, for use in Google Earth™ and other similar software) formats through the GEON OpenTopography Portal (http://www.OpenTopography.org/data). Importantly, vegetation can be digitally removed from lidar data, producing high-resolution images (0.5- or 1.0-meter DEMs) of the ground surface beneath forested regions that reveal landforms typically obscured by vegetation canopy (Figure 2).

  4. Application of multi-sensor information fusion technology on fault diagnosis of hydraulic system

    The structural layers and methods of multi-sensor information fusion technology are analysed, and its application in fault diagnosis of hydraulic system is discussed. Aiming at hydraulic system, a model of hydraulic fault diagnosis system based on multi-sensor information fusion technology is presented. Choosing and implementing the method of information fusion reasonably, the model can fuse and calculate various fault characteristic parameters in hydraulic system effectively and provide more valuable result for fault diagnosis of hydraulic system.

  5. Study on the Consultation Mechanism of an Internet-Based Remote Fault Diagnosis System

    2006-01-01

    Aimed at the deficiency of the mechanism of management and consultation, an idea of an internet-based Virtual Diagnosis Center (VDC) for machine fault is proposed, and the key elements of remote consultation are abstracted. Around the key elements, the construct scheme and cooperative mechanism among experts of VDC are designed. According to the diagnosed object, the context knowledge of a fault machine, fault cases and ActiveX-based analysis tools are integrated into a multimedia consultation environment in VDC to enhance the efficiency of expert consultation. Simultaneously, the technique of push subscription in a SQL Server is utilized to collect machine condition data in an enterprise machine condition database, which ensures the security of the database. The VDC system in Xi'an Jiaotong University has been applied to remote diagnosis of a blower in Wuhan Iron and Steel Corporation and the system construction reasonableness and the running stability are verified.

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

    Narayan, Anand P.

    1998-07-01

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

  7. Fault diagnosis model based on multi-manifold learning and PSO-SVM for machinery

    Wang Hongjun; Xu Xiaoli; Rosen B G

    2014-01-01

    Fault diagnosis technology plays an important role in the industries due to the emergency fault of a machine could bring the heavy lost for the people and the company. A fault diagnosis model based on multi-manifold learning and particle swarm optimization support vector machine (PSO-SVM) is studied. This fault diagnosis model is used for a rolling bearing experimental of three kinds faults. The results are verified that this model based on multi-manifold learning and PSO-SVM is good at the fault sensitive features acquisition with effective accuracy.

  8. Application of particle swarm optimization blind source separation technology in fault diagnosis of gearbox

    黄晋英; 潘宏侠; 毕世华; 杨喜旺

    2008-01-01

    Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.

  9. Fault Diagnosis Scheme for Nonlinear Stochastic Hybrid Systems With Time-Varying Fault

    Nguyen, H.Q.; Čelikovský, Sergej

    Lima: Tarea Asociación Gráfica Educativa, Lima, Peru, 2012, s. 1-7. ISBN 978-612-4057-71-7. [15th Latinamerican Control Conference CLCA 2012. Lima (PE), 23.10.2012-26.10.2012] Institutional support: RVO:67985556 Keywords : Fault detection and diagnosis * nonlinear stochastic hybrid system * B-spline functions * probability density function(PDF) Subject RIV: BC - Control Systems Theory

  10. Knowledge Processing Method of Fault Diagnosis Expert Systems for Letter Sorting Equipment

    2001-01-01

    Based on the analysis of fault diagnosis knowledge of lettersorting machine, this paper proposes a processing method by which the fault diagnosis knowledge is divided into exact knowledge, inadequate knowledge and fuzzy knowledge. Then their presenting and implementing form in fault diagnosis expert system is discussed and studied. It is proved that the expert system has good feasibility in the field of the diagnosis of letter sorting machine.

  11. Study on intelligent fault diagnosis system of nuclear power plants based on information fusion technique

    The technology of information fusion is used in the fault diagnosis for ship nuclear power plants in this paper. The space fusion structure is built based on fault tree expert system, NN diagnosis system, and mechanism model validation system. Not only the system deep-level knowledge, but also the shallow knowledge and the mechanism model knowledge are fully used. The simulation validation and verification showed that the information fusion diagnosis system could improve the fault diagnosis reliability effectively. (authors)

  12. Application of ENN-1 for Fault Diagnosis of Wind Power Systems

    Meng-Hui Wang; Hung-Cheng Chen

    2012-01-01

    Maintaining a wind turbine and ensuring secure is not easy because of long-term exposure to the environment and high installation locations. Wind turbines need fully functional condition-monitoring and fault diagnosis systems that prevent accidents and reduce maintenance costs. This paper presents a simulator design for fault diagnosis of wind power systems and further proposes some fault diagnosis technologies such as signal analysis, feature selecting, and diagnosis methods. First, this pap...

  13. Fuzzy Timing Petri Net for Fault Diagnosis in Power System

    Alireza Tavakholi Ghainani

    2012-01-01

    Full Text Available A model-based system for fault diagnosis in power system is presented in this paper. It is based on fuzzy timing Petri net (FTPN. The ordinary Petri net (PN tool is used to model the protective components, relays, and circuit breakers. In addition, fuzzy timing is associated with places (token/transition to handle the uncertain information of relays and circuits breakers. The received delay time information of relays and breakers is mapped to fuzzy timestamps, π(τ, as initial marking of the backward FTPN. The diagnosis process starts by marking the backward sub-FTPNs. The final marking is found by going through the firing sequence, σ, of each sub-FTPN and updating fuzzy timestamp in each state of σ. The final marking indicates the estimated fault section. This information is then in turn used in forward FTPN to evaluate the fault hypothesis. The FTPN will increase the speed of the inference engine because of the ability of Petri net to describe parallel processing, and the use of time-tag data will cause the inference procedure to be more accurate.

  14. Fault diagnosis and fault-tolerant control strategies for non-linear systems analytical and soft computing approaches

    Witczak, Marcin

    2014-01-01

      This book presents selected fault diagnosis and fault-tolerant control strategies for non-linear systems in a unified framework. In particular, starting from advanced state estimation strategies up to modern soft computing, the discrete-time description of the system is employed Part I of the book presents original research results regarding state estimation and neural networks for robust fault diagnosis. Part II is devoted to the presentation of integrated fault diagnosis and fault-tolerant systems. It starts with a general fault-tolerant control framework, which is then extended by introducing robustness with respect to various uncertainties. Finally, it is shown how to implement the proposed framework for fuzzy systems described by the well-known Takagi–Sugeno models. This research monograph is intended for researchers, engineers, and advanced postgraduate students in control and electrical engineering, computer science,as well as mechanical and chemical engineering.

  15. Combinatorial Optimization Algorithms for Dynamic Multiple Fault Diagnosis in Automotive and Aerospace Applications

    Kodali, Anuradha

    In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a

  16. Soft computing for fault diagnosis in power plants

    Considering the advancements in the AI technology, there arises a new concept known as soft computing. It can be defined as the processing of uncertain information with the AI methods, that refers to explicitly the methods using neural networks, fuzzy logic and evolutionary algorithms. In this respect, soft computing is a new dimension in information processing technology where linguistic information can also be processed in contrast with the classical stochastic and deterministic treatments of data. On one hand it can process uncertain/incomplete information and on the other hand it can deal with non-linearity of large-scale systems where uncertainty is particularly relevant with respect to linguistic information and incompleteness is related to fault tolerance in fault diagnosis. In this perspective, the potential role of soft computing in power plant operation is presented. (author)

  17. Intercurrent fault diagnosis of nuclear power plants based on hybrid artificial neural network

    Based on the analysis of the structure of ART-2 and parallel BP neural network, a hybrid artificial neural network is proposed aiming at the intercurrent faults diagnosis of nuclear power plants. Firstly the ART-2 net is used to identify the single fault, then the parallel BP net is used to distinguish intercurrent faults from new fault. The simulation shows that, the hybrid artificial neural network resolves the problem of single neural network in distinguishing intercurrent faults from new fault, and can diagnose the intercurrent fault and new fault efficiently. (authors)

  18. Active fault detection in MIMO systems

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2014-01-01

    from auxiliary input to residual outputs. The analysis is based on a singular value decomposition of these transfer functions Based on this analysis, it is possible to design auxiliary input as well as design of the associated residual vector with respect to every single parametric fault in the system......The focus in this paper is on active fault detection (AFD) for MIMO systems with parametric faults. The problem of design of auxiliary inputs with respect to detection of parametric faults is investigated. An analysis of the design of auxiliary inputs is given based on analytic transfer functions...

  19. Diagnosis of Constant Faults in Read-Once Contact Networks over Finite Bases using Decision Trees

    Busbait, Monther I.

    2014-05-01

    We study the depth of decision trees for diagnosis of constant faults in read-once contact networks over finite bases. This includes diagnosis of 0-1 faults, 0 faults and 1 faults. For any finite basis, we prove a linear upper bound on the minimum depth of decision tree for diagnosis of constant faults depending on the number of edges in a contact network over that basis. Also, we obtain asymptotic bounds on the depth of decision trees for diagnosis of each type of constant faults depending on the number of edges in contact networks in the worst case per basis. We study the set of indecomposable contact networks with up to 10 edges and obtain sharp coefficients for the linear upper bound for diagnosis of constant faults in contact networks over bases of these indecomposable contact networks. We use a set of algorithms, including one that we create, to obtain the sharp coefficients.

  20. Unsupervised process monitoring and fault diagnosis with machine learning methods

    Aldrich, Chris

    2013-01-01

    This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data

  1. Logic-dynamic approach to fault diagnosis in mechatronic systems

    A. N. Zhirabok

    2008-11-01

    Full Text Available This paper presents a problem of fault detection and isolation (FDI in mechatronic systems described by nonlinear dynamic models with such types of no differentiable nonlinearities as saturation, Coulomb friction, backlash, and hysteresis. To solve this problem, so-called logic-dynamic approach is suggested. This approach consists of three main steps: replacing the initial nonlinear system by certain linear logic-dynamic system, obtaining the bank of linear logic-dynamic observers, and transforming these observes into the nonlinear ones. Logic-dynamic approach allows one to use the linear FDI methods for diagnosis in nonlinear mechatronic systems.

  2. MULTIPLE FAULT DIAGNOSIS FOR HIGH SPEED HYBRID MEMORY ARCHITECTURE

    B. Kamala Soundari; M. Praveena

    2013-01-01

    This paper presents a built-in self-test (BIST)-based scheme for fault diagnosis that can be used toidentify permanent failures and automatic correction in all memories & circuits. The proposed approachoffers a simple test flow and does not require intensive interactions between a BIST controller and a tester.The scheme rests on partitioning of rows and columns of the memory array by employing low cost test logic.It is designed to meet requirements of at-speed test thus enabling detection of ...

  3. Application of Petri Net to Fault Diagnosis in Satellite

    2001-01-01

    A prototype of fault diagnosis based on Petri net, which is developed for a satellite tele-control subsystem, is introduced in this paper. Its structure is first given with the emphasis on a Petri net modeling tool which is designed using the object oriented method. The prototype is connected to the database with DAO (Date Access Object) technique, and makes the Petri net's firing mechanism and its analyzing methods to be packed up as DLL (Dynamic Link Library) documents. Compared with the rule-based expert system method, the Petri net-based one can store the knowledge in mathematical matrix and make inference more quickly and effectively.

  4. Nuclear power plant fault-diagnosis using artificial neural networks

    Artificial neural networks (ANNs) have been applied to various fields due to their fault and noise tolerance and generalization characteristics. As an application to nuclear engineering, we apply neural networks to the early recognition of nuclear power plant operational transients. If a transient or accident occurs, the network will advise the plant operators in a timely manner. More importantly, we investigate the ability of the network to provide a measure of the confidence level in its diagnosis. In this research an ANN is trained to diagnose the status of the San Onofre Nuclear Generation Station using data obtained from the plant's training simulator. Stacked generalization is then applied to predict the error in the ANN diagnosis. The data used consisted of 10 scenarios that include typical design basis accidents as well as less severe transients. The results show that the trained network is capable of diagnosing all 10 instabilities as well as providing a measure of the level of confidence in its diagnoses

  5. Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes

    Compound faults diagnosis is a challenge for rotating machinery fault diagnosis. The vibration signals measured from gearboxes are usually complex, non-stationary, and nonlinear. When compound faults occur in a gearbox, weak fault characteristic signals are always submerged by the strong ones. Therefore, it is difficult to detect a weak fault by using the demodulating analysis of vibration signals of gearboxes directly. The key to compound faults diagnosis of gearboxes is to separate different fault characteristic signals from the collected vibration signals. Aiming at that problem, a new method for the compound faults diagnosis of gearboxes is proposed based on the energy operator demodulating of optimal resonance components. In this method, the genetic algorithm is first used to obtain the optimal decomposition parameters. Then the compound faults vibration signals of a gearbox are subject to resonance-based signal sparse decomposition (RSSD) to separate the fault characteristic signals of the gear and the bearing by using the optimal decomposition parameters. Finally, the separated fault characteristic signals are analyzed by energy operator demodulating, and each one’s instantaneous amplitude can be calculated. According to the spectra of instantaneous amplitudes of fault characteristic signals, the faults of the gear and the bearing can be diagnosed, respectively. The performance of the proposed method is validated by using the simulation data and the experiment vibration signals from a gearbox with compound faults. (paper)

  6. Use of autocorrelation of wavelet coefficients for fault diagnosis

    Rafiee, J.; Tse, P. W.

    2009-07-01

    This paper presents a novel time-frequency-based feature recognition system for gear fault diagnosis using autocorrelation of continuous wavelet coefficients (CWC). Furthermore, it introduces an original mathematical approximation of gearbox vibration signals which approximates sinusoidal components of noisy vibration signals generated from gearboxes, including incipient and serious gear failures using autocorrelation of CWC. First, the drawbacks of the continuous wavelet transform (CWT) have been eliminated using autocorrelation function. Secondly, the autocorrelation of CWC is introduced as an original pattern for fault identification in machine condition monitoring. Thirdly, a sinusoidal summation function consisting of eight terms was used to approximate the periodic waveforms generated by autocorrelation of CWC for normal gearboxes (NGs) as well as occurrences of incipient and severe gear fault (e.g. slight-worn, medium-worn, and broken-tooth gears). In other words, the size of vibration signals can be reduced with minimal loss of significant frequency content by means of the sinusoidal approximation of generated autocorrelation waveforms of CWC as reported in this paper.

  7. Dynamic eccentricity fault diagnosis in round rotor synchronous motors

    Research highlights: → We have presented a novel approach to detect dynamic eccentricity in round rotor synchronous motors. → We have introduced an efficient index based on processing torque using time series data mining method. → The stator current spectrum of the motor under different levels of fault and load are computed. → Winding function method has been employed to model healthy and faulty synchronous motors. -- Abstract: In this paper, a novel approach is presented to detect dynamic eccentricity in round rotor synchronous motors. For this, an efficient index is introduced based on processing developed torque using time series data mining (TSDM) method. This index can be utilized to diagnose eccentricity fault and its degree. The capability of this index to predict dynamic eccentricity is illustrated by investigation of load variation impacts on the nominated index. Stator current spectrum of the faulty synchronous motor under different loads and dynamic eccentricity degrees are computed. Effects of the dynamic eccentricity and load variation simultaneously are scrutinized on the magnitude of 17th and 19th harmonic components as traditional indices for eccentricity fault diagnosis in synchronous motors. Necessity signals and parameters for processing and feature extraction are evaluated by winding function method which is employed to model healthy and faulty synchronous motors.

  8. Study and Design of Diaphragm Pump Vibration Detection Fault Diagnosis System Based on FFT

    Jia Yin; Jiande Wu; Xuyi Yuan; Xiaodong Wang; Yugang Fan

    2013-01-01

    This study has proposed a fault diagnosis system based on vibration detection. The system mainly includes four modules: signal acquisition module, signal processing module, state identification module, fault diagnosis and alarm module. The system uses CMSS 2200 acceleration sensor to collect vibration signals, processing spectrum with FFT (Fast Fourier Transform) which is used effectively in current industry and finally achieve fault diagnosis and prediction for diaphragm pump. Through collec...

  9. A Fault Diagnosis Scheme and Its Quality Issue in Reconfigurable Array Architecture

    Chen, Yung-Yuan

    2006-01-01

    In this paper, we propose an efficient diagnosis scheme to detect and locate the switching network defects/faults in reconfigurable array architecture. This diagnosis scheme performs the test of switching network based on the scan path and fault intersection test methodology to locate the faults occurring in the switching network. After the diagnosis of switching network, the processing element (PE) test can then be initiated through the good switches and links. Errors in testing that cause a...

  10. STUDY OF A FAULT DIAGNOSIS EXPERT SYSTEM FOR SYNTHETIC MINING SYSTEM HYDRAULIC SUPPORT

    Han Yilun

    2000-01-01

    Fault diagnosis expert system for hydraulic support is studied.The system is achieved by Turbo-prolong Language, it summaries the experience of the domain expert and sets up a fault tree, knowledge base is developed by a productive rule.According to the feature of diagnosis, the system selects forward non-determination inferring and limited depth-first search strategy.It can accomplish expert diagnosis of more than 50 kinds faults in hydraulic support.

  11. On-line fault diagnosis of industrial processes based on artificial intelligence techniques

    Calado, J. M. F.

    1996-01-01

    In this research the application of artificial intelligence techniques for on-line process control and fault detection and diagnosis are investigated. The majority of the research is on using artificial intelligence techniques in on-line fault detection and diagnosis of industrial processes. Several on-line approaches, including a rule based controller and several fault detection and diagnosis systems, have been developed and implemented and are described throughout this thesis. The research ...

  12. Artificial Intelligence Applications in the Diagnosis of Power Transformer Incipient Faults

    Wang, Zhenyuan

    2000-01-01

    This dissertation is a systematic study of artificial intelligence (AI) applications for the diagnosis of power transformer incipient fault. The AI techniques include artificial neural networks (ANN, or briefly neural networks - NN), expert systems, fuzzy systems and multivariate regression. The fault diagnosis is based on dissolved gas-in-oil analysis (DGA). A literature review showed that the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC) an...

  13. Demagnetization fault diagnosis in permanent magnet synchronous motors: A review of the state-of-the-art

    Moosavi, S.S., E-mail: anchepoli@gmail.com [University of Technology Belfort Montbeliard (UTBM), Laboratory of IRTES-SET, Belfort (France); Engineering Department, Amol University of Special Modern Technology, Amol (Iran, Islamic Republic of); Djerdir, A. [University of Technology Belfort Montbeliard (UTBM), Laboratory of IRTES-SET, Belfort (France); Amirat, Y.Ait. [Laboratory of Femto-ST, University of Franche-Comte (France); Khaburi, D.A. [Center of Excellence for Power System Automation and Operation, Iran University of Science and Technology (IUST), Tehran (Iran, Islamic Republic of)

    2015-10-01

    There are a lot of research activities on developing techniques to detect permanent magnet (PM) demagnetization faults (DF). These faults decrease the performance, the reliability and the efficiency of permanent magnet synchronous motor (PMSM) drive systems. In this work, we draw a broad perspective on the status of these studies. The advantages, disadvantages of each method, a deeper view investigated and a comprehensive list of references are reported. - Highlights: • A review of state of the art on demagnetization fault diagnosis was studied deeply. • Critical points in fault diagnosis are discussed aiming to safety and cost management. • Critical comparison on all existent demagnetization diagnosis methods was done. • It is proved that DE and UL have the same signature frequencies as partial demagnetization. • MCSA have some limitations in frequency component under uniform demagnetization.

  14. Demagnetization fault diagnosis in permanent magnet synchronous motors: A review of the state-of-the-art

    There are a lot of research activities on developing techniques to detect permanent magnet (PM) demagnetization faults (DF). These faults decrease the performance, the reliability and the efficiency of permanent magnet synchronous motor (PMSM) drive systems. In this work, we draw a broad perspective on the status of these studies. The advantages, disadvantages of each method, a deeper view investigated and a comprehensive list of references are reported. - Highlights: • A review of state of the art on demagnetization fault diagnosis was studied deeply. • Critical points in fault diagnosis are discussed aiming to safety and cost management. • Critical comparison on all existent demagnetization diagnosis methods was done. • It is proved that DE and UL have the same signature frequencies as partial demagnetization. • MCSA have some limitations in frequency component under uniform demagnetization

  15. Fault Detection and Diagnosis System for the Air-conditioning

    Nakahara, Nobuo

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

  16. Health and Maintenance Status Determination and Predictive Fault Diagnosis System Project

    National Aeronautics and Space Administration — The objective of this project is to demonstrate intelligent health and maintenance status determination and predictive fault diagnosis techniques for NASA rocket...

  17. Study of fault diagnosis of nuclear power plant based on improved genetic algorithm

    Aimed at the characteristics of marine nuclear power plant that fault and corresponding sign are various, and in order to overcome inhere problem of traditional genetic algorithm, improved genetic algorithm and probability causal model were integrated to establish fault diagnosis system. The system is made up of data collection module and fault diagnosis module with high real time and speed. To validate the system, stream generator was diagnosed, and the computation result is in agreement with conclusion of some literature. At last, the comparison with tradition arithmetic was made. The results indicate that with improved genetic algorithm, mistaken diagnosis is improved effectively, and accuracy of fault diagnosis is enhanced. (authors)

  18. Review of Diagnosis Technique for Equipment Faults and its Development Trend

    Sun Lihua

    2015-01-01

    Full Text Available Modern control system is becoming larger and more complicated with each passing day and the possibility of system breakdown increases with it, so people eagerly need to set up fault diagnosis system to conduct real-time monitoring and fault diagnosis for production system and take necessary measures to improve its overall reliability and maintainability. This paper states the principles and basic approaches of diagnosis technique for equipment faults, introduces development phrase of fault diagnosis technique and points out future development trend in this field.

  19. Fault Diagnosis for Actuators in a Class of Nonlinear Systems Based on an Adaptive Fault Detection Observer

    Runxia Guo

    2016-01-01

    Full Text Available The problem of actuators’ fault diagnosis is pursued for a class of nonlinear control systems that are affected by bounded measurement noise and external disturbances. A novel fault diagnosis algorithm has been proposed by combining the idea of adaptive control theory and the approach of fault detection observer. The asymptotical stability of the fault detection observer is guaranteed by setting the adaptive adjusting law of the unknown fault vector. A theoretically rigorous proof of asymptotical stability has been given. Under the condition that random measurement noise generated by the sensors of control systems and external disturbances exist simultaneously, the designed fault diagnosis algorithm is able to successfully give specific estimated values of state variables and failures rather than just giving a simple fault warning. Moreover, the proposed algorithm is very simple and concise and is easy to be applied to practical engineering. Numerical experiments are carried out to evaluate the performance of the fault diagnosis algorithm. Experimental results show that the proposed diagnostic strategy has a satisfactory estimation effect.

  20. Diagnosis and Fault-Tolerant Control for Thruster-Assisted Position Mooring System

    Nguyen, Trong Dong; Blanke, Mogens; Sørensen, Asgeir

    2007-01-01

    Development of fault-tolerant control systems is crucial to maintain safe operation of o®shore installations. The objective of this paper is to develop a fault- tolerant control for thruster-assisted position mooring (PM) system with faults occurring in the mooring lines. Faults in line's pretens......Development of fault-tolerant control systems is crucial to maintain safe operation of o®shore installations. The objective of this paper is to develop a fault- tolerant control for thruster-assisted position mooring (PM) system with faults occurring in the mooring lines. Faults in line......'s pretension or line breaks will degrade the performance of the positioning of the vessel. Faults will be detected and isolated through a fault diagnosis procedure. When faults are detected, they can be accommodated through the control action in which only parameter of the controlled plant has to be updated to...

  1. A Study on Turbo-rotor Multi-fault Diagnosis Based on a Neural Network

    SUN Shou-qun; ZHAO San-xing; ZHANG Wei; CHANG Xin-long

    2003-01-01

    The multi-fault phenomena are common in the turbo-rotor system of a liquid rocket engine. As it has many excellent qualities, the neural network might be used to solve the problems of multi-fault diagnosis of a turbo-rotor system. First, the feature expression of a common turbo-rotor fault was studied in order to build up the standard fault pattern and satisfy the need of neural network studying and diagnosing. Then, the turbo-rotor fault identification and diagnosis problems were investigated by using a BP(back-propagation) neural network. According to the BP neural network problems, the parallel BP neural network method of multi-fault diagnosis and classification was presented and investigated. The results indicated that the parallel BP neural network method could solve the turbo-rotor multi-fault diagnosis problems.

  2. Data-driven design of fault diagnosis systems nonlinear multimode processes

    Haghani Abandan Sari, Adel

    2014-01-01

    In many industrial applications early detection and diagnosis of abnormal behavior of the plant is of great importance. During the last decades, the complexity of process plants has been drastically increased, which imposes great challenges in development of model-based monitoring approaches and it sometimes becomes unrealistic for modern large-scale processes. The main objective of Adel Haghani Abandan Sari is to study efficient fault diagnosis techniques for complex industrial systems using process historical data and considering the nonlinear behavior of the process. To this end, different methods are presented to solve the fault diagnosis problem based on the overall behavior of the process and its dynamics. Moreover, a novel technique is proposed for fault isolation and determination of the root-cause of the faults in the system, based on the fault impacts on the process measurements. Contents Process monitoring Fault diagnosis and fault-tolerant control Data-driven approaches and decision making Target...

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

    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.

  4. Fault Diagnosis and Fault Tolerant Control with Application on a Wind Turbine Low Speed Shaft Encoder

    Odgaard, Peter Fogh; Sardi, Hector Eloy Sanchez; Escobet, Teressa;

    2015-01-01

    In recent years, individual pitch control has been developed for wind turbines, with the purpose of reducing blade and tower loads. Such algorithms depend on reliable sensor information. The azimuth angle sensor, which positions the wind turbine rotor in its rotation, is quite important. This sen...... due to individual pitch control algorithm operating with faulty azimuth angle inputs. The proposed approach is evaluated on a wind turbine benchmark model, which is based on the FAST aero-elastic code provided by NREL.......In recent years, individual pitch control has been developed for wind turbines, with the purpose of reducing blade and tower loads. Such algorithms depend on reliable sensor information. The azimuth angle sensor, which positions the wind turbine rotor in its rotation, is quite important. This...... sensor has to be correct as blade pitch actions should be different at different azimuth angle as the wind speed varies within the rotor field due to different phenomena. A scheme detecting faults in this sensor has previously been designed for the application of a high end fault diagnosis and fault...

  5. Research and design of distributed intelligence fault diagnosis system in nuclear power plant

    In order to further reduce the misoperation after the faults occurring of nuclear power plant, according to the function distribution of nuclear power equipment and the distributed control features of digital instrument control system, a nuclear power plant distributed condition monitoring and fault diagnosis system was researched and designed. Based on decomposition-integrated diagnostic thinking, a fuzzy neural network and RBF neural network was presented to do the distributed local diagnosis and multi-source information fusion technology for the global integrated diagnosis. Simulation results show that the developed distributed status monitoring and fault diagnosis system can diagnose more typical accidents of PWR to provide effective diagnosis and operation information. (authors)

  6. Research on Satellite Fault Diagnosis and Prediction Using Multi-modal Reasoning

    YangTianshe; SunYanhong; CaoYuping

    2004-01-01

    Diagnosis and prediction of satellite fault are more difficult than that of other equipment due to the complex structure of satellites and the presence of muhi-excite sources of satellite faults. Generally, one kind of reasoning model can only diagnose and predict one kind of satellite faults. In this paper the author introduces an application of a new method using multi-modal reasoning to diagnose and predict satellite faults. The method has been used in the development of knowledge-based satellite fault diagnosis and recovery system (KSFDRS) successfully. It is shown that the method is effective.

  7. Open-Switch Fault Diagnosis and Fault Tolerant for Matrix Converter with Finite Control Set-Model Predictive Control

    Peng, Tao; Dan, Hanbing; Yang, Jian;

    2016-01-01

    topology and a cost function to select the best switching state for the next sampling period. The proposed fault diagnosis method is realized by monitoring the load currents and judging the switching state to locate the faulty switch. Compared to the conventional modulation strategies such as carrier......To improve the reliability of the matrix converter (MC), a fault diagnosis method to identify single open-switch fault is proposed in this paper. The introduced fault diagnosis method is based on finite control set-model predictive control (FCS-MPC), which employs a time-discrete model of the MC......-based modulation method, indirect space vector modulation and optimum Alesina-Venturini, the FCS-MPC has known and unchanged switching state in a sampling period. It is simpler to diagnose the exact location of the open switch in MC with FCS-MPC. To achieve better quality of the output current under single open-switch...

  8. Open-switch fault diagnosis and fault tolerant matrix converter with finite control set-model predictive control

    Peng, Tao; Dan, Hanbing; Yang, Jian; Deng, Hui; Zhu, Qi; Wang, Chunsheng; Gui, Weihua; Guerrero, Josep M.

    2016-01-01

    To improve the reliability of the matrix converter (MC), a fault diagnosis method to identify single open-switch fault is proposed in this paper. The introduced fault diagnosis method is based on finite control set-model predictive control (FCS-MPC), which employs a time-discrete model of the MC...... topology and a cost function to select the best switching state for the next sampling period. The proposed fault diagnosis method is realized by monitoring the load currents and judging the switching state to locate the faulty switch. Compared to the conventional modulation strategies such as carrier......-based modulation method, indirect space vector modulation and optimum Alesina-Venturini, the FCS-MPC has known and unchanged switching state in a sampling period. It is simpler to diagnose the exact location of the open switch in MC with FCS-MPC. To achieve better quality of the output current under single open...

  9. Knowledge-based fault diagnosis system for refuse collection vehicle

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle

  10. Condition Monitoring and Fault Diagnosis for an Antifalling Safety Device

    Guangxiang Yang

    2015-01-01

    Full Text Available There is a constant need for the safe operation and reliability of antifalling safety device (AFSD of an elevator. This paper reports an experimental study on rotation speed and catching torque monitoring and fault diagnosis of an antifalling safety device in a construction elevator. Denoising the signal using wavelet transform is presented in this paper. Based on the denoising effects for several types of wavelets, the sym8 wavelet basis, which introduces the high order approximation and an adaptive threshold, is employed for denoising the signal. The experimental result shows a maximum data error reduction of 7.5% is obtained and SNRs (signal-to-noise ratio of rotation speed and catching torque are improved for 3.9% and 6.4%, respectively.

  11. Knowledge-based fault diagnosis system for refuse collection vehicle

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y. [Centre of Advanced Research on Energy, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka (Malaysia)

    2015-05-15

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  12. Knowledge-based fault diagnosis system for refuse collection vehicle

    Tan, CheeFai; Juffrizal, K.; Khalil, S. N.; Nidzamuddin, M. Y.

    2015-05-01

    The refuse collection vehicle is manufactured by local vehicle body manufacturer. Currently; the company supplied six model of the waste compactor truck to the local authority as well as waste management company. The company is facing difficulty to acquire the knowledge from the expert when the expert is absence. To solve the problem, the knowledge from the expert can be stored in the expert system. The expert system is able to provide necessary support to the company when the expert is not available. The implementation of the process and tool is able to be standardize and more accurate. The knowledge that input to the expert system is based on design guidelines and experience from the expert. This project highlighted another application on knowledge-based system (KBS) approached in trouble shooting of the refuse collection vehicle production process. The main aim of the research is to develop a novel expert fault diagnosis system framework for the refuse collection vehicle.

  13. Feature evaluation and extraction based on neural network in analog circuit fault diagnosis

    Yuan Haiying; Chen Guangju; Xie Yongle

    2007-01-01

    Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit.The feature evaluation and extraction methods based on neural network are presented.Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently.The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency.A fault diagnosis illustration validated this method.

  14. Surveillance and fault diagnosis for power plants in the Netherlands: operational experience

    Nuclear Power Plant (NPP) surveillance and fault diagnosis systems in Dutch Borssele (PWR) and Dodewaard (BWR) power plants are summarized. Deterministic and stochastic models and artificial intelligence (AI) methodologies effectively process the information from the sensors. The processing is carried out by means of methods and algorithms that are collectively referred to Power Reactor Noise Fault Diagnosis. Two main schemes used are failure detection and instrument fault detection. In addition to conventional and advanced modern fault diagnosis methodologies involved, also the applications of emerging technologies in Dutch reactors are given and examples from operational experience are presented. (author)

  15. A NEW APPROACH OF SIMULTANEOUS FAULTS DIAGNOSIS BASED ON RANDOM SETS AND DSMT

    Li Zhiliang; Xu Xiaobin; Wen Chenglin

    2009-01-01

    Simultaneous faults often occur in running equipments, in order to solve the problems of the simultaneous faults, a new approach based on random sets and Dezert-Smarandache Theory (DSmT) is proposed in this paper. Firstly, the simultaneous faults' model is built based on the generalized frame of discernment in DSmT. Secondly, according to the unified description of combination rules in evidence reasoning based on random sets, a new combination rule for simultaneous faults diagnosis is proposed. Thirdly, according to the working characteristics and environment of the sensors used to acquire fault characteristic information, a new method to construct basic probability assignment function is proposed based on membership. Finally, diagnosis result is obtained by use of the new combination rule combined with decision rules. A case pertaining to the fault diagnosis for a multi-function rotor test-bed is given, and the result shows that the proposed diagnosis approach is feasible and efficient.

  16. Towards self-tuning residual generators for UAV control surface fault diagnosis

    Blanke, Mogens; Hansen, Søren

    2013-01-01

    Control surface fault diagnosis is essential for timely detection of manoeuvring and stability risks for an unmanned aircraft. Timely detection is crucial since control surface related faults impact stability of flight and safety. Reliable diagnosis require well fitting dynamical models but with ...... of test flights with different members of a population of UAVs that have inherent model uncertainty from one member to another and from one flight to another. Events with actual faults on control surfaces demonstrates the efficacy of the approach....

  17. Lossy Electric Transmission Line Soft Fault Diagnosis: an Inverse Scattering Approach

    Tang, Huaibin; Zhang, Qinghua

    2010-01-01

    In this paper, the diagnosis of soft faults in lossy electric transmission lines is studied through the inverse scattering approach. The considered soft faults are modeled as continuous spatial variations of distributed characteristic parameters of transmission lines. The diagnosis of such faults from reflection and transmission coefficients measured at the ends of a line can be formulated as an inverse problem. The relationship between this inverse problem and the inverse scattering theory h...

  18. Application of data fusion method to fault diagnosis of nuclear power plant

    XIE Chun-li; XIA Hong; LIU Yong-kuo

    2005-01-01

    The work condition of nuclear power plant (NPP) is very bad, which makes it has faults easily. In order to diagnose the faults real time, the fusion diagnosis system is built. The data fusion fault diagnosis system adopts data fusion method and divides the fault diagnosis into three levels, which are data fusion level, feature level and decision level. The feature level uses three parallel neural networks whose structures are the same. The purpose of using neural networks is mainly to get basic probability assignment (BPA) of D-S evidence theory, and the neural networks in feature level are used for local diagnosis. D-S evidence theory is adopted to integrate the local diagnosis results in decision level. The reactor coolant system is the study object and we choose 2# steam generator U-tubes break of the reactor coolant system as a diagnostic example. The experiments prove that the fusion diagnosis system can satisfy the fault diagnosis requirement of complicated system, and verify that the fusion fault diagnosis system can realize the fault diagnosis of NPP on line timely.

  19. Controller modification applied for active fault detection

    Niemann, Hans Henrik; Stoustrup, Jakob; Poulsen, Niels Kjølstad

    2014-01-01

    This paper is focusing on active fault detection (AFD) for parametric faults in closed-loop systems. This auxiliary input applied for the fault detection will also disturb the external output and consequently reduce the performance of the controller. Therefore, only small auxiliary inputs are used...... modify the feedback controller with a minor effect on the external output in the fault free case. Further, in the faulty case, the signature of the auxiliary input can be optimized. This is obtained by using a band-pass filter for the YJBK parameter that is only effective in a small frequency range where...... the frequency for the auxiliary input is selected. This gives that it is possible to apply an auxiliary input with a reduced amplitude. An example is included to show the results....

  20. A Fault Diagnosis Expert System for a Heavy Motor Used in a Rolling Mill

    2002-01-01

    A fault diagnosis expert system for a heavy motor used in a rolling mill is established in this paper. The fault diagnosis knowledge base was built, and its knowledge was represented by production rules. The knowledge base includes daily inspection system, brief diagnosis system and precise diagnosis system. A pull-down menu was adopted for the management of the knowledge base. The system can run under the help of expert system development tools. Practical examples show that the expert system can diagnose faults rapidly and precisely.

  1. An analytical model of electronic fault diagnosis on extension of the dependency theory

    Based on the D-matrix model, the dependency theory is widely used in the field of fault diagnosis to model the fault flows in complex electronic systems. However, the traditional dependency model can only handle a single fault; it fails to recognize and diagnose multiple faults. In addition, it is not tolerant with system structural or functional changes. These inherent weaknesses of the traditional dependency theory may lead to unsatisfactory acquisition of the diagnosis results. To solve the problem, an improved dependency model is invented as novel analytic diagnosis model to better describe the relationships between faults and tests. The system fault diagnosis based on the improved dependency model is formulated as an optimization problem with binary logic operations where all the fault hypotheses are tested. The calculation process consists of three steps: establishment of the objective function, determination of the nominal states, and determination of the expected states. Finally, the proposed method is demonstrated via an avionic processor case using the improved dependency model. The optimization-based fault diagnosis problem is formulated and the optimal solution is obtained. The diagnosis result demonstrates that the proposed method is successful on performance assessment and fault diagnosis. - Highlights: • An improved dependency model is proposed considering the drawbacks of the traditional dependency model. • Fault diagnosis is formulated as an optimization problem with binary logic operations • The model can make estimations of the fault sections, as well as the malfunctioned switches and test points. • The proposed method is demonstrated via an avionic processor case using the improved dependency model

  2. InSAR measurements around active faults: creeping Philippine Fault and un-creeping Alpine Fault

    Fukushima, Y.

    2013-12-01

    Recently, interferometric synthetic aperture radar (InSAR) time-series analyses have been frequently applied to measure the time-series of small and quasi-steady displacements in wide areas. Large efforts in the methodological developments have been made to pursue higher temporal and spatial resolutions by using frequently acquired SAR images and detecting more pixels that exhibit phase stability. While such a high resolution is indispensable for tracking displacements of man-made and other small-scale structures, it is not necessarily needed and can be unnecessarily computer-intensive for measuring the crustal deformation associated with active faults and volcanic activities. I apply a simple and efficient method to measure the deformation around the Alpine Fault in the South Island of New Zealand, and the Philippine Fault in the Leyte Island. I use a small-baseline subset (SBAS) analysis approach (Berardino, et al., 2002). Generally, the more we average the pixel values, the more coherent the signals are. Considering that, for the deformation around active faults, the spatial resolution can be as coarse as a few hundred meters, we can severely 'multi-look' the interferograms. The two applied cases in this study benefited from this approach; I could obtain the mean velocity maps on practically the entire area without discarding decorrelated areas. The signals could have been only partially obtained by standard persistent scatterer or single-look small-baseline approaches that are much more computer-intensive. In order to further increase the signal detection capability, it is sometimes effective to introduce a processing algorithm adapted to the signal of interest. In an InSAR time-series processing, one usually needs to set the reference point because interferograms are all relative measurements. It is difficult, however, to fix the reference point when one aims to measure long-wavelength deformation signals that span the whole analysis area. This problem can be

  3. Fault diagnosis of rocket engine ground testing bed with self-organizing maps (SOMs)

    ZHU Ning; FENG Zhi-gang; WANG Qi

    2009-01-01

    To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed, a fault diagnosis approach based on self-organizing map (SOM) is proposed. The SOM projects the multidimensional ground testing bed data into a two-dimensional map. Visualization of the SOM is used to cluster the ground testing bed data. The out map of the SOM is divided to several regions. Each region is represented for one fault mode. The fault mode of testing data is determined according to the region of their labels belonged to. The method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault states. The results show that it is a reliable and effective method for fault diagnosis with good visualization property.

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

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

    2008-03-01

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

  5. Outline of a fault diagnosis system for a large-scale board machine

    Jämsä-Jounela, Sirkka-Liisa; Tikkala, Vesa-Matti; Zakharov, Alexey; Pozo Garcia, Octavio; Laavi, Helena; Myller, Tommi; Kulomaa, Tomi; Hämäläinen, Veikko

    2013-01-01

    Global competition forces process industries to continuously optimize plant operation. One of the latest trends for efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most li...

  6. Fault Self-Diagnosis for Modular Robotic Systems Using M-Lattice Modules

    Enguang Guan

    2015-04-01

    Full Text Available In the domain of modular robotic systems, self-configuration, self-diagnosis and self-repair are known to be highly challenging tasks. This paper presents a novel fault self-diagnosis strategy which consists of two parts: fault detection and fault message transmission. In fault detection, a bionic synchronization ‘healthy heartbeat’ method is used to guarantee the high efficiency of the exogenous detection strategy. For fault message transmission, the Dijkstra method is modified to be capable of guiding the passage of fault messages along the optimal path. In a modular robotic system, fault message transmission depends mainly on local communications between adjacent modules, so there is no need for global broadcast information. Computational simulations of one system form, M-Lattice, have demonstrated the practical effectiveness of the proposed strategy. The strategy should be applicable in modular robotic systems in general.

  7. Remarks on Urban Active Fault Exploration and Assessment of Fault Activity

    Deng Qidong; Lu Zaoxun; Yang Zhu'en

    2008-01-01

    According to the practice of urban active fault exploration and associated fault activity assessment conducted in recent years, this paper summarizes the problems encountered in geological, geomorphological, geochemical and geophysical surveys, and proposes the following means and suggestions to solve these problems. To determine the most recent faults or fault zones, emphasis should be placed on identifying the youngest active faults and offset geomorphology. To understand the history of faulting and to discover the latest offset event, it is suggested that geophysical prospecting, drilling and trenching be conducted on one profile.Because of significant uncertainties in late Quaternary dating, we advise systematic sampling and the use of multiple dating methods. Shallow seismic reflection has been proven to be the most useful method in urban active fault exploration. However, there is a pressing need to increase the quality of data acquisition and processing to obtain high resolution images so as to enhance our ability to identify active faults. The combination of seismic P-wave reflection and S-wave reflection methods is proved to be a powerful means to investigate the tectonic environments of the deep crust.

  8. Fault detection and diagnosis for complex multivariable processes using neural networks

    Development of a reliable fault diagnosis method for large-scale industrial plants is laborious and often difficult to achieve due to the complexity of the targeted systems. The main objective of this thesis is to investigate the application of neural networks to the diagnosis of non-catastrophic faults in an industrial nuclear fuel processing plant. The proposed methods were initially developed by application to a simulated chemical process prior to further validation on real industrial data. The diagnosis of faults at a single operating point is first investigated. Statistical data conditioning methods of data scaling and principal component analysis are investigated to facilitate fault classification and reduce the complexity of neural networks. Successful fault diagnosis was achieved with significantly smaller networks than using all process variables as network inputs. Industrial processes often manufacture at various operating points, but demonstrated applications of neural networks for fault diagnosis usually only consider a single (primary) operating point. Developing a standard neural network scheme for fault diagnosis at all operating points would be usually impractical due to the unavailability of suitable training data for less frequently used (secondary) operating points. To overcome this problem, the application of a single neural network for the diagnosis of faults operating at different points is investigated. The data conditioning followed the same techniques as used for the fault diagnosis of a single operating point. The results showed that a single neural network could be successfully used to diagnose faults at operating points other than that it is trained for, and the data conditioning significantly improved the classification. Artificial neural networks have been shown to be an effective tool for process fault diagnosis. However, a main criticism is that details of the procedures taken to reach the fault diagnosis decisions are embedded in

  9. Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

    Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

  10. Fault Diagnosis and Fault-Tolerant Control of Wind Turbines via a Discrete Time Controller with a Disturbance Compensator

    Yolanda Vidal

    2015-05-01

    Full Text Available This paper develops a fault diagnosis (FD and fault-tolerant control (FTC of pitch actuators in wind turbines. This is accomplished by combining a disturbance compensator with a controller, both of which are formulated in the discrete time domain. The disturbance compensator has a dual purpose: to estimate the actuator fault (which is used by the FD algorithm and to design the discrete time controller to obtain an FTC. That is, the pitch actuator faults are estimated, and then, the pitch control laws are appropriately modified to achieve an FTC with a comparable behavior to the fault-free case. The performance of the FD and FTC schemes is tested in simulations with the aero-elastic code FAST.

  11. Robust Fault Diagnosis Design for Linear Multiagent Systems with Incipient Faults

    Jingping Xia

    2015-01-01

    Full Text Available The design of a robust fault estimation observer is studied for linear multiagent systems subject to incipient faults. By considering the fact that incipient faults are in low-frequency domain, the fault estimation of such faults is proposed for discrete-time multiagent systems based on finite-frequency technique. Moreover, using the decomposition design, an equivalent conclusion is given. Simulation results of a numerical example are presented to demonstrate the effectiveness of the proposed techniques.

  12. Nuclear power plant pressurizer fault diagnosis using fuzzy signed-digraph method

    In this study, The Fuzzy Signed Digraph method which has been researched and applied to the chemical process is improved and applied to the fault diagnosis of the pressurizer in nuclear power plants. The Fuzzy Signed-Digraph (FSD) is the method which applies the fuzzy number to the Signed-Digraph (SDG) method. The current SDG methods have many merits as follows: (1) SDG method can directly use the value of sensors not the alarm to the fault diagnosis. (2) This method can diagnose the fault independent on the pattern. (3) This method can diagnose the faults fastly because the method uses the cause-effect relation instead of the complex control equation among the variables. But, they are not proper to be applied to the diagnosis of the multi-faults and to diagnose faults on real time. It is because the unmeasured nodes in those methods must be connected to each other in order to find out the single fault under the single-fault assumption. These methods need long CPU time and cannot be applied to the multi-faults diagnosis. We propose a method in which the values of the unmeasured nodes are calculated from the relations between the unmeasured nodes and the measured nodes. By using this method, the CPU time for diagnosis can be reduced. This CPU time reduction makes the real-time diagnosis possible. This method can also be applied for the multi-faults diagnosis. This method is applied to the diagnosis of the pressurizer of the nuclear power plant KORI-2 in Korea. (author)

  13. Study on a data source for fault diagnosis of nuclear power plant based on RELAP5

    The model of nuclear power plant primary and secondary circuits was established, taking Qinshan Unit 1 as the object. The fault of SG U-tube rupture was calculated based on RELAP5 for solving the fault data source problem. Through analyzing the calculation results, we can know that simulation node is reasonable, input data card is exact, and data based on this model is credible. The designed data combining with fault diagnosis system has been debugged. The result indicates that the data is exact and enough and can be one of the databases for study on fault diagnosis of nuclear' power plant. (authors)

  14. Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot

    Zhao, Bo; Skjetne, Roger; Blanke, Mogens;

    2014-01-01

    A particle filter based robust navigation with fault diagnosis is designed for an underwater robot, where 10 failure modes of sensors and thrusters are considered. The nominal underwater robot and its anomaly are described by a switchingmode hidden Markov model. By extensively running a particle...... filter on the model, the fault diagnosis and robust navigation are achieved. Closed-loop full-scale experimental results show that the proposed method is robust, can diagnose faults effectively, and can provide good state estimation even in cases where multiple faults occur. Comparing with other methods...

  15. Active fault traces along Bhuj Fault and Katrol Hill Fault, and trenching survey at Wandhay, Kachchh, Gujarat, India

    Michio Morino; Javed N Malik; Prashant Mishra; Chandrashekhar Bhuiyan; Fumio Kaneko

    2008-06-01

    Several new active fault traces were identified along Katrol Hill Fault (KHF).A new fault (named as Bhuj Fault,BF)that extends into the Bhuj Plain was also identified.These fault traces were identified based on satellite photo interpretation and field survey.Trenches were excavated to identify the paleoseismic events,pattern of faulting and the nature of deformation.New active fault traces were recognized about 1 km north of the topographic boundary between the Katrol Hill and the plain area.The fault exposure along the left bank of Khari River with 10 m wide shear zone in the Mesozoic rocks and showing displacement of the overlying Quaternary deposits is indicative of continued tectonic activity along the ancient fault.The E-W trending active fault traces along the KHF in the western part changes to NE-SW or ENE-WSW near Wandhay village. Trenching survey across a low scarp near Wandhay village reveals three major fault strands F1, F2,and F3.These fault strands displaced the older terrace deposits comprising Sand,Silt and Gravel units along with overlying younger deposits from units 1 to 5 made of gravel,sand and silt. Stratigraphic relationship indicates at least three large magnitude earthquakes along KHF during Late Holocene or recent historic past.

  16. Active tectonics of Himalayan Frontal Fault system

    Thakur, V. C.

    2013-04-01

    In the Sub-Himalayan zone, the frontal Siwalik range abuts against the alluvial plain with an abrupt physiographic break along the Himalayan Frontal Thrust (HFT), defining the present-day tectonic boundary between the Indian plate and the Himalayan orogenic prism. The frontal Siwalik range is characterized by large active anticline structures, which were developed as fault propagation and fault-bend folds in the hanging wall of the HFT. Fault scarps showing surface ruptures and offsets observed in excavated trenches indicate that the HFT is active. South of the HFT, the piedmont zone shows incipient growth of structures, drainage modification, and 2-3 geomorphic depositional surfaces. In the hinterland between the HFT and the MBT, reactivation and out-of-sequence faulting displace Late Quaternary-Holocene sediments. Geodetic measurements across the Himalaya indicate a ~100-km-wide zone, underlain by the Main Himalayan Thrust (MHT), between the HFT and the main microseismicity belt to north is locked. The bulk of shortening, 15-20 mm/year, is consumed aseismically at mid-crustal depth through ductile by creep. Assuming the wedge model, reactivation of the hinterland faults may represent deformation prior to wedge attaining critical taper. The earthquake surface ruptures, ≥240 km in length, interpreted on the Himalayan mountain front through paleoseismology imply reactivation of the HFT and may suggest foreland propagation of the thrust belt.

  17. Expert system application to fault diagnosis and procedure synthesis

    Two knowledge based systems have been developed to detect plant faults, to validate sensor data in a nuclear power plant, and to synthesize procedures to assure safety goals are met when a transient occurs. These two systems are being combined into a single system through a Plant Status Monitoring System (PSMS) and a common database accessed by all the components of the integrated system. The system is designed to sit on top of an existing Safety Parameter Display System (SPDS), and to use the existing data acquisition and data control software of the SPDS. The integrated system will communicate with the SPDS software through a single database. This database will receive sensor values and equipment status indications in a form acceptable to the knowledge based system and according to an update plan designed specifically for the system. PSMS will monitor the plant status by scanning the system database continuously, and will respond to the changes in the plant data according to three priorities: direct indications of plant changes that can be resolved by simple actions such as assuring that a back up pump has been started; more complex indications that lead to entry conditions for predefined event procedures or other individual system recovery procedures; and plant conditions that are defined as the entry points to the symptomatic emergency procedure guidelines (EPGs). At present, the knowledge base is being built using scenarios run on a BWR-6 plant referenced simulator. Concurrently, software is being developed for performing diagnosis and procedure synthesis

  18. Early Oscillation Detection for DC/DC Converter Fault Diagnosis

    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.

  19. Method of fault diagnosis in nuclear power plant base on genetic algorithm and knowledge base

    Via using the knowledge base, combining Genetic Algorithm and classical probability and contraposing the characteristic of the fault diagnosis of NPP. The authors put forward a method of fault diagnosis. In the process of fault diagnosis, this method contact the state of NPP with the colony in GA and transform the colony to get the individual that adapts to the condition. On the 950MW full size simulator in Beijing NPP simulation training center, experimentation shows it has comparative adaptability to the imperfection of expert knowledge, illusive signal and other instance

  20. Neural network fault diagnosis method optimization with rough set and genetic algorithms

    SUN Hong-yan; XIE Zhi-jiang; OUYANG Qi

    2006-01-01

    Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

  1. The application of neural networks for fault diagnosis in nuclear reactors

    In recent years considerable work have been done in the field of neural networks due to the recent development of effective learning algorithms, and the results of their applications have suggested that they can provide useful tools for solving practical problems. Artificial neural networks are mathematical models of theorized mind and brain activity. They are aimed to explore and reproduce human information processing tasks such as speech, vision, knowledge processing and control. The possibility of using artificial neural networks for fault and accident diagnosis in the Loss Of Fluid Test (LOFT) reactor, a small scale pressurised water reactor, is examined and explained in the paper. (author)

  2. A review on empirical mode decomposition in fault diagnosis of rotating machinery

    Lei, Yaguo; Lin, Jing; He, Zhengjia; Zuo, Ming J.

    2013-02-01

    Rotating machinery covers a broad range of mechanical equipment and plays a significant role in industrial applications. It generally operates under tough working environment and is therefore subject to faults, which could be detected and diagnosed by using signal processing techniques. Empirical mode decomposition (EMD) is one of the most powerful signal processing techniques and has been extensively studied and widely applied in fault diagnosis of rotating machinery. Numerous publications on the use of EMD for fault diagnosis have appeared in academic journals, conference proceedings and technical reports. This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics. First, the EMD method is briefly introduced, the usefulness of the method is illustrated and the problems and the corresponding solutions are listed. Then, recent applications of EMD to fault diagnosis of rotating machinery are summarized in terms of the key components, such as rolling element bearings, gears and rotors. Finally, the outstanding open problems of EMD in fault diagnosis are discussed and potential future research directions are identified. It is expected that this review will serve as an introduction of EMD for those new to the concepts, as well as a summary of the current frontiers of its applications to fault diagnosis for experienced researchers.

  3. A summary of the active fault investigation in the extension sea area of Kikugawa fault and the Nishiyama fault , N-S direction fault in south west Japan

    Abe, S.

    2010-12-01

    In this study, we carried out two sets of active fault investigation by the request from Ministry of Education, Culture, Sports, Science and Technology in the sea area of the extension of Kikugawa fault and the Nishiyama fault. We want to clarify the five following matters about both active faults based on those results. (1)Fault continuity of the land and the sea. (2) The length of the active fault. (3) The division of the segment. (4) Activity characteristics. In this investigation, we carried out a digital single channel seismic reflection survey in the whole area of both active faults. In addition, a high-resolution multichannel seismic reflection survey was carried out to recognize the detailed structure of a shallow stratum. Furthermore, the sampling with the vibrocoring to get information of the sedimentation age was carried out. The reflection profile of both active faults was extremely clear. The characteristics of the lateral fault such as flower structure, the dispersion of the active fault were recognized. In addition, from analysis of the age of the stratum, it was recognized that the thickness of the sediment was extremely thin in Holocene epoch on the continental shelf in this sea area. It was confirmed that the Kikugawa fault extended to the offing than the existing results of research by a result of this investigation. In addition, the width of the active fault seems to become wide toward the offing while dispersing. At present, we think that we can divide Kikugawa fault into some segments based on the distribution form of the segment. About the Nishiyama fault, reflection profiles to show the existence of the active fault was acquired in the sea between Ooshima and Kyushu. From this result and topographical existing results of research in Ooshima, it is thought that Nishiyama fault and the Ooshima offing active fault are a series of structure. As for Ooshima offing active fault, the upheaval side changes, and a direction changes too. Therefore, we

  4. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Reaction Wheels

    Baldi, P.; Blanke, Mogens; Castaldi, P.; Mimmo, N.; Simani, S.

    2015-01-01

    This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank of...... residual filters organized in a generalized scheme, followed by a fault estimation module consisting of a bank of adaptive estimation filters. The residuals are decoupled from aerodynamic disturbances thanks to the Nonlinear Geometric Approach. The use of Radial Basis Function Neural Networks is shown to...... allow design of generalized fault estimation filters, which do not need a priori information about the faults internal model. Simulation results with a detailed nonlinear spacecraft model, which includes disturbances, show that the proposed diagnosis scheme can deal with faults affecting both reaction...

  5. Sensor fault diagnosis for fast steering mirror system based on Kalman filter

    Wang, Hongju; Bao, Qiliang; Yang, Haifeng; Tao, Sunjie

    2015-10-01

    In this paper, to improve the reliability of a two-axis fast steering mirror system with minimum hardware consumption, a fault diagnosis method based on Kalman filter was developed. The dynamics model of the two-axis FSM was established firstly, and then the state-space form of the FSM was adopted. A bank of Kalman filters for fault detection was designed based on the state-space form. The effects of the sensor faults on the innovation sequence were investigated, and a decision approach called weighted sum-squared residual (WSSR) was adopted to isolate the sensor faults. Sensor faults could be detected and isolated when the decision statistics changed. Experimental studies on a prototype system show that the faulty sensor can be isolated timely and accurately. Meanwhile, the mathematical model of FSM system was used to design fault diagnosis scheme in the proposed method, thus the consumption of the hardware and space is decreased.

  6. Learning and case-based reasoning for faults diagnosis-aiding in nuclear power plants

    The aim of this thesis is the design of a faults diagnosis-aiding system in a nuclear facility of the Cea. Actually the existing system allows the optimization of the production processes in regular operating conditions. Meanwhile during accidental events, the alarms, managed by threshold, are bringing no relevant information. To increase the reliability and the safety, the human operator needs a faults diagnosis-aiding system. The developed system, SECAPI, combines problem solving techniques and automatic learning techniques, that allow the diagnosis and the the simulation of various faults happening on nuclear facilities. Its reasoning principle uses case-based and rules-based techniques. SECAPI owns a learning module which reads out knowledge connected with faults. It can then simulate various faults, using the inductive logical computing. SECAPI has been applied on a radioactive tritium treatment operating channel, at the Cea with good results. (A.L.B.)

  7. Condition Monitoring and Fault Diagnosis of Wet-Shift Clutch Transmission Based on Multi-technology

    Chen, Man; Wang, Liyong; Ma, Biao

    Based on the construction feature and operating principle of the wet-shift clutch transmission, the condition monitoring and fault diagnosis for the transmission of the tracklayer with wet-shift clutch were implemented with using the oil analysis technology, function parameter test method and vibration analysis technology. The new fault diagnosis methods were proposed, which are to build the gray modeling with the oil analysis data, and to test the function parameter of the clutch press, the rotate speed of each gear, the oil press of the steer system and lubrication system and the hydraulic torque converter. It's validated that the representative function signals were chosen to execute the condition monitoring analysis, when the fault symptoms were found, and the oil analysis data were used to apply the gray modeling to forecast the fault occurs time can satisfy the demand of the condition monitoring and fault diagnosis for the transmission regular work.

  8. Active Faulting and Quaternary Landforms Deformation Related to the Nain Fault

    Abolghasem Gourabi

    2011-01-01

    Full Text Available Problem statement: Landforms developed across terrain defining boundary the Nain fault have imprints of recent tectonic activity in the west region of Central Iran. Depositional landforms such as alluvial fans bear signatures of later phases of tectonic activity in the form of faulting of alluvial fan deposits and development of fault traces and scarps within 100 km long and a NW-SE-trending zone, 1000-2000 m wide. Approach: We are addressing the neotectonic landforms based on detailed field work carried out in the Nain exposed active fault segments which brought forward some outstanding morphtectonic evidence of quaternary tectonically activities. Tectonic geomorphology applied to the Nain fault suggests recent subsurface activity along the Nain fault and an interconnecting faulting network of roughly NW-SE-trending, right-lateral, strike-slip segments and mostly NW-SE-oriented, transtensional to normal faults. Results: Evidence for recent activity is provided by faulted Pleistocene-Holocene deposits, fresh scarps in Late Quaternary deposits, 8-15 m lateral offsets locally affecting the drainage pattern of the area, ground creeping, aligning of series of spring faults, deflected streams and fault trace over recent alluvial fans. The existences of strike-slip faults system in the Nain area can be implications for seismic hazard. Conclusion: Motion along these structures suggests, in fact, that cumulative displacements include normal, transtensional and strike-slip components. Based on all evidence of active tectonics, earthquake risk and occurrence area is significant.

  9. Lateral migration of fault activity in Weihe basin

    冯希杰; 戴王强

    2004-01-01

    Lateral migration of fault activity in Weihe basin is a popular phenomenon and its characteristics are also typical.Taking the activity migrations of Wangshun Mountain piedmont fault toward Lishan piedmont fault and Weinan platform front fault, Dabaopi-Niujiaojian fault toward Shenyusi-Xiaojiazhai fault, among a serial of NE-trending faults from Baoji city to Jingyang County as examples, their migration time and process are analyzed and discussed in the present paper. It is useful for further understanding the structure development and physiognomy evolution history of Weihe basin.

  10. Lateral migration of fault activity in Weihe basin

    Feng, Xi-Jie; Dai, Wang-Qiang

    2004-03-01

    Lateral migration of fault activity in Weihe basin is a popular phenomenon and its characteristics are also typical. Taking the activity migrations of Wangshun Mountain piedmont fault toward Lishan piedmont fault and Weinan platform front fault, Dabaopi-Niujiaojian fault toward Shenyusi-Xiaojiazhai fault, among a serial of NE-trending faults from Baoji city to Jingyang County as examples, their migration time and process are analyzed and discussed in the present paper. It is useful for further understanding the structure development and physiognomy evolution history of Weihe basin.

  11. Fault Diagnosis for Electrical Distribution Systems using Structural Analysis

    Knüppel, Thyge; Blanke, Mogens; Østergaard, Jacob

    Fault-tolerance in electrical distribution relies on the ability to diagnose possible faults and determine which components or units cause a problem or are close to doing so. Faults include defects in instrumentation, power generation, transformation and transmission. The focus of this paper is the...... structure graph. This paper shows how three-phase networks are modelled and analysed using structural methods, and it extends earlier results by showing how physical faults can be identified such that adequate remedial actions can be taken. The paper illustrates a feasible modelling technique for structural...... analysis of power systems, it demonstrates detection and isolation of failures in a network, and shows how typical faults are diagnosed. Nonlinear fault simulations illustrate the results....

  12. Fault diagnosis in neutral point indirectly grounded system based on information fusion

    于飞; 鞠丽叶; 刘喜梅; 崔平远; 钟秋海

    2003-01-01

    In neutral point indirectly grounded systems, phase-to-ground fault is putting new demands on fault diagnosis technology. Information fusion is applied to detect the phase-to-ground fault, which integrates several sources of information, including line current, line voltage, zero sequence current and voltage, and quintic harmonic wave component. This method is testified through the simulation of Matlab. Simulation results show that the precision and reliability of the detection has been greatly increased.

  13. Automatic diagnosis of software functional faults by means of inferred behavioral models

    Pastore, .

    2010-01-01

    Software faults have a relevant impact on today economy. Modern software systems are often composed by different third-party components, of which developers have only a partial knowledge because of the absence of source code or complete specifications. The lack of complete knowledge about the integrated components is the principal cause of faults, and makes fault diagnosis difficult as well. The increase of maintenance costs and services unavailability is one of the consequences of difficult ...

  14. State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis

    MRUGALSKI, M.; WITCZAK, M.

    2012-01-01

    Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation ...

  15. Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis

    Silvio Simani; Saverio Farsoni; Paolo Castaldi

    2014-01-01

    In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the reliability of the available measurements. In this work, adata-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosissystem. Fuzzy theory jointly with the Frisch identificati...

  16. Handling Fault Diagnosis Problem of Linear-Analogue Circuits with Voltage Phasor Measurement

    2014-01-01

    This paper proposes a novel method to estimate the influence of hard-fault in linear-analogue circuit system based on the measurement of voltage phasor with assistant branch introduced. Furthermore, a new fault diagnosis strategy based on the voltage phasor modeling is established, and the tolerance influence on the corresponding voltage measurement is also discussed. The actual analogue circuit test shows us that the proposed method is effective and reliable to locate the accurate fault sign...

  17. Study and Design of Diaphragm Pump Vibration Detection Fault Diagnosis System Based on FFT

    Jia Yin

    2013-02-01

    Full Text Available This study has proposed a fault diagnosis system based on vibration detection. The system mainly includes four modules: signal acquisition module, signal processing module, state identification module, fault diagnosis and alarm module. The system uses CMSS 2200 acceleration sensor to collect vibration signals, processing spectrum with FFT (Fast Fourier Transform which is used effectively in current industry and finally achieve fault diagnosis and prediction for diaphragm pump. Through collection and analysis of the history signal data, set threshold value in the fault diagnosis system. According to the characteristics of different types, set the corresponding effective threshold value. The simulation results show that, the spectrum after FFT transformation processing, can really and effectively reflect equipment operating condition of the diaphragm. This system is not only simple and stable, but also can predict pump failure effectively, so that it reduces equipment downtime, plan maintenance time and unplanned maintenance time.

  18. Nuclear power plant fault diagnosis based on genetic-RBF neural network

    SHI Xiao-cheng; XIE Chun-ling; WANG Yuan-hui

    2006-01-01

    It is necessary to develop an automatic fault diagnosis system to avoid a possible nuclear disaster caused by an inaccurate fault diagnosis in the nuclear power plant by the operator. Because Radial Basis Function Neural Network (RBFNN) has the characteristics of optimal approximation and global approximation. The mixed coding of binary system and decimal system is introduced to the structure and parameters of RBFNN, which is trained in course of the genetic optimization. Finally, a fault diagnosis system according to the frequent faults in condensation and feed water system of nuclear power plant is set up. As a result, Genetic-RBF Neural Network (GRBFNN) makes the neural network smaller in size and higher in generalization ability. The diagnosis speed and accuracy are also improved.

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

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

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

    Zhao, Jinsong; Huang, Jianchao; Sun, Wei

    2008-11-01

    A fault detection and diagnosis framework is proposed in this paper for early fault detection and diagnosis (FDD) of municipal solid waste incinerators (MSWIs) in order to improve the safety and continuity of production. In this framework, principal component analysis (PCA), one of the multivariate statistical technologies, is used for detecting abnormal events, while rule-based reasoning performs the fault diagnosis and consequence prediction, and also generates recommendations for fault mitigation once an abnormal event is detected. A software package, SWIFT, is developed based on the proposed framework, and has been applied in an actual industrial MSWI. The application shows that automated real-time abnormal situation management (ASM) of the MSWI can be achieved by using SWIFT, resulting in an industrially acceptable low rate of wrong diagnosis, which has resulted in improved process continuity and environmental performance of the MSWI. PMID:18255276

  1. Joint Parametric Fault Diagnosis and State Estimation Using KF-ML Method

    Sun, Zhen; Yang, Zhenyu

    2014-01-01

    The paper proposes a new method for a kind of parametric fault online diagnosis with state estimation jointly. The considered fault affects not only the deterministic part of the system but also the random circumstance. The proposed method first applies Kalman Filter (KF) and Maximum Likelihood (ML...

  2. A Framework for Diagnosis of Critical Faults in Unmanned Aerial Vehicles

    Hansen, Søren; Blanke, Mogens; Adrian, Jens

    2014-01-01

    Unmanned Aerial Vehicles (UAVs) need a large degree of tolerance towards faults. If not diagnosed and handled in time, many types of faults can have catastrophic consequences if they occur during flight. Prognosis of faults is also valuable and so is the ability to distinguish the severity of the...... different faults in terms of both consequences and the frequency with which they appear. In this paper flight data from a fleet of UAVs is analysed with respect to certain faults and their frequency of appearance. Data is taken from a group of UAV's of the same type but with small differences in weight and...... handling due to different types of payloads and engines used. Categories of critical faults, that could and have caused UAV crashes are analysed and requirements to diagnosis are formulated. Faults in air system sensors and in control surfaces are given special attention. In a stochastic framework, and...

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

    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

  4. A Fault Diagnosis Mechanism for a Proactive Maintenance Scheme for Wireless Systems

    Walsh, Barbara; Farrell, Ronan

    2008-01-01

    This paper presents the fault diagnosis mechanism for a proactive maintenance scheme for wireless systems which is aimed at reducing the high operational costs encountered in the wireless industry by decreasing maintenance costs and system downtime. An on-line monitoring system, based on the aforementioned fault diagnosis mechanism, is used to identify performance degradation, as well as its possible sources, so as to ensure that maintenance occurs only when necessary.

  5. Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine

    Jian-Jiun Ding; Chun-Chieh Wang; Chiu-Wen Wu; Po-Hung Wu; Shuen-De Wu

    2012-01-01

    Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, multiscale permutation entropy (MPE) was introduced for feature extraction from faulty bearing vibration signals. After extracting feature vectors by MPE, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. Simulation results demonstr...

  6. Improved methods for incipient multiple fault diagnosis with application to nuclear power plant

    This paper proposes an improved method based on Chung's approach for multiple fault diagnosis in large-scale nuclear power plant. In this method, an improved approach is presented for selecting a most suspected faulty node among several candidates by using the characteristic of each fault propagation time from each suspected faulty node (device) to a sensor representing the clearest symptom. A way is shown for getting the dominant feed forward control loop which has multi-path. The corresponding fault diagnosis procedure is also given. As an illustrative example, the primary system in the Kori nuclear power plant unit has been taken up to show the usefulness of the proposed method

  7. Fault Detection and Diagnosis for Brine to Water Heat Pump Systems

    Vecchio, Daniel

    2014-01-01

    The overall objective of this thesis is to develop methods for fault detection and diagnosis for ground source heat pumps that can be used by servicemen to assist them to accurately detect and diagnose faults during the operation of the heat pump. The aim of this thesis is focused to develop two fault detection and diagnosis methods, sensitivity ratio and data-driven using principle component analysis. For the sensitivity ratio method model, two semi-empirical models for heat pump unit were b...

  8. Research on fault mode and diagnosis of methane sensor

    WANG Qi-jun; CHENG Jiu-long

    2008-01-01

    To improve the reliability of coal mine safety monitoring systems we have analyzed the characteristics of a methane sensor, an important component of the monitoring system of production safety in a coal mine and studied the main type and mode of faults when the sensor was used on-line. We introduced a new method based on artificial neural network to detect faults of me-thane sensors. In addition, using the output information of a single methane sensor, we established a sensor output model of a dy-namic non-linear neural network for on-line fault detection. Finally, the fault of the heating wire of the sensor was simulated, indi-cating that, when the methane sensor had a fault, the predicted output of the neural network clearly deviated from the actual output,exceeding the pre-set threshold and showing that a fault had occurred in the methane sensor. The result shows that the model has good convergence and stability, and is quite capable of meeting the requirements for on-line fault detection of methane sensors.

  9. Semi-Supervised Classification for Fault Diagnosis in Nuclear Power Plants

    Ma, Jian Ping; Jiang, Jin [University of Western Ontario, Ontario (Canada)

    2014-08-15

    Pattern classification methods have become important tools for fault diagnosis in industrial systems. However, it is normally difficult to obtain reliable labeled data to train a supervised pattern classification model for applications in a nuclear power plant (NPP). However, unlabeled data easily become available through increased deployment of supervisory, control, and data acquisition (SCADA) systems. In this paper, a fault diagnosis scheme based on semi-supervised classification (SSC) method is developed with specific applications for NPP. In this scheme, newly measured plant data are treated as unlabeled data. They are integrated with selected labeled data to train a SSC model which is then used to estimate labels of the new data. Compared to exclusive supervised approaches, the proposed scheme requires significantly less number of labeled data to train a classifier. Furthermore, it is shown that higher degree of uncertainties in the labeled data can be tolerated. The developed scheme has been validated using the data generated from a desktop NPP simulator and also from a physical NPP simulator using a graph-based SSC algorithm. Two case studies have been used in the validation process. In the first case study, three faults have been simulated on the desktop simulator. These faults have all been classified successfully with only four labeled data points per fault case. In the second case, six types of fault are simulated on the physical NPP simulator. All faults have been successfully diagnosed. The results have demonstrated that SSC is a promising tool for fault diagnosis.

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

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

    2016-06-01

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

  11. Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles

    Zhentong Liu

    2015-06-01

    Full Text Available The battery critical functions such as State-of-Charge (SoC and State-of-Health (SoH estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected.

  12. A New Fault Diagnosis Algorithm for PMSG Wind Turbine Power Converters under Variable Wind Speed Conditions

    Yingning Qiu

    2016-07-01

    Full Text Available Although Permanent Magnet Synchronous Generator (PMSG wind turbines (WTs mitigate gearbox impacts, they requires high reliability of generators and converters. Statistical analysis shows that the failure rate of direct-drive PMSG wind turbines’ generators and inverters are high. Intelligent fault diagnosis algorithms to detect inverters faults is a premise for the condition monitoring system aimed at improving wind turbines’ reliability and availability. The influences of random wind speed and diversified control strategies lead to challenges for developing intelligent fault diagnosis algorithms for converters. This paper studies open-circuit fault features of wind turbine converters in variable wind speed situations through systematic simulation and experiment. A new fault diagnosis algorithm named Wind Speed Based Normalized Current Trajectory is proposed and used to accurately detect and locate faulted IGBT in the circuit arms. It is compared to direct current monitoring and current vector trajectory pattern approaches. The results show that the proposed method has advantages in the accuracy of fault diagnosis and has superior anti-noise capability in variable wind speed situations. The impact of the control strategy is also identified. Experimental results demonstrate its applicability on practical WT condition monitoring system which is used to improve wind turbine reliability and reduce their maintenance cost.

  13. Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis

    Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang

    2016-09-01

    For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.

  14. Active fault diagnosis based on stochastic tests

    Poulsen, Niels Kjølstad; Niemann, Hans Henrik

    2008-01-01

    error output from the system. The classical cumulative sum (CUSUM) test will be modified with respect to the AFD approach applied. The CUSUM method will be altered such that it will be able to detect a change in the signature from the auxiliary input signal in an (error) output signal. It will be shown...... how it is possible to apply both the gain and the phase change of the output signal in CUSUM tests. The method is demonstrated using an example....

  15. Remote Fault Information Acquisition and Diagnosis System of the Combine Harvester Based on LabVIEW

    Chen, Jin; Wu, Pei; Xu, Kai

    Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.

  16. A Method of Rotating Machinery Fault Diagnosis Based on the Close Degree of Information Entropy

    GENG Jun-bao; HUANG Shu-hong; JIN Jia-shan; CHEN Fei; LIU Wei

    2006-01-01

    This paper presents a method of rotating machinery fault diagnosis based on the close degree of information entropy. In the view of the information entropy, we introduce four information entropy features of the rotating machinery, which describe the vibration condition of the machinery. The four features are, respectively, denominated as singular spectrum entropy, power spectrum entropy, wavelet space state feature entropy and wavelet power spectrum entropy. The value scopes of the four information entropy features of the rotating machinery in some typical fault conditions are gained by experiments, which can be acted as the standard features of fault diagnosis. According to the principle of the shorter distance between the more similar models, the decision-making method based on the close degree of information entropy is put forward to deal with the recognition of fault patterns. We demonstrate the effectiveness of this approach in an instance involving the fault pattern recognition of some rotating machinery.

  17. Degradation Assessment and Fault Diagnosis for Roller Bearing Based on AR Model and Fuzzy Cluster Analysis

    Lingli Jiang

    2011-01-01

    Full Text Available This paper proposes a new approach combining autoregressive (AR model and fuzzy cluster analysis for bearing fault diagnosis and degradation assessment. AR model is an effective approach to extract the fault feature, and is generally applied to stationary signals. However, the fault vibration signals of a roller bearing are non-stationary and non-Gaussian. Aiming at this problem, the set of parameters of the AR model is estimated based on higher-order cumulants. Consequently, the AR parameters are taken as the feature vectors, and fuzzy cluster analysis is applied to perform classification and pattern recognition. Experiments analysis results show that the proposed method can be used to identify various types and severities of fault bearings. This study is significant for non-stationary and non-Gaussian signal analysis, fault diagnosis and degradation assessment.

  18. A Method for Aileron Actuator Fault Diagnosis Based on PCA and PGC-SVM

    Wei-Li Qin

    2016-01-01

    Full Text Available Aileron actuators are pivotal components for aircraft flight control system. Thus, the fault diagnosis of aileron actuators is vital in the enhancement of the reliability and fault tolerant capability. This paper presents an aileron actuator fault diagnosis approach combining principal component analysis (PCA, grid search (GS, 10-fold cross validation (CV, and one-versus-one support vector machine (SVM. This method is referred to as PGC-SVM and utilizes the direct drive valve input, force motor current, and displacement feedback signal to realize fault detection and location. First, several common faults of aileron actuators, which include force motor coil break, sensor coil break, cylinder leakage, and amplifier gain reduction, are extracted from the fault quadrantal diagram; the corresponding fault mechanisms are analyzed. Second, the data feature extraction is performed with dimension reduction using PCA. Finally, the GS and CV algorithms are employed to train a one-versus-one SVM for fault classification, thus obtaining the optimal model parameters and assuring the generalization of the trained SVM, respectively. To verify the effectiveness of the proposed approach, four types of faults are introduced into the simulation model established by AMESim and Simulink. The results demonstrate its desirable diagnostic performance which outperforms that of the traditional SVM by comparison.

  19. Undecimated Lifting Wavelet Packet Transform with Boundary Treatment for Machinery Incipient Fault Diagnosis

    Lixiang Duan

    2016-01-01

    Full Text Available Effective signal processing in fault detection and diagnosis (FDD is an important measure to prevent failure and accidents of machinery. To address the end distortion and frequency aliasing issues in conventional lifting wavelet transform, a Volterra series assisted undecimated lifting wavelet packet transform (ULWPT is investigated for machinery incipient fault diagnosis. Undecimated lifting wavelet packet transform is firstly formulated to eliminate the frequency aliasing issue in traditional lifting wavelet packet transform. Next, Volterra series, as a boundary treatment method, is used to preprocess the signal to suppress the end distortion in undecimated lifting wavelet packet transform. Finally, the decomposed wavelet coefficients are trimmed to the original length as the signal of interest for machinery incipient fault detection. Experimental study on a reciprocating compressor is performed to demonstrate the effectiveness of the presented method. The results show that the presented method outperforms the conventional approach by dramatically enhancing the weak defect feature extraction for reciprocating compressor valve fault diagnosis.

  20. Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

    Manikandan Pandiyan

    2014-09-01

    Full Text Available The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI. Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

  1. Design of fault diagnosis system for inertial navigation system based on virtual technology

    Hu, Baiqing; Wang, Boxiong; Li, An; Zhang, Mingzhao; Qin, Fangjun; Pan, Hua

    2006-11-01

    With regard to the complex structure of the inertial navigation system and the low rate of fault detection with BITE (built-in test equipment), a fault diagnosis system for INS based on virtual technologies (virtual instrument and virtual equipment) is proposed in this paper. The hardware of the system is a PXI computer with highly stable performance and strong extensibility. In addition to the basic functions of digital multimeter, oscilloscope and cymometer, it can also measure the attitude of the ship in real-time, connect and control the measurement instruments with digital interface. The software is designed with the languages of Measurement Studio for VB, JAVA, and CULT3D. Using the extensively applied fault-tree reasoning and fault cases makes fault diagnosis. To suit the system to the diagnosis for various navigation electronic equipments, the modular design concept is adopted for the software programming. Knowledge of the expert system is digitally processed and the parameters of the system's interface and the expert diagnosis knowledge are stored in the database. The application shows that system is stable in operation, easy to use, quick and accurate in fault diagnosis.

  2. Design of a real-time fault diagnosis expert system for the EAST cryoplant

    Highlights: ► An expert system of real-time fault diagnosis for EAST cryoplant is designed. ► Knowledge base is built via fault tree analysis based on our fault experience. ► It can make up the deficiency of safety monitoring in cryogenic DCS. ► It can help operators to find the fault causes and give operation suggestion. ► It plays a role of operators training in certain degree. - Abstract: The EAST cryoplant consists of a 2 kW/4 K helium refrigerator and a helium distribution system. It is a complex process system which involves many process variables and cryogenic equipments. Each potential fault or abnormal event may influence stability and safety of the cryogenic system, thereby disturbing the fusion experiment. The cryogenic control system can monitor the process data and detect process alarms, but it is difficult to effectively diagnose the fault causes and provide operation suggestions to operators when anomalies occur. Therefore, a real-time fault diagnosis expert system is essential for a safe and steady operation of EAST cryogenic system. After a brief description of the EAST cryoplant and its control system, the structure design of the cryogenic fault diagnosis expert system is proposed. Based on the empirical knowledge, the fault diagnosis model is built adopting fault tree analysis method which considers the uncertainty. The knowledge base and the inference machine are presented in detail. A cross-platform integrated development environment Qt Creator and MySQL database have been used to develop the system. The proposed expert system has a fine graphic user interface for monitoring and operation. Preliminary test was conducted and the results found to be satisfactory.

  3. Geophysical characterization of buried active faults: the Concud Fault (Iberian Chain, NE Spain)

    Pueyo Anchuela, Óscar; Lafuente, Paloma; Arlegui, Luis; Liesa, Carlos L.; Simón, José L.

    2015-12-01

    The Concud Fault is a ~14-km-long active fault that extends close to Teruel, a city with about 35,000 inhabitants in the Iberian Range (NE Spain). It shows evidence of recurrent activity during Late Pleistocene time, posing a significant seismic hazard in an area of moderate-to-low tectonic rates. A geophysical survey was carried out along the mapped trace of the southern branch of the Concud Fault to evaluate the geophysical signature from the fault and the location of paleoseismic trenches. The survey identified a lineation of inverse magnetic dipoles at residual and vertical magnetic gradient, a local increase in apparent conductivity, and interruptions of the underground sediment structure along GPR profiles. The origin of these anomalies is due to lateral contrast between both fault blocks and the geophysical signature of Quaternary materials located above and directly south of the fault. The spatial distribution of anomalies was successfully used to locate suitable trench sites and to map non-exposed segments of the fault. The geophysical anomalies are related to the sedimentological characteristics and permeability differences of the deposits and to deformation related to fault activity. The results illustrate the usefulness of geophysics to detect and map non-exposed faults in areas of moderate-to-low tectonic activity where faults are often covered by recent pediments that obscure geological evidence of the most recent earthquakes. The results also highlight the importance of applying multiple geophysical techniques in defining the location of buried faults.

  4. Methods for Probabilistic Fault Diagnosis: An EPS Case Study

    National Aeronautics and Space Administration — Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in...

  5. Rotor broken bar fault diagnosis for induction motors based on double PQ transformation

    2007-01-01

    A new rotor broken bar fault diagnosis method for induction motors based on the double PQ transformation is presented. By distinguishing the different patterns of the PQ components in the PQ plane, the rotor broken bar fault can be detected.The magnitude of power component directly resulted from rotor fault is used as the fault indicator and the distance between the point of no-load condition and the center of the ellipse as its normalization value. Based on these, the fault severity factor which is completely independent of the inertia and load level is defined. Moreover, a method to reliably discriminate between rotor faults and periodic load fluctuation is presented. Experimental results from a 4 kW induction motor demonstrated the validity of the proposed method.

  6. Research on intelligent fault diagnosis based on time series analysis algorithm

    CHEN Gang; LIU Yang; ZHOU Wen-an; SONG Jun-de

    2008-01-01

    Aiming to realize fast and accurate fault diagnosisin complex network environment, this article proposes a set ofanomaly detection algorithm and intelligent fault diagnosismodel. Firstly, a novel anomaly detection algorithm based ontime series analysis is put forward to improve the generalizedlikelihood ratio (GLR) test, and thus, detection accuracy isenhanced and the algorithm complexity is reduced. Secondly,the intelligent fault diagnosis model is established byintroducing neural network technology, and thereby, theanomaly information of each node in end-to-end network isintegrated and processed in parallel to intelligently diagnosethe fault cause. Finally, server backup solution in enterpriseinformation network is taken as the simulation scenario. Theresults demonstrate that the proposed method can not onlydetect fault occurrence in time, but can also implement onlinediagnosis for fault cause, and thus, real-time and intelligent faultmanagement process is achieved.

  7. FAULT DIAGNOSIS APPROACH FOR ROLLER BEARINGS BASED ON EMPIRICAL MODE DECOMPOSITION METHOD AND HILBERT TRANSFORM

    Yu Dejie; Cheng Junsheng; Yang Yu

    2005-01-01

    Based upon empirical mode decomposition (EMD) method and Hilbert spectrum, a method for fault diagnosis of roller bearing is proposed. The orthogonal wavelet bases are used to translate vibration signals of a roller bearing into time-scale representation, then, an envelope signal can be obtained by envelope spectrum analysis of wavelet coefficients of high scales. By applying EMD method and Hilbert transform to the envelope signal, we can get the local Hilbert marginal spectrum from which the faults in a roller bearing can be diagnosed and fault patterns can be identified. Practical vibration signals measured from roller bearings with out-race faults or inner-race faults are analyzed by the proposed method. The results show that the proposed method is superior to the traditional envelope spectrum method in extracting the fault characteristics of roller bearings.

  8. Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine

    Jian-Hua Zhong

    2016-02-01

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

  9. Simultaneous-Fault Diagnosis of Gearboxes Using Probabilistic Committee Machine.

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

    2016-01-01

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

  10. Elementwise Business Diagnosis of Enterprise Activity

    Skrynkovskyy Ruslan M.

    2016-02-01

    Full Text Available The article presents methodological and indicator apparatus for elementwise business diagnosis of enterprise activity directed at achieving such elementwise diagnostic objectives: diagnosis of return on assets; diagnosis of return on equity capital; diagnosis of production profitability; diagnosis of gross profit margin of product sales; diagnosis of operating margin of product sales; diagnosis of net margin of product sales; diagnosis of absolute liquidity; diagnosis of instant liquidity; diagnosis of overall liquidity; diagnosis of coverage; diagnosis of financial independence; diagnosis of equity capital maneuverability; diagnosis of financial leverage; diagnosis of the long-term investment structure; diagnosis of accounts payable turnover; diagnosis of the accounts payable repayment period, diagnosis of receivables turnover; diagnosis of receivables repayment period; diagnosis of assets turnover; diagnosis of inventories turnover; diagnosis of the inventories turnover period; diagnosis of equity capital turnover; diagnosis of fixed assets turnover (return on assets; diagnosis of capital coefficient; diagnosis of the ratio of output value to the materials cost; diagnosis of material consumption; diagnosis of the total production cost; diagnosis of enterprise market share; diagnosis of fixed assets wear; diagnosis of fixed assets renewal; diagnosis of fixed assets retirement; performance diagnosis; diagnosis of labor intensity, diagnosis of the capital-labour ratio; diagnosis of efficiency; diagnosis of conducting the business; diagnosis of business relations; diagnosis of administrative-legal relations; diagnosis of knowledge management. The elementwise diagnostic objectives of the enterprise system of diagnostic objectives are aimed at a narrow highly detailed diagnostics of individual indicators of the enterprise activity, i.e. the evaluation of specific analytical indicators,monitoring (research of their dynamics, comparison of the planned

  11. Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

    Hao Sun

    2014-01-01

    Full Text Available The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO is proposed. Geronimo, Hardin, and Massopust (GHM multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.

  12. Control Surface Fault Diagnosis with Specified Detection Probability - Real Event Experiences

    Hansen, Søren; Blanke, Mogens

    2013-01-01

    desired levels of false alarms and detection probabilities. Self-tuning residual generators are employed for diagnosis and are combined with statistical change detection to form a setup for robust fault diagnosis. On-line estimation of test statistics is used to obtain a detection threshold and a desired...... false alarm probability. A data based method is used to determine the validity of the methods proposed. Verification is achieved using real data and shows that the presented diagnosis method is efficient and could have avoided incidents where faults led to loss of aircraft....

  13. APPROACH TO FAULT ON-LINE DETECTION AND DIAGNOSIS BASED ON NEURAL NETWORKS FOR ROBOT IN FMS

    1998-01-01

    Based on radial basis function (RBF) neural networks, the healthy working model of each sub-system of robot in FMS is established. A new approach to fault on-line detection and diagnosis according to neural networks model is presented. Fault double detection based on neural network model and threshold judgement and quick fault identification based on multi-layer feedforward neural networks are applied, which can meet quickness and reliability of fault detection and diagnosis for robot in FMS.

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

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

    2011-01-01

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

  15. Fault detection and diagnosis using statistical control charts and artificial neural networks

    In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using Cumulative Summation (CUSUM) Control Charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor. The results of the investigation indicate that a FDD system using CUSUM Control Charts and a Radial Basis Function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked together by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect 6 fault conditions and correctly diagnose 5 out of the 6 faults. The diagnosis for the sixth fault was inconclusive. (author). 9 refs., 6 tabs., 7 figs

  16. Robust condition monitoring and fault diagnosis of rolling element bearings using improved EEMD and statistical features

    Condition monitoring and fault diagnosis play an important role in the health management of mechanical equipment. However, the robust performance of data-driven-based methods with unknown fault inputs remains to be further improved. In this paper, a novel approach of condition monitoring and fault diagnosis is proposed for rolling element bearings based on an improved ensemble empirical mode decomposition (IEEMD), which is able to solve the non-intrinsic mode function problem of EEMD. In this method, IEEMD is applied to process the primordial vibration signals collected from rolling element bearings at first. Then the correlation analysis and data fusion technology are introduced to extract statistical features from these decomposition results of IEEMD. Finally, a complete self-zero space model is constructed for the condition monitoring and fault diagnosis of rolling element bearings. Experiments are implemented on a mechanical fault simulator to demonstrate the reliability and effectiveness of the proposed method. The experimental results show that the proposed method can not only diagnose known faults but also monitor unknown faults with strong robust performance. (paper)

  17. Fault Diagnosis of Mixed-Signal Analog Circuit using Artificial Neural Networks

    Ashwani Kumar Narula

    2015-06-01

    Full Text Available This paper presents parametric fault diagnosis in mixed-signal analog circuit using artificial neural networks. Single parametric faults are considered in this study. A benchmark R2R digital to analog converter circuit has been used as an example circuit for experimental validations. The input test pattern required for testing are reduced to optimum value using sensitivity analysis of the circuit under test. The effect of component tolerances has also been taken care of by performing the Monte-Carlo analysis. In this study parametric fault models are defined for the R2R network of the digital to analog converter. The input test patterns are applied to the circuit under test and the output responses are measured for each fault model covering all the Monte-Carlo runs. The classification of the parametric faults is done using artificial neural networks. The fault diagnosis system is developed in LabVIEW environment in the form of a virtual instrument. The artificial neural network is designed using MATLAB and finally embedded in the virtual instrument. The fault diagnosis is validated with simulated data and with the actual data acquired from the circuit hardware.

  18. FaultBuster: data driven fault detection and diagnosis for industrial systems

    Bergantino, Nicola; Caponetti, Fabio; Longhi, Sauro

    2009-01-01

    Efficient and reliable monitoring systems are mandatory to assure the required security standards in industrial complexes. This paper describes the recent developments of FaultBuster, a purely data-driven diagnostic system. It is designed so to be easily scalable to different monitor tasks. Multivariate statistical models based on principal components are used to detect abnormal situations. Tailored to alarms, a probabilistic inference engine process the fault evidences to output the most pro...

  19. Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

    Yuan Pu

    2015-01-01

    Full Text Available BP neural network (Back-Propagation Neural Network, BP-NN is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.

  20. Rolling Element Bearing Fault Diagnosis Based on Multiscale General Fractal Features

    Weigang Wen

    2015-01-01

    Full Text Available Nonlinear characteristics are ubiquitous in the vibration signals produced by rolling element bearings. Fractal dimensions are effective tools to illustrate nonlinearity. This paper proposes a new approach based on Multiscale General Fractal Dimensions (MGFDs to realize fault diagnosis of rolling element bearings, which are robust to the effects of variation in operating conditions. The vibration signals of bearing are analyzed to extract the general fractal dimensions in multiscales, which are in turn utilized to construct a feature space to identify fault pattern. Finally, bearing faults are revealed by pattern recognition. Case studies are carried out to evaluate the validity and accuracy of the approach. It is verified that this approach is effective for fault diagnosis of rolling element bearings under various operating conditions via experiment and data analysis.

  1. A Review on Fault Mechanism and Diagnosis Approach for Li-Ion Batteries

    Chao Wu

    2015-01-01

    Full Text Available Li-ion battery has attracted more and more attention as it is a promising storage device which has long service life, higher energy, and power density. However, battery ageing always occurs during operation and leads to performance degradation and system fault which not only causes inconvenience, but also risks serious consequences such as thermal runaway or even explosion. This paper reviews recent research and development of ageing mechanisms of Li-ion batteries to understand the origins and symptoms of Li-ion battery faults. Common ageing factors are covered with their effects and consequences. Through ageing tests, relationship between performance and ageing factors, as well as cross-dependence among factors can be quantified. Summary of recent research about fault diagnosis technology for Li-ion batteries is concluded with their cons and pros. The suggestions on novel fault diagnosis approach and remaining challenges are provided at the end of this paper.

  2. Diagnosis and Fault-tolerant Control for Ship Station Keeping

    Blanke, Mogens

    2005-01-01

    design for systems of high complexity, and also analyse the cases of cascaded or multiple faults. The paper takes as example a ship with two CP propellers, rudders and a bow thruster as actuators, and instrumentation with a suite of global position sensors, inertial navigation units and conventional gyro...

  3. Control Surface Fault Diagnosis for Small Autonomous Aircraft

    Hansen, Søren; Blanke, Mogens

    2011-01-01

    hardware or are analytical, and formulates residuals from which faults can be prognosed or diagnosed. An approach is suggested where detailed modelling is not needed but normal behaviour is learned from short segments of flight data using adaptive methods for learning. Statistical characterisation of...

  4. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

    Weiying Wang; Zhiqiang Xu; Rui Tang; Shuying Li; Wei Wu

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine...

  5. Control rod position fault diagnosis and its software realization of pressurized water reactor

    PLC software is adopted in the Rod Position Monitoring System of QS2NPS. By this software, the position of control rods can be monitored in real time, the abnormal phenomena can be identified immediately, the correctness and timeliness of fault diagnosis are improved remarkably. the identification and recordance of rod position fault, the performance validation of measure channel are realized also. The function and effect of this software are introduced. (authors)

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

    Jinglong Chen; Yu Wang; Zhengjia He; Xiaodong Wang

    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. Fir...

  7. Diagnosis of three types of constant faults in read-once contact networks over finite bases

    Busbait, Monther

    2016-03-24

    We study the depth of decision trees for diagnosis of three types of constant faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis and each type of faults, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in networks. For bases containing networks with at most 10 edges, we find sharp coefficients for linear bounds.

  8. Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy

    Li-Ye Zhao; Lei Wang; Ru-Qiang Yan

    2015-01-01

    This paper presents a rolling bearing fault diagnosis approach by integrating wavelet packet decomposition (WPD) with multi-scale permutation entropy (MPE). The approach uses MPE values of the sub-frequency band signals to identify faults appearing in rolling bearings. Specifically, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of sub-frequency band signals by means of the WPD method. Then, each sub-frequency band sign...

  9. High-Speed Spindle Fault Diagnosis with the Empirical Mode Decomposition and Multiscale Entropy Method

    Nan-Kai Hsieh; Wei-Yen Lin; Hong-Tsu Young

    2015-01-01

    The root mean square (RMS) value of a vibration signal is an important indicator used to represent the amplitude of vibrations in evaluating the quality of high-speed spindles. However, RMS is unable to detect a number of common fault characteristics that occur prior to bearing failure. Extending the operational life and quality of spindles requires reliable fault diagnosis techniques for the analysis of vibration signals from three axes. This study used empirical mode decomposition to decomp...

  10. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine

    Nantian Huang; Huaijin Chen; Shuxin Zhang; Guowei Cai; Weiguo Li; Dianguo Xu; Lihua Fang

    2015-01-01

    Mechanical faults of high voltage circuit breakers (HVCBs) are one of the most important factors that affect the reliability of power system operation. Because of the limitation of a lack of samples of each fault type; some fault conditions can be recognized as a normal condition. The fault diagnosis results of HVCBs seriously affect the operation reliability of the entire power system. In order to improve the fault diagnosis accuracy of HVCBs; a method for mechanical fault diagnosis of HVCBs...

  11. A Study on Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion

    ANG Xue-ye

    2007-01-01

    The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given . It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.

  12. A study on group decision-making based fault multi-symptom-domain consensus diagnosis

    In the field of fault diagnosis for rotating machines, the conventional methods or the neural network based methods are mainly single symptom domain based methods, and the diagnosis accuracy of which is not always satisfactory. In this paper, in order to utilize multiple symptom domains to improve the diagnosis accuracy, an idea of fault multi-symptom-domain consensus diagnosis is developed. From the point of view of the group decision-making, two particular multi-symptom-domain diagnosis strategies are proposed. The proposed strategies use BP (Back-Propagation) neural networks as diagnosis models in various symptom domains, and then combine the outputs of these networks by two combination schemes, which are based on Dempster-Shafer evidence theory and fuzzy integral theory, respectively. Finally, a case study pertaining to the fault diagnosis for rotor-bearing systems is given in detail, and the results show that the proposed diagnosis strategies are feasible and more efficient than conventional stacked-vector methods

  13. In-Flight Fault Diagnosis for Autonomous Aircraft Via Low-Rate Telemetry Channel

    Blanke, Mogens; Hansen, Søren

    2012-01-01

    An in-flight diagnosis system that is able to detect faults on an unmanned aircraft using real-time telemetry data could provide operator assistance to warn about imminent risks due to faults. However, limited bandwidth of the air-ground radio-link makes diagnosis difficult. Loss of information...... about rapid dynamic changes and high parameter uncertainty are the main difficulties. This paper explores time-domain relations in received telemetry signals and uses knowledge of aircraft dynamics and the mechanics behind physical faults to obtain a set of greybox models for diagnosis. Relating...... actuator fin deflections with angular rates of the aircraft, low order models are derived and parameters are estimated using system identification techniques. Change detection methods are applied to the prediction error of angular rate estimates and properties of the test statistics are determined...

  14. Advanced neural network-based computational schemes for robust fault diagnosis

    Mrugalski, Marcin

    2014-01-01

    The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

  15. SDG-Based HAZOP and Fault Diagnosis Analysis to the Inversion of Synthetic Ammonia

    L(U) Ning; WANG Xiong

    2007-01-01

    This paper presents some practical applications of signed directed graphs(SDGs)to computeraided hazard and operability study (HAZOP) and fault diagnosis, based on an analysis of the SDG theory.The SDG is modeled for the inversion of synthetic ammonia, which is highly dangerous in process industry,and HAZOP and fault diagnosis based on the SDG model are presented.A new reasoning method,whereby inverse inference is combined with forward inference,is presented to implement SDG fault diagnosis based on a breadth-first algorithm with consistency rules. Compared with conventional inference engines, this new method can better avoid qualitative spuriousness and combination explosion, and can deal with unobservable nodes in SDGs more effectively. Experimental results show the validity and advantages of the new SDG method.

  16. Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

    Zhen, D.; Zhao, H. L.; Gu, F.; Ball, A. D.

    2012-05-01

    Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase-compensation-based DTW allows the fault-related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier-transform-based methods.

  17. Fault diagnosis of bearings based on a sensitive feature decoupling technique

    Bearings are commonly used in machine industry, and their faults may result in unexpected vibration and even cause breakdown of a whole rotating machine. This paper proposes a novel fault diagnosis approach for bearings by using a sensitive feature decoupling technique. This approach does not require a training procedure as in machine learning methods and can classify the occurred faults by a simple algebraic computation. Firstly, the features of vibration signals which show the most significant difference under different bearing health conditions are selected and defined as sensitive features. Then those sensitive features under different health conditions are used to construct a feature matrix, and its left null space is computed to obtain the so-called feature decoupling vectors. The bearing faults are finally classified with the help of the decoupling vectors according to a simple decision logic. Since the obtained decoupling vectors may not be unique, we also propose an algorithm to select the optimal ones in order to improve the performance of fault diagnosis. Experiments are carried out to test the proposed approach and the results show that the approach is feasible and effective for the fault diagnosis of bearings. (paper)

  18. Phase-compensation-based dynamic time warping for fault diagnosis using the motor current signal

    Dynamic time warping (DTW) is a time-domain-based method and widely used in various similar recognition and data mining applications. This paper presents a phase-compensation-based DTW to process the motor current signals for detecting and quantifying various faults in a two-stage reciprocating compressor under different operating conditions. DTW is an effective method to align two signals for dissimilarity analysis. However, it has drawbacks such as singularities and high computational demands that limit its application in processing motor current signals for obtaining modulation characteristics accurately in diagnosing compressor faults. Therefore, a phase compensation approach is developed to reduce the singularity effect and a sliding window is designed to improve computing efficiency. Based on the proposed method, the motor current signals measured from the compressor induced with different common faults are analysed for fault diagnosis. Results show that residual signal analysis using the phase-compensation-based DTW allows the fault-related sideband features to be resolved more accurately for obtaining reliable fault detection and diagnosis. It provides an effective and easy approach to the analysis of motor current signals for better diagnosis in the time domain in comparison with conventional Fourier-transform-based methods. (paper)

  19. Actuator fault diagnosis of time-delay systems based on adaptive observer

    2005-01-01

    A novel approach for the actuator fault diagnosis of time-delay systems is presented by using an adaptive observer technique. Systems without model uncertainty are initially considered, followed by a discussion of a general situation where the system is subjected to either model uncertainty or external disturbance. An adaptive diagnostic algorithm is developed to diagnose the fault, and a modified version is proposed for general system to improve robustness. The selection of the threshold for fault detection is also discussed. Finally, a numerical example is given to illustrate the efficiency of the proposed method.

  20. Active Fault Detection and Isolation for Hybrid Systems

    Gholami, Mehdi; Schiøler, Henrik; Bak, Thomas;

    2009-01-01

    An algorithm for active fault detection and isolation is proposed. In order to observe the failure hidden due to the normal operation of the controllers or the systems, an optimization problem based on minimization of test signal is used. The optimization based method imposes the normal and faulty...... models predicted outputs such that their discrepancies are observable by passive fault diagnosis technique. Isolation of different faults is done by implementation a bank of Extended Kalman Filter (EKF) where the convergence criterion for EKF is confirmed by Genetic Algorithm (GA). The method is applied...

  1. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning

    Chuan Li

    2016-06-01

    Full Text Available Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM. The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.

  2. A Unified Framework for Fault Detection and Diagnosis Using Particle Filter

    Bo Zhao

    2014-10-01

    Full Text Available In this paper, a particle filter (PF based fault detection and diagnosis framework is proposed. A system with possible faults is modeled as a group of hidden Markov models representing the system in fault-free mode and different failure modes, and a first order Markov chain is modeling the system mode transitions. A modified particle filter algorithm is developed to estimate the system states and mode. By doing this, system faults are detected when estimating the system mode, and the size of the fault is diagnosed by estimating the system state. A new resampling method is also developed for running the modified PF efficiently. Two introductory examples and a case study are given in detail. The introduction examples demonstrate the manner to model a system with possible faults into hidden Markov model and Markov chain. The case study considers a numerical model with common measurement failure modes. It focuses on the verification of the proposed fault diagnosis and detection algorithm and shows the behavior of the particle filter.

  3. Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning.

    Li, Chuan; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego

    2016-01-01

    Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represented in the time, frequency, and time-frequency domains, each of which is then used to produce a statistical feature set. For learning statistical features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs) are stacked to develop a Gaussian-Bernoulli deep Boltzmann machine (GDBM). The suggested approach is applied as a deep statistical feature learning tool for both gearbox and bearing systems. The fault classification performances in experiments using this approach are 95.17% for the gearbox, and 91.75% for the bearing system. The proposed approach is compared to such standard methods as a support vector machine, GRBM and a combination model. In experiments, the best fault classification rate was detected using the proposed model. The results show that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults. PMID:27322273

  4. Reconfigurable Test Architecture for Online Concurrent Fault Detection, Diagnosis and Repair

    Rajeevan Chandel

    2012-03-01

    Full Text Available A complete and versatile online test solution based on reconfigurable test architecture is presented in the present paper. Reconfigurable test architecture works alongside the controllers for online concurrent fault detection. The output vectors of the controllers are concurrently monitored and any fault present is detected in a few cycles from the sensitization of the fault. The architecture is then reprogrammed to a similar set of diagnostic hardware to locate a sub block which is the cause for the fault. The same architecture is then reprogrammed to replace the faulty block thereby completing repair. The test architecture is designed based on configurable logic blocks. The design has several advantages viz. (i it works well for critical VLSI controllers where shutting down or suspending the operation of a controller for testing is not possible and where the fault needs to be detected at the earliest, during the run time of the system, (ii after a fault is detected, diagnosis can be performed online, (iii once a faulty block is located, repair is also done online. Since fault detection, diagnosis and repair are completed online with one test hardware, the effective hardware overhead is negligible and the system can resume its function within a brief period. The applicability of the architecture is demonstrated for the control blocks in OC8051.

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

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

    2016-06-01

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

  6. Distributed model-based nonlinear sensor fault diagnosis in wireless sensor networks

    Lo, Chun; Lynch, Jerome P.; Liu, Mingyan

    2016-01-01

    Wireless sensors operating in harsh environments have the potential to be error-prone. This paper presents a distributive model-based diagnosis algorithm that identifies nonlinear sensor faults. The diagnosis algorithm has advantages over existing fault diagnosis methods such as centralized model-based and distributive model-free methods. An algorithm is presented for detecting common non-linearity faults without using reference sensors. The study introduces a model-based fault diagnosis framework that is implemented within a pair of wireless sensors. The detection of sensor nonlinearities is shown to be equivalent to solving the largest empty rectangle (LER) problem, given a set of features extracted from an analysis of sensor outputs. A low-complexity algorithm that gives an approximate solution to the LER problem is proposed for embedment in resource constrained wireless sensors. By solving the LER problem, sensors corrupted by non-linearity faults can be isolated and identified. Extensive analysis evaluates the performance of the proposed algorithm through simulation.

  7. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

    Li, Chuan; Sanchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Cabrera, Diego; Vásquez, Rafael E.

    2016-08-01

    Fault diagnosis is an effective tool to guarantee safe operations in gearboxes. Acoustic and vibratory measurements in such mechanical devices are all sensitive to the existence of faults. This work addresses the use of a deep random forest fusion (DRFF) technique to improve fault diagnosis performance for gearboxes by using measurements of an acoustic emission (AE) sensor and an accelerometer that are used for monitoring the gearbox condition simultaneously. The statistical parameters of the wavelet packet transform (WPT) are first produced from the AE signal and the vibratory signal, respectively. Two deep Boltzmann machines (DBMs) are then developed for deep representations of the WPT statistical parameters. A random forest is finally suggested to fuse the outputs of the two DBMs as the integrated DRFF model. The proposed DRFF technique is evaluated using gearbox fault diagnosis experiments under different operational conditions, and achieves 97.68% of the classification rate for 11 different condition patterns. Compared to other peer algorithms, the addressed method exhibits the best performance. The results indicate that the deep learning fusion of acoustic and vibratory signals may improve fault diagnosis capabilities for gearboxes.

  8. Investigation of the synthetic experiment system of machine equipment fault diagnosis

    Liu, Hongyu; Xu, Zening; Yu, Xiaoguang

    2008-12-01

    The invention and manufacturing of the synthetic experiment system of machine equipment fault diagnosis filled in the blank of this kind of experiment equipment in China and obtained national practical new type patent. By the motor speed regulation system, machine equipment fault imitation system, measuring and monitoring system and analysis and diagnosis system of the synthetic experiment system, students can regulate motor speed arbitrarily, imitate multi-kinds of machine equipment parts fault, collect the signals of acceleration, speed, displacement, force and temperature and make multi-kinds of time field, frequency field and figure analysis. The application of the synthetic experiment system in our university's teaching practice has obtained good effect on fostering professional eligibility in measuring, monitoring and fault diagnosis of machine equipment. The synthetic experiment system has the advantages of short training time, quick desirable result and low test cost etc. It suits for spreading in university extraordinarily. If the systematic software was installed in portable computer, user can fulfill measuring, monitoring, signal processing and fault diagnosis on multi-kinds of field machine equipment conveniently. Its market foreground is very good.

  9. Sensor Fault Detection and Diagnosis for autonomous vehicles

    Realpe Miguel; Vintimilla Boris; Vlacic Ljubo

    2015-01-01

    In recent years testing autonomous vehicles on public roads has become a reality. However, before having autonomous vehicles completely accepted on the roads, they have to demonstrate safe operation and reliable interaction with other traffic participants. Furthermore, in real situations and long term operation, there is always the possibility that diverse components may fail. This paper deals with possible sensor faults by defining a federated sensor data fusion architecture. The proposed ar...

  10. Sensor Placement for Fault Diagnosis Based On Causal Computations

    Rosich, Albert; Frisk, Erik; Åslund, Jan; Sarrate, Ramon; Nejjari, Fatiha

    2009-01-01

    This work develops a methodology to solve the sensor placement problem for fault detection and isolation. The proposed methodology is suitable to handle highly non-linear and large scale systems since it is based on structural models. Furthermore, causality is assigned in those variable-equation relations that the variable can be computed from the equation in order to guarantee the computability of the unknown variables in the residual generation design. Finally, the developed methodology is ...

  11. An Intelligent Fault Diagnosis System for Machine Tools

    Chia Wang

    2014-08-01

    Full Text Available An automatic intelligent system is developed to diagnose shaft fault types. Features related to shaft faults are extracted from vibration signals to effectively identify the corresponding fault condition. Feature extraction is accomplished using Fourier Transform, empirical mode decomposition (EMD and multi-scale entropy (MSE. Through the EMD method, the model uses characteristics of intrinsic mode functions (such as zero-crossing rate and energy, to represent shaft condition features. MSE is used to calculate the entropy of multi-scale of the signal. At a larger MSE scale, the MSE result can be used to clearly identify some shaft defect types. The conventional approach to monitoring of a machine’s health online based on linear time-frequency analysis is subject to limitations, as the mechanical vibration signal is nonlinear and non-stationary in nature. Thus this research develops a diagnostic system based on the implementation of Fourier, EMD and MSE-based methods. In the buildup stage a knowledgeware is created from a database of existing defect types. Finally, the automatic intelligent monitoring system is implemented in a machine tool manufacturing company to verify its performance.

  12. Fault Diagnosis Strategies for SOFC-Based Power Generation Plants

    Paola Costamagna

    2016-08-01

    Full Text Available The success of distributed power generation by plants based on solid oxide fuel cells (SOFCs is hindered by reliability problems that can be mitigated through an effective fault detection and isolation (FDI system. However, the numerous operating conditions under which such plants can operate and the random size of the possible faults make identifying damaged plant components starting from the physical variables measured in the plant very difficult. In this context, we assess two classical FDI strategies (model-based with fault signature matrix and data-driven with statistical classification and the combination of them. For this assessment, a quantitative model of the SOFC-based plant, which is able to simulate regular and faulty conditions, is used. Moreover, a hybrid approach based on the random forest (RF classification method is introduced to address the discrimination of regular and faulty situations due to its practical advantages. Working with a common dataset, the FDI performances obtained using the aforementioned strategies, with different sets of monitored variables, are observed and compared. We conclude that the hybrid FDI strategy, realized by combining a model-based scheme with a statistical classifier, outperforms the other strategies. In addition, the inclusion of two physical variables that should be measured inside the SOFCs can significantly improve the FDI performance, despite the actual difficulty in performing such measurements.

  13. Process Monitoring and Fault Diagnosis for Shell Rolling Production of Seamless Tube

    Dong Xiao

    2015-01-01

    Full Text Available Continuous rolling production process of seamless tube has many characteristics, including multiperiod and strong nonlinearity, and quickly changing dynamic characteristics. It is difficult to build its mechanism model. In this paper we divide production data into several subperiods by K-means clustering algorithm combined with production process; then we establish a continuous rolling production monitoring and fault diagnosis model based on multistage MPCA method. Simulation experiments show that the rolling production process monitoring and fault diagnosis model based on multistage MPCA method is effective, and it has a good real-time performance, high reliability, and precision.

  14. Machinery fault diagnosis expert system based on case-based reasoning

    LI Wen-hong; SUN Shao-wen; ZHANG Qi

    2007-01-01

    A mechinery fault diagnosis expert system based on case-based reasoning (CBR) technology was established. The process of the CBR fault diagnosis is analyzed from three main aspects: expression and memory, retrieving and matching, and modification and maintenance of a case. The results indicate that the CBR method is flexible and simple to implement, and it has strong self-studying ability. Using a large enough number of case reasoning sets, it can accumulate the experience of problem solving, avoid the difficulty of knowledge acquisition, shorten the course of solving problems, improve efficiency of reasoning, and save the time of developing.

  15. Active Fault Characterization in the Urban Area of Vienna

    Decker, Kurt; Grupe, Sabine; Hintersberger, Esther

    2016-04-01

    The identification of active faults that lie beneath a city is of key importance for seismic hazard assessment. Fault mapping and characterization in built-up areas with strong anthropogenic overprint is, however, a challenging task. Our study of Quaternary faults in the city of Vienna starts from the re-assessment of a borehole database of the municipality containing several tens of thousands of shallow boreholes. Data provide tight constraints on the geometry of Quaternary deposits and highlight several locations with fault-delimited Middle to Late Pleistocene terrace sediments of the Danube River. Additional information is obtained from geological descriptions of historical outcrops which partly date back to about 1900. The latter were found to be particularly valuable by providing unprejudiced descriptions of Quaternary faults, sometimes with stunning detail. The along-strike continuations of some of the identified faults are further imaged by industrial 2D/3D seismic acquired outside the city limits. The interpretation and the assessment of faults identified within the city benefit from a very well constrained tectonic model of the active Vienna Basin fault system which derived from data obtained outside the city limits. This data suggests that the urban faults are part of a system of normal faults compensating fault-normal extension at a releasing bend of the sinistral Vienna Basin Transfer Fault. Slip rates estimated for the faults in the city are in the range of several hundredths of millimetres per year and match the slip rates of normal faults that were trenched outside the city. The lengths/areas of individual faults estimated from maps and seismic reach up to almost 700 km² suggesting that all of the identified faults are capable of producing earthquakes with magnitudes M>6, some with magnitudes up to M~6.7.

  16. Functional Modelling for Fault Diagnosis and its application for NPP

    Lind, Morten; Zhang, Xinxin

    2014-01-01

    The paper presents functional modelling and its application for diagnosis in nuclear power plants.Functional modelling is defined and it is relevance for coping with the complexity of diagnosis in large scale systems like nuclear plants is explained. The diagnosis task is analyzed....... The use of MFM for reasoning about causes and consequences is explained in detail and demonstrated using the reasoning tool the MFM Suite. MFM applications in nuclear power systems are described by two examples a PWR and a FBRreactor. The PWR example show how MFM can be used to model and reason about...

  17. Fault diagnosis in chemical plants integrated to the information system

    Ruiz, Diego

    2001-01-01

    La contribución que se pretende con esta tesis se refiere a la implantación de un sistema de diagnosis de fallos en plantas químicas completas integrado al sistema de supervisión, gestión y control de la producción.El sistema de diagnosis de fallos que se presenta consiste en una combinación de un sistema de reconocimiento de patrones basado en redes neuronales artificiales y un sistema de inferencia basado en la lógica difusa. La información necesaria para desarrollar el sistema de diagnosis...

  18. Detection and diagnosis of bearing and cutting tool faults using hidden Markov models

    Boutros, Tony; Liang, Ming

    2011-08-01

    Over the last few decades, the research for new fault detection and diagnosis techniques in machining processes and rotating machinery has attracted increasing interest worldwide. This development was mainly stimulated by the rapid advance in industrial technologies and the increase in complexity of machining and machinery systems. In this study, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults. The technique is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults. In the first case the model correctly detected the state of the tool (i.e., sharp, worn, or broken) whereas in the second application, the model classified the severity of the fault seeded in two different engine bearings. The success rate obtained in our tests for fault severity classification was above 95%. In addition to the fault severity, a location index was developed to determine the fault location. This index has been applied to determine the location (inner race, ball, or outer race) of a bearing fault with an average success rate of 96%. The training time required to develop the HMMs was less than 5 s in both the monitoring cases.

  19. Fault diagnosis of direct-drive wind turbine based on support vector machine

    A fault diagnosis method of direct-drive wind turbine based on support vector machine (SVM) and feature selection is presented. The time-domain feature parameters of main shaft vibration signal in the horizontal and vertical directions are considered in the method. Firstly, in laboratory scale five experiments of direct-drive wind turbine with normal condition, wind wheel mass imbalance fault, wind wheel aerodynamic imbalance fault, yaw fault and blade airfoil change fault are carried out. The features of five experiments are analyzed. Secondly, the sensitive time-domain feature parameters in the horizontal and vertical directions of vibration signal in the five conditions are selected and used as feature samples. By training, the mapping relation between feature parameters and fault types are established in SVM model. Finally, the performance of the proposed method is verified through experimental data. The results show that the proposed method is effective in identifying the fault of wind turbine. It has good classification ability and robustness to diagnose the fault of direct-drive wind turbine.

  20. NN-Es Fault Diagnosis Method in Nuclear Power Equipment Based on Concept Lattice

    In order to improve the fault diagnosis accuracy of nuclear power plant,neural network and expert systems were combined to give full play to their advantages. In this paper, the concept lattice was applied to get the object properties, extracting the core attributes, dispensable attributes and relative necessary attributes from a large number raw data of fault symptoms.Based on these attributes, neural networks with different levels of importance were designed to improve the learning speed and diagnosis accuracy, and the diagnosis results of the neural networks were verified by using rule-based reasoning expert system. To verify the accuracy of this method, some simulation experiments about the typical faults of nuclear power plant were conducted. And the simulation results show that it is feasible to diagnose nuclear power plant faults with the confederation diagnosis methods combined the neural networks based on the concept lattice theory and expert system, with the distinctive features such as the efficiency of neural network learning, less calculation and reliability of diagnosis results and so on. (authors)

  1. Fault diagnosis of motor drives using stator current signal analysis based on dynamic time warping

    Zhen, D.; Wang, T.; Gu, F.; Ball, A. D.

    2013-01-01

    Electrical motor stator current signals have been widely used to monitor the condition of induction machines and their downstream mechanical equipment. The key technique used for current signal analysis is based on Fourier transform (FT) to extract weak fault sideband components from signals predominated with supply frequency component and its higher order harmonics. However, the FT based method has limitations such as spectral leakage and aliasing, leading to significant errors in estimating the sideband components. Therefore, this paper presents the use of dynamic time warping (DTW) to process the motor current signals for detecting and quantifying common faults in a downstream two-stage reciprocating compressor. DTW is a time domain based method and its algorithm is simple and easy to be embedded into real-time devices. In this study DTW is used to suppress the supply frequency component and highlight the sideband components based on the introduction of a reference signal which has the same frequency component as that of the supply power. Moreover, a sliding window is designed to process the raw signal using DTW frame by frame for effective calculation. Based on the proposed method, the stator current signals measured from the compressor induced with different common faults and under different loads are analysed for fault diagnosis. Results show that DTW based on residual signal analysis through the introduction of a reference signal allows the supply components to be suppressed well so that the fault related sideband components are highlighted for obtaining accurate fault detection and diagnosis results. In particular, the root mean square (RMS) values of the residual signal can indicate the differences between the healthy case and different faults under varying discharge pressures. It provides an effective and easy approach to the analysis of motor current signals for better fault diagnosis of the downstream mechanical equipment of motor drives in the time

  2. Artificial neural network application for space station power system fault diagnosis

    Momoh, James A.; Oliver, Walter E.; Dias, Lakshman G.

    1995-01-01

    This study presents a methodology for fault diagnosis using a Two-Stage Artificial Neural Network Clustering Algorithm. Previously, SPICE models of a 5-bus DC power distribution system with assumed constant output power during contingencies from the DDCU were used to evaluate the ANN's fault diagnosis capabilities. This on-going study uses EMTP models of the components (distribution lines, SPDU, TPDU, loads) and power sources (DDCU) of Space Station Alpha's electrical Power Distribution System as a basis for the ANN fault diagnostic tool. The results from the two studies are contrasted. In the event of a major fault, ground controllers need the ability to identify the type of fault, isolate the fault to the orbital replaceable unit level and provide the necessary information for the power management expert system to optimally determine a degraded-mode load schedule. To accomplish these goals, the electrical power distribution system's architecture can be subdivided into three major classes: DC-DC converter to loads, DC Switching Unit (DCSU) to Main bus Switching Unit (MBSU), and Power Sources to DCSU. Each class which has its own electrical characteristics and operations, requires a unique fault analysis philosophy. This study identifies these philosophies as Riddles 1, 2 and 3 respectively. The results of the on-going study addresses Riddle-1. It is concluded in this study that the combination of the EMTP models of the DDCU, distribution cables and electrical loads yields a more accurate model of the behavior and in addition yielded more accurate fault diagnosis using ANN versus the results obtained with the SPICE models.

  3. FaultBuster: data driven fault detection and diagnosis for industrial systems

    Bergantino, Nicola; Caponetti, Fabio; Longhi, Sauro

    2009-01-01

    Efficient and reliable monitoring systems are mandatory to assure the required security standards in industrial complexes. This paper describes the recent developments of FaultBuster, a purely data-driven diagnostic system. It is designed so to be easily scalable to different monitor tasks...

  4. Uranium groundwater anomalies and active normal faulting

    The ability to predict earthquakes is one of the greatest challenges for Earth Sciences. Radon has been suggested as one possible precursor, and its groundwater anomalies associated with earthquakes and water-rock interactions were proposed in several seismogenic areas worldwide as due to possible transport of radon through microfractures, or due to crustal gas fluxes along active faults. However, the use of radon as a possible earthquake's precursor is not clearly linked to crustal deformation. It is shown in this paper that uranium groundwater anomalies, which were observed in cataclastic rocks crossing the underground Gran Sasso National Laboratory, can be used as a possible strain meter in domains where continental lithosphere is subducted. Measurements evidence clear, sharp anomalies from July, 2008 to the end of March, 2009, related to a preparation phase of the seismic swarm, which occurred near L'Aquila, Italy, from October, 2008 to April, 2009. On April 6th, 2009 an earthquake (Mw = 6.3) occurred at 01:33 UT in the same area, with normal faulting on a NW-SE oriented structure about 15 km long, dipping toward SW. In the framework of the geophysical and geochemical models of the area, these measurements indicate that uranium may be used as a possible strain meter in extensional tectonic settings similar to those where the L'Aquila earthquake occurred. (author)

  5. Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

    Nandkumar Wagh

    2014-01-01

    Full Text Available Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP, radial basis function (RBF neural network, and adaptive neurofuzzy inference system (ANFIS has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

  6. Physically-based modeling of speed sensors for fault diagnosis and fault tolerant control in wind turbines

    Weber, Wolfgang; Jungjohann, Jonas; Schulte, Horst

    2014-12-01

    In this paper, a generic physically-based modeling framework for encoder type speed sensors is derived. The consideration takes into account the nominal fault-free and two most relevant fault cases. The advantage of this approach is a reconstruction of the output waveforms in dependence of the internal physical parameter changes which enables a more accurate diagnosis and identification of faulty incremental encoders i.a. in wind turbines. The objectives are to describe the effect of the tilt and eccentric of the encoder disk on the digital output signals and the influence of the accuracy of the speed measurement in wind turbines. Simulation results show the applicability and effectiveness of the proposed approach.

  7. Distributed multisensor fusion for machine condition monitoring fault diagnosis

    Wang, Xue; Zhao, Guohua; Xie, Xin

    2001-09-01

    This paper presents a new general framework for multisensor fusion based on a distributed detection. Parallel processing and distributed multisensor fusion, as rapidly emerging and promising technologies, provides powerful tools for solving this difficult problem, The distribution and parallelism of proposing and confirming of hypothesis in condition and diagnostic is prosed. A combination serial and parallel reconfiguration of n sensors for decision fusion is analyzed. It shows the result for a real-time parallel distributed complex machine condition monitor and fault diagnostic system.

  8. Control Surface Fault Diagnosis for Small Autonomous Aircraft

    Hansen, Søren; Blanke, Mogens

    distributions and change detection methods are employed to reach decisions about not-normal behaviour and it is shown how control surface faults can be diagnosed for a specific UAV without adding additional hardware to the platform. Only telemetry data from the aircraft is used together with a basic model of...... relations between signals within the aircraft. Frequency domain methods are shown to be robust in exploring relevant properties of the signals. The detection is shown to work on data from a real incident where an aileron gets stuck during launch of a UAV....

  9. PWM VLSI Neural Network for Fault Diagnosis%PWM型VLSI神经网络在故障诊断中的应用

    吕琛; 王桂增; 张泽宇

    2005-01-01

    An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information,each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.

  10. Acoustic diagnosis of mechanical fault feature based on reference signal frequency domain semi-blind extraction

    Zeguang YI; Pan, Nan; Liu, Feng

    2015-01-01

    Aiming at fault diagnosis problems caused by complex machinery parts, serious background noises and the application limitations of traditional blind signal processing algorithm to the mechanical acoustic signal processing, a failure acoustic diagnosis based on reference signal frequency domain semi-blind extraction is proposed. Key technologies are introduced: Based on frequency-domain blind deconvolution algorithm, the artificial fish swarm algorithm which is good for global optimization is ...

  11. A Game-Theoretic approach to Fault Diagnosis of Hybrid Systems

    Davide Bresolin

    2011-06-01

    Full Text Available Physical systems can fail. For this reason the problem of identifying and reacting to faults has received a large attention in the control and computer science communities. In this paper we study the fault diagnosis problem for hybrid systems from a game-theoretical point of view. A hybrid system is a system mixing continuous and discrete behaviours that cannot be faithfully modeled neither by using a formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, and we define a Fault Diagnosis Game on them, using two players: the environment and the diagnoser. The environment controls the evolution of the system and chooses whether and when a fault occurs. The diagnoser observes the external behaviour of the system and announces whether a fault has occurred or not. Existence of a winning strategy for the diagnoser implies that faults can be detected correctly, while computing such a winning strategy corresponds to implement a diagnoser for the system. We will show how to determine the existence of a winning strategy, and how to compute it, for some decidable classes of hybrid automata like o-minimal hybrid automata.

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

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

    2016-05-01

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

  13. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs

    Lei, Yaguo; Zuo, Ming J.

    2009-12-01

    A Hilbert-Huang transform (HHT) is a time-frequency technique and has been widely applied to analyzing vibration signals in the field of fault diagnosis of rotating machinery. It analyzes the vibration signals using intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). However, EMD sometimes cannot reveal the signal characteristics accurately because of the problem of mode mixing. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate this problem. The IMFs generated by EEMD have different sensitivity to faults. Some IMFs are sensitive and closely related to the faults but others are irrelevant. To enhance the accuracy of the HHT in fault diagnosis of rotating machinery, an improved HHT based on EEMD and sensitive IMFs is proposed in this paper. Simulated signals demonstrate the effectiveness of the improved HHT in diagnosing the faults of rotating machinery. Finally, the improved HHT is applied to diagnosing an early rub-impact fault of a heavy oil catalytic cracking machine set, and the application results prove that the improved HHT is superior to the HHT based on all IMFs of EMD.

  14. Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis

    He, Shuilong; Chen, Jinglong; Zhou, Zitong; Zi, Yanyang; Wang, Yanxue; Wang, Xiaodong

    2016-08-01

    Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what's more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.

  15. A Model of Intelligent Fault Diagnosis of Power Equipment Based on CBR

    Gang Ma

    2015-01-01

    Full Text Available Nowadays the demand of power supply reliability has been strongly increased as the development within power industry grows rapidly. Nevertheless such large demand requires substantial power grid to sustain. Therefore power equipment’s running and testing data which contains vast information underpins online monitoring and fault diagnosis to finally achieve state maintenance. In this paper, an intelligent fault diagnosis model for power equipment based on case-based reasoning (IFDCBR will be proposed. The model intends to discover the potential rules of equipment fault by data mining. The intelligent model constructs a condition case base of equipment by analyzing the following four categories of data: online recording data, history data, basic test data, and environmental data. SVM regression analysis was also applied in mining the case base so as to further establish the equipment condition fingerprint. The running data of equipment can be diagnosed by such condition fingerprint to detect whether there is a fault or not. Finally, this paper verifies the intelligent model and three-ratio method based on a set of practical data. The resulting research demonstrates that this intelligent model is more effective and accurate in fault diagnosis.

  16. Active faults of the Baikal depression

    Levi, K.G.; Miroshnichenko, A.I.; San'kov, V. A.; Babushkin, S.M.; Larkin, G.V.; Badardinov, A.A.; Wong, H.K.; Colman, S.; Delvaux, D.

    1997-01-01

    The Baikal depression occupies a central position in the system of the basins of the Baikal Rift Zone and corresponds to the nucleus from which the continental lithosphere began to open. For different reasons, the internal structure of the Lake Baikal basin remained unknown for a long time. In this article, we present for the first time a synthesis of the data concerning the structure of the sedimentary section beneath Lake Baikal, which were obtained by complex seismic and structural investigations, conducted mainly from 1989 to 1992. We make a brief description of the most interesting seismic profiles which provide a rough idea of a sedimentary unit structure, present a detailed structural interpretation and show the relationship between active faults in the lake, heat flow anomalies and recent hydrothermalism.

  17. Improving Diagnosability of Hybrid Systems through Active Diagnosis

    National Aeronautics and Space Administration — Fault diagnosis is key to ensuring system safety through fault-adaptive control. This task is diffcult in hybrid systems with combined continuous and discrete...

  18. Fault mirrors in seismically active fault zones: A fossil of small earthquakes at shallow depths

    Kuo, Li-Wei; Song, Sheng-Rong; Suppe, John; Yeh, En-Chao

    2016-03-01

    Fault mirrors (FMs) are naturally polished and glossy fault slip surfaces that can record seismic deformation at shallow depths. They are important for investigating the processes controlling dynamic fault slip. We characterize FMs in borehole samples from the hanging wall damage zone of the active Hsiaotungshi reverse fault, Taiwan. Here we report the first documented occurrence of the combination of silica gel and melt patches coating FMs, with the silica gel resembling those observed on experimentally formed FMs that were cataclastically generated. In addition, the melt patches, which are unambiguous indicators of coseismic slip, suggest that the natural FMs were produced at seismic rates, presumably resulting from flash heating at asperities on the slip surfaces. Since flash heating is efficient at small slip, we propose that these natural FMs represent fossils of small earthquakes, formed in either coseismic faulting and folding or aftershock deformation in the active Taiwan fold-and-thrust belt.

  19. Switched Fault Diagnosis Approach for Industrial Processes based on Hidden Markov Model

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

    2015-11-01

    Traditional fault diagnosis methods based on hidden Markov model (HMM) use a unified method for feature extraction, such as principal component analysis (PCA), kernel principal component analysis (KPCA) and independent component analysis (ICA). However, every method has its own limitations. For example, PCA cannot extract nonlinear relationships among process variables. So it is inappropriate to extract all features of variables by only one method, especially when data characteristics are very complex. This article proposes a switched feature extraction procedure using PCA and KPCA based on nonlinearity measure. By the proposed method, we are able to choose the most suitable feature extraction method, which could improve the accuracy of fault diagnosis. A simulation from the Tennessee Eastman (TE) process demonstrates that the proposed approach is superior to the traditional one based on HMM and could achieve more accurate classification of various process faults.

  20. Fault diagnosis using noise modeling and a new artificial immune system based algorithm

    Abbasi, Farshid; Mojtahedi, Alireza; Ettefagh, Mir Mohammad

    2015-12-01

    A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.

  1. STUDY ON NATURAL LANGUAGE INTERFACE OF NETWORK FAULT DIAGNOSIS EXPERT SYSTEM

    Liu Peiqi; Li Zengzhi; Zhao Yinliang

    2006-01-01

    The expert system is an important field of the artificial intelligence. The traditional interface of the expert system is the command, menu and window at present. It limits the application of the expert system and embarrasses the enthusiasm of using expert system. Combining with the study on the expert system of network fault diagnosis, the natural language interface of the expert system has been discussed in this article. This interface can understand and generate Chinese sentences. Using this interface, the user and field experts can use the expert system to diagnose the fault of network conveniently. In the article, first, the extended production rule has been proposed. Then the methods of Chinese sentence generation from conceptual graphs and the model of expert system are introduced in detail. Using this model, the network fault diagnosis expert system and its natural language interface have been developed with Prolog.

  2. A Fault Diagnosis Method for Rotating Machinery Based on PCA and Morlet Kernel SVM

    Shaojiang Dong

    2014-01-01

    Full Text Available A novel method to solve the rotating machinery fault diagnosis problem is proposed, which is based on principal components analysis (PCA to extract the characteristic features and the Morlet kernel support vector machine (MSVM to achieve the fault classification. Firstly, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD to obtain the corresponding intrinsic mode function (IMF. The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the PCA is introduced to extract the characteristic features and reduce the dimension. The characteristic features are input into the MSVM to train and construct the running state identification model; the rotating machinery running state identification is realized. The running states of a bearing normal inner race and several inner races with different degree of fault were recognized; the results validate the effectiveness of the proposed algorithm.

  3. FAULT DETECTION AND DIAGNOSIS ON A PWM INVERTER BY DIFFERENT TECHNIQUES

    S. Chafei

    2008-06-01

    Full Text Available This paper investigates the use of different techniques for fault detection in voltage-fed asynchronous machine drive systems. With the proposed techniques it is possible to detect and identify the power switch in which the fault has occurred. A diagnosis system which uses only the input variables of the drive is presented. It is based on the analysis of the current-vector trajectory, of the instantaneous frequency in faulty mode, and the evaluation of machine state variables which are processed due to the machine control algorithm. With this algorithm a fast an reliable fault detection can be realized. Furthermore limited drive operation in case of a fault mode will be discussed. All obtained results are based on computer simulation. These knowledge based methods have been test in simulation.

  4. State-Space GMDH Neural Networks for Actuator Robust Fault Diagnosis

    MRUGALSKI, M.

    2012-08-01

    Full Text Available Most fault diagnosis methods focus on the fault detection of the system or sensors and do not take into account the problem of the fault detection and isolation of the actuators, which are an important part of the contemporary industrial systems. To solve such a problem, the system outputs and inputs estimator based on a dynamic Group Method of Data Handling neural network in the state-space representation is proposed. In particular, the methodology of the adaptive thresholds calculation for system inputs and outputs is presented. The approach is based on the application of the Unscented Kalman Filter and Unknown Input Filter is presented. This result enables performing robust fault detection and isolation of the actuators. The final part of the paper presents an application study, which confirms the effectiveness of the proposed approach.

  5. Fault detection and diagnosis in nonlinear systems a differential and algebraic viewpoint

    Martinez-Guerra, Rafael

    2014-01-01

    The high reliability required in industrial processes has created the necessity of detecting abnormal conditions, called faults, while processes are operating. The term fault generically refers to any type of process degradation, or degradation in equipment performance because of changes in the process's physical characteristics, process inputs or environmental conditions. This book is about the fundamentals of fault detection and diagnosis in a variety of nonlinear systems which are represented by ordinary differential equations. The fault detection problem is approached from a differential algebraic viewpoint, using residual generators based upon high-gain nonlinear auxiliary systems (‘observers’). A prominent role is played by the type of mathematical tools that will be used, requiring knowledge of differential algebra and differential equations. Specific theorems tailored to the needs of the problem-solving procedures are developed and proved. Applications to real-world problems, both with constant an...

  6. Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM

    Jie Xu

    2013-01-01

    Full Text Available New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA and sparse support vector machine (SVM. The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA. The data are firstly mapped into high-dimensional feature subspace. Then, the ICA algorithm seeks the projection directions in the KPCA whitened space. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. If the statistical indexes exceed the predefined control limit, a fault may have occurred. Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE chemical process. The simulation results show that the proposed method can identify various types of faults accurately and rapidly.

  7. Real-time diagnosis of incipient multiple faults with application for nuclear power plants

    This paper presents a method which can diagnose the incipient multiple faults in real time in large-scale systems such as nuclear power plants. To verify the performance of the proposed method, it is applied to the diagnosis of a pressurizer and its related subsystems in the Kori Nuclear Power Plant unit 2 in Korea under a transient condition

  8. Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

    Rusmir Bajric

    2016-01-01

    Full Text Available Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.

  9. Diagnosis of inverter switch open circuit faults based on neutral point voltage signal analysis

    Liwei GUO

    Full Text Available Using the current signal to diagnose inverter faults information is apt to be affected by the load, noise and other factors; besides, it requires long diagnosis period with special algorithms and the diagnosis result is easily to be incorrect with no-load or light-load. Focusing on this issue, the logical analysis method is proposed for correlation logical analysis of leg neutral-point voltage and pulse signal to realize the diagnosis of the open circuit faults of inverter switches. The logical expressions of output signals of inverter power tube open-circuit faults is put forward and interrelated hardware circuit design is also elaborated. Delaying the rising edge of inverter power tube's pulse signal can effectively avoid the diagnosis error caused by the power tube's switching on/off. The experiment results show that the method can effectively diagnose the open-circuit faults of single-phase single power tube inverter in real-time and the hardware circuit cost is low, which shows it is effective and feasible.

  10. Wavelet Entropy-Based Traction Inverter Open Switch Fault Diagnosis in High-Speed Railways

    Keting Hu

    2016-03-01

    Full Text Available In this paper, a diagnosis plan is proposed to settle the detection and isolation problem of open switch faults in high-speed railway traction system traction inverters. Five entropy forms are discussed and compared with the traditional fault detection methods, namely, discrete wavelet transform and discrete wavelet packet transform. The traditional fault detection methods cannot efficiently detect the open switch faults in traction inverters because of the low resolution or the sudden change of the current. The performances of Wavelet Packet Energy Shannon Entropy (WPESE, Wavelet Packet Energy Tsallis Entropy (WPETE with different non-extensive parameters, Wavelet Packet Energy Shannon Entropy with a specific sub-band (WPESE3,6, Empirical Mode Decomposition Shannon Entropy (EMDESE, and Empirical Mode Decomposition Tsallis Entropy (EMDETE with non-extensive parameters in detecting the open switch fault are evaluated by the evaluation parameter. Comparison experiments are carried out to select the best entropy form for the traction inverter open switch fault detection. In addition, the DC component is adopted to isolate the failure Isolated Gate Bipolar Transistor (IGBT. The simulation experiments show that the proposed plan can diagnose single and simultaneous open switch faults correctly and timely.

  11. NEW FEATURE SELECTION METHOD IN MACHINE FAULT DIAGNOSIS

    Wang Xinfeng; Qiu Jing; Liu Guanjun

    2005-01-01

    Aiming to deficiency of the filter and wrapper feature selection methods, a new method based on composite method of filter and wrapper method is proposed. First the method filters original features to form a feature subset which can meet classification correctness rate, then applies wrapper feature selection method select optimal feature subset. A successful technique for solving optimization problems is given by genetic algorithm (GA). GA is applied to the problem of optimal feature selection. The composite method saves computing time several times of the wrapper method with holding the classification accuracy in data simulation and experiment on bearing fault feature selection. So this method possesses excellent optimization property, can save more selection time, and has the characteristics of high accuracy and high efficiency.

  12. Active Fault Exploration and Seismic Hazard Assessment in Fuzhou City

    Zhu Jinfang; Han Zhujun; Huang Zonglin; Xu Xiwei; Zheng Rongzhang; Fang Shengmin; Bai Denghai; Wang Guangcai; Min Wei; Wen Xueze

    2005-01-01

    It has been proven by a number of earthquake case studies that an active fault-induced earthquake beneath a city can be devastating. It is an urgent issue for seismic hazard reduction to explore the distribution of active faults beneath the urban area and identify the seismic source and the risks underneath. As a pilot project of active fault exploration in China, the project, entitled "Active fault exploration and seismic hazard assessment in Fuzhou City",started in early 2001 and passed the check before acceptance of China Earthquake Administration in August 2004. The project was aimed to solve a series of scientific issues such as fault location, dating, movement nature, deep settings, seismic risk and hazard,preparedness of earthquake prevention and disaster reduction, and etc. by means of exploration and assessment of active faults by stages, i.e., the preliminary survey and identification of active faults in target area, the exploration of deep seismotectonic settings, the risk evaluation of active seismogenic faults, the construction of geographic information system of active faults, and so on. A lot of exploration methods were employed in the project such as the detection of absorbed mercury, free mercury and radon in soil, the geological radar,multi-channel DC electrical method, tsansient electromagnetic method, shallow seismic refraction and reflection, effect contrast of explored sources, and various sounding experiments, to establish the buried Quaternary standard section of the Fuzhou basin. By summing up, the above explorations and experiments have achieved the following results and conclusions:(1) The results of the synthetic pilot project of active fault exploration in Fuzhou City demonstrate that, on the basis of sufficient collection, sorting out and analysis of geological,geophysical and borehole data, the best method for active fault exploration (location) and seismic risk assessnent (dating and characterizing) in urban area is the combination

  13. A New Method of PV Array Faults Diagnosis in Smart Grid

    Ze Cheng

    2014-01-01

    Full Text Available A new fault diagnosis method is proposed for PV arrays with SP connection in this study, the advantages of which are that it would minimize the number of sensors needed and that the accuracy and anti-interference ability are improved with the introduction of fuzzy group decision-making theory. We considered five “decision makers” contributing to the diagnosis of PV array faults, including voltage, current, environmental temperature, panel temperature, and solar illumination. The accuracy and reliability of the proposed method were verified experimentally, and the possible factors contributing to diagnosis deviation were analyzed, based on which solutions were suggested to reduce or eliminate errors in aspects of hardware and software.

  14. Multi-scale autocorrelation via morphological wavelet slices for rolling element bearing fault diagnosis

    Li, Chuan; Liang, Ming; Zhang, Yi; Hou, Shumin

    2012-08-01

    Fault features of rolling element bearings can be reflected by geometrical structures of the bearing vibration signals. These symptoms, however, often spread over various morphological scales without a known pattern. For this reason, we propose a multi-scale autocorrelation via morphological wavelet slices (MAMWS) approach to detect bearing fault signatures. The vibration measurement of a bearing is decomposed using morphological stationary wavelet with different resolutions of structuring elements. The extracted temporal components are then transformed to form a frequency-domain view of morphological slices by the Fourier transform. Although this three-dimensional representation is more intuitive in terms of fault diagnosis, the existence of the noise may reduce its readability. Hence the autocorrelation function is exploited to produce a multi-scale autocorrelation spectrogram from which the maximal autocorrelation values of all frequencies are aggregated into an ichnographical spectral representation. Accordingly the fault signature is highlighted for easy diagnosis of bearing faults. The effectiveness of the proposed approach has been demonstrated by both the simulation and experimental signal analyses.

  15. Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

    Xinghui Zhang

    2015-01-01

    Full Text Available Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

  16. Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

    Liang Hua

    2015-01-01

    Full Text Available Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

  17. Development of the Task-Based Expert System for Machine Fault Diagnosis

    Bo, Ma; Zhi-nong, Jiang; Zhong-qing, Wei

    2012-05-01

    The operating mechanism of expert systems widely used in fault diagnosis is to formulate a set of diagnostic rules, according to the mechanism and symptoms of faults, in order to instruct the fault diagnosis or directly give diagnostic results. In practice, due to differences existing in such aspects as production technology, drivers, etc., a certain fault may derive from different causes, which will lead to a lower diagnostic accuracy of expert systems. Besides, a variety of expert systems now available have a dual problem of low generality and low expandability, of which the former can lead to the repeated development of expert systems for different machines, while the latter restricts users from expanding the system. Aimed at these problems, a type of task-based software architecture of expert system is proposed in this paper, which permits a specific optimization based on a set of common rules, and allows users to add or modify rules on a man-machine dialog so as to keep on absorbing and improving the expert knowledge. Finally, the integration of the expert system with the condition monitoring system to implement the automatic and semi-automatic diagnosis is introduced.

  18. Development of the Task-Based Expert System for Machine Fault Diagnosis

    The operating mechanism of expert systems widely used in fault diagnosis is to formulate a set of diagnostic rules, according to the mechanism and symptoms of faults, in order to instruct the fault diagnosis or directly give diagnostic results. In practice, due to differences existing in such aspects as production technology, drivers, etc., a certain fault may derive from different causes, which will lead to a lower diagnostic accuracy of expert systems. Besides, a variety of expert systems now available have a dual problem of low generality and low expandability, of which the former can lead to the repeated development of expert systems for different machines, while the latter restricts users from expanding the system. Aimed at these problems, a type of task-based software architecture of expert system is proposed in this paper, which permits a specific optimization based on a set of common rules, and allows users to add or modify rules on a man-machine dialog so as to keep on absorbing and improving the expert knowledge. Finally, the integration of the expert system with the condition monitoring system to implement the automatic and semi-automatic diagnosis is introduced.

  19. An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system

    The commercial viability of PEMFC (proton exchange membrane fuel cell) systems depends on using effective fault diagnosis technologies in PEMFC systems. However, many researchers have experimentally studied PEMFC (proton exchange membrane fuel cell) systems without considering certain fault conditions. In this paper, an ANN (artificial neural network) ensemble method is presented that improves the stability and reliability of the PEMFC systems. In the first part, a transient model giving it flexibility in application to some exceptional conditions is built. The PEMFC dynamic model is built and simulated using MATLAB. In the second, using this model and experiments, the mechanisms of four different faults in PEMFC systems are analyzed in detail. Third, the ANN ensemble for the fault diagnosis is built and modeled. This model is trained and tested by the data. The test result shows that, compared with the previous method for fault diagnosis of PEMFC systems, the proposed fault diagnosis method has higher diagnostic rate and generalization ability. Moreover, the partial structure of this method can be altered easily, along with the change of the PEMFC systems. In general, this method for diagnosis of PEMFC has value for certain applications. - Highlights: • We analyze the principles and mechanisms of the four faults in PEMFC (proton exchange membrane fuel cell) system. • We design and model an ANN (artificial neural network) ensemble method for the fault diagnosis of PEMFC system. • This method has high diagnostic rate and strong generalization ability

  20. Combination Method of Principal Component Analysis and Support Vector Machine for On-line Process Monitoring and Fault Diagnosis

    2006-01-01

    On-line monitoring and fault diagnosis of chemical process is extremely important for operation safety and product quality. Principal component analysis (PCA) has been widely used in multivariate statistical process monitoring for its ability to reduce processes dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults correctly in complex chemical process.Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. In this paper, an integrated method is applied for process monitoring and fault diagnosis, which combines PCA for fault feature extraction and multiple SVMs for identification of different fault sources. This approach is verified and illustrated on the Tennessee Eastman benchmark process as a case study.Results show that the proposed PCA-SVMs method has good diagnosis capability and overall diagnosis correctness rate.

  1. A study on the development of an automatic fault diagnosis system for testing NPP digital electronic circuits

    This paper describes a study on the development of an automatic fault diagnosis system for testing digital electronic circuits of nuclear power plants. Compared with the other conventional fault diagnosis systems, the system described in this paper uses Artificial Intelligence technique of model based reasoning and corroboration, which makes fault diagnosis much more efficient. In order to reduce the testing time, an optimal testing set which means a minimal testing set to determine whether or not the circuit is fault-free and to locate the faulty gate was derived. Compared with the testing using an exhaustive testing set, the testing using the optimal testing set makes fault diagnosis much more fast. Since the system diagnoses the circuit boards bases only on input and output signals, it can be further developed for on-line testing. The system was implemented on a microprocessor and was applied for Universal Circuit board testing of the Solid State protection System in nuclear power plants

  2. NC Machine Tools Fault Diagnosis Based on Kernel PCA and k-Nearest Neighbor Using Vibration Signals

    Zhou Yuqing

    2015-01-01

    Full Text Available This paper focuses on the fault diagnosis for NC machine tools and puts forward a fault diagnosis method based on kernel principal component analysis (KPCA and k-nearest neighbor (kNN. A data-dependent KPCA based on covariance matrix of sample data is designed to overcome the subjectivity in parameter selection of kernel function and is used to transform original high-dimensional data into low-dimensional manifold feature space with the intrinsic dimensionality. The kNN method is modified to adapt the fault diagnosis of tools that can determine thresholds of multifault classes and is applied to detect potential faults. An experimental analysis in NC milling machine tools is developed; the testing result shows that the proposed method is outperforming compared to the other two methods in tool fault diagnosis.

  3. A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

    Al-Bugharbee, Hussein; Trendafilova, Irina

    2016-05-01

    This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pretreatment allows the use of a linear time invariant autoregressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasises the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal. The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process.

  4. Quantitative NDE thermography for fault diagnosis of ball bearings with micro-foreign substances

    In this study, a non-destructive evaluation (NDE) method is proposed for ball bearings contaminated with micro foreign substances, which were inserted into a ball bearing to create a defective specimen. The non-contact quantitative infrared thermographic technique was applied for NDE condition monitoring. Passive thermographic experiments were conducted to perform early fault diagnosis, for bearings operated at optimized torque status under a dynamic load condition. The temperature profiles for normal and defective specimens were quantitatively compared, and the thermographic data analyzed. Based on the NDE results, the temperature characteristics and abnormal fault detection of the ball bearing were quantitatively analyzed according to the rise in temperature.

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

    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.

  6. Application of ″Black Box″ to fault diagnosis of rotating machine

    2001-01-01

    Presents the successful application of an accident recallingsystem in the Linyuan refine oil works as part of a rotating machine vibration state monitoring and fault diagnosis system which consists of vibration pre-processor,comparator and plus generator, and system gives the CPU of vibration state monitoring and fault diagnose system an interrupt plus when the vibration amplitude exceed a dangerous level to enable it to sample and store the vibration data and gets the accident data timely because the interval between the happening of accident and the beginning of sampling are shorter than 1 ms.

  7. Diagnosis of Multiple Fixture Faults in Multiple-Station Manufacturing Processes Based on State Space Approach

    田兆青; 来新民; 林忠钦

    2004-01-01

    Dimensional quality is one of the most critical challenges in industries, which uses the multistage manufacturing process (MMP) such as assembly and machining for automotive and aerospace industries. According to investigations, fixture faults accounted for 72% of all the dimensional faults. Previous studies focused on only one fault or multiple faults occurred in one station or one fault in multiple stations, but these cases rarely appear in the real manufacturing. This paper presents a method for diagnosis of multiple fixture faults in the multi-station manufacturing process. The proposed method is based on the state space model of the MMP processes, which carries the information of the fixture layout geometry and sensor position. To identify the root cause, three continuous steps were used: a) development of the state space model and the construction of the statistics variables on offline mode, b) measurement of the coordinate measuring machines data on online mode and calculation of the statistics variables, and c) diagnostic algorithm for identifying the root cause. The presented paper integrates the state space model of the manufacturing processes and hypothesis test considering the impact of the measure noises. A case study verifies the proposed method.

  8. Optimum IMFs Selection Based Envelope Analysis of Bearing Fault Diagnosis in Plunger Pump

    Wenliao Du

    2016-01-01

    Full Text Available As the plunger pump always works in a complicated environment and the hydraulic cycle has an intrinsic fluid-structure interaction character, the fault information is submerged in the noise and the disturbance impact signals. For the fault diagnosis of the bearings in plunger pump, an optimum intrinsic mode functions (IMFs selection based envelope analysis was proposed. Firstly, the Wigner-Ville distribution was calculated for the acquired vibration signals, and the resonance frequency brought on by fault was obtained. Secondly, the empirical mode decomposition (EMD was employed for the vibration signal, and the optimum IMFs and the filter bandwidth were selected according to the Wigner-Ville distribution. Finally, the envelope analysis was utilized for the selected IMFs filtered by the band pass filter, and the fault type was recognized by compared with the bearing character frequencies. For the two modes, inner race fault and compound fault in the inner race and roller of rolling element bearing in plunger pump, the experiments show that a promising result is achieved.

  9. Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

    Xinyi Yang

    2016-01-01

    Full Text Available A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine.

  10. Neural network based expert system for fault diagnosis of particle accelerators

    Particle accelerators are generators that produce beams of charged particles, acquiring different energies, depending on the accelerator type. The MGC-20 cyclotron is a cyclic particle accelerator used for accelerating protons, deuterons, alpha particles, and helium-3 to different energies. Its applications include isotope production, nuclear reaction, and mass spectroscopy studies. It is a complicated machine, it consists of five main parts, the ion source, the deflector, the beam transport system, the concentric and harmonic coils, and the radio frequency system. The diagnosis of this device is a very complex task. it depends on the conditions of 27 indicators of the control panel of the device. The accurate diagnosis can lead to a high system reliability and save maintenance costs. so an expert system for the cyclotron fault diagnosis is necessary to be built. In this thesis , a hybrid expert system was developed for the fault diagnosis of the MGC-20 cyclotron. Two intelligent techniques, multilayer feed forward back propagation neural network and the rule based expert system, are integrated as a pre-processor loosely coupled model to build the proposed hybrid expert system. The architecture of the developed hybrid expert system consists of two levels. The first level is two feed forward back propagation neural networks, used for isolating the faulty part of the cyclotron. The second level is the rule based expert system, used for troubleshooting the faults inside the isolated faulty part. 4-6 tabs., 4-5 figs., 36 refs

  11. Robust fault diagnosis of physical systems in operation. Ph.D. Thesis - Rutgers - The State Univ.

    Abbott, Kathy Hamilton

    1991-01-01

    Ideas are presented and demonstrated for improved robustness in diagnostic problem solving of complex physical systems in operation, or operative diagnosis. The first idea is that graceful degradation can be viewed as reasoning at higher levels of abstraction whenever the more detailed levels proved to be incomplete or inadequate. A form of abstraction is defined that applies this view to the problem of diagnosis. In this form of abstraction, named status abstraction, two levels are defined. The lower level of abstraction corresponds to the level of detail at which most current knowledge-based diagnosis systems reason. At the higher level, a graph representation is presented that describes the real-world physical system. An incremental, constructive approach to manipulating this graph representation is demonstrated that supports certain characteristics of operative diagnosis. The suitability of this constructive approach is shown for diagnosing fault propagation behavior over time, and for sometimes diagnosing systems with feedback. A way is shown to represent different semantics in the same type of graph representation to characterize different types of fault propagation behavior. An approach is demonstrated that threats these different behaviors as different fault classes, and the approach moves to other classes when previous classes fail to generate suitable hypotheses. These ideas are implemented in a computer program named Draphys (Diagnostic Reasoning About Physical Systems) and demonstrated for the domain of inflight aircraft subsystems, specifically a propulsion system (containing two turbofan systems and a fuel system) and hydraulic subsystem.

  12. Faults

    National Oceanic and Atmospheric Administration, Department of Commerce — Through the study of faults and their effects, much can be learned about the size and recurrence intervals of earthquakes. Faults also teach us about crustal...

  13. Contribution of qualitative analysis and fuzzy sets to industrial process fault diagnosis: Application to the DIAPASON project

    The construction of fault indicators is the foundation of model-based fault diagnosis. The development of precise mathematical models for complex facilities is generally difficult and expensive; new and less constraining techniques, notably seeking to account for behaviour, open new perspectives for fault detection and diagnosis. The authors propose a combined approach based on quantitative processing with qualitative assessment of the results. A veritable numerical-symbolic interface then ensure a more satisfactory balance between the two levels of knowledge - analytic and heuristic - necessary to optimize the performance of a diagnostic procedure. Our supervision support system DIAPASON provides operators of industrial continuous processes with an aid to watch and diagnosis. The reasoning is based on a casual graph and on a knowledge base. After an overview of qualitative simulation, defect diagnosis and fault diagnosis, the way in which these three cooperate in DIAPASON is amplified. (author). 21 refs, 5 figs

  14. Contribution of qualitative analysis and fuzzy sets to industrial process fault diagnosis: application to the Diapason project

    The construction of fault indicators is the foundation of model-based fault diagnosis. The development of precise mathematical models for complex facilities is generally difficult and expensive; new and less constraining techniques, notably seeking to account for behaviour, open new perspectives for fault detection and diagnosis. The authors propose a combined approach based on quantitative processing with qualitative assessment of the results. A veritable numerical-symbolic interface then ensures a more satisfactory balance between the two levels of knowledge - analytic and heuristic -necessary to optimize the performance of a diagnostic procedure. Our supervision support system DIAPASON provides operators of industrial continuous processes with an aid to watch and diagnosis. The reasoning is based on a causal graph and on a knowledge base. After an overview of qualitative simulation, defect diagnosis and fault diagnosis, the way in which these three cooperate in DIAPASON is amplified. (authors). 21 refs., 5 figs

  15. Data mining based sensor fault diagnosis and validation for building air conditioning system

    A strategy based on the data mining (DM) method is developed to detect and diagnose sensor faults based on the past running performance data in heating, ventilating and air conditioning (HVAC) systems, combining a rough set approach and an artificial neural network (ANN). The reduced information is used to develop classification rules and train the neural network to infer appropriate parameters. The differences between measured thermodynamic states and predicted states obtained from models for normal performance (residuals) are used as performance indices for sensor fault detection and diagnosis. Real test results from a real HVAC system show that only the temperature and humidity measurements of many air handling units (AHU) can work very well as the measurements to distinguish simultaneous temperature sensor faults of the supply chilled water (SCW) and return chilled water (RCW)

  16. Evolving Neural Network Using Genetic Algorithm for Faults Diagnosis of Urban Rail Vehicle Auxiliary Inverter

    Ji Changxu

    2013-07-01

    Full Text Available In this article, an efficient method is proposed to diagnose urban rail vehicle auxiliary inverter faults based on wavelet packet neural network and genetic algorithm. Firstly, the original signals are decomposed into different frequency subbands by wavelet packet. Secondly, the wavelet packet energy eigenvector is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train neural network, In order to improve the function approximation accuracy and general capability of the neural network system, an efficient genetic algorithm approach is used to adjust the parameters of translation and weights functions. The experiment shows that the GA-ANN model gives superior result. This approach can be used as a useful tool for the auxiliary inverter fault diagnosis.

  17. Diagnosis of Intermittent Faults in IGBTs Using the Latent Nestling Method with Hybrid Coloured Petri Nets

    Leonardo Rodriguez-Urrego

    2015-01-01

    Full Text Available This paper presents a fault diagnosis application of the Latent Nestling Method to IGBTs. The paper extends the Latent Nestling Method based in Coloured Petri Nets (CPNs to hybrid systems in such a manner that IGBTs performance can be modeled. CPNs allow for an enhanced capability for synthesis and modeling in contrast to the classical phenomena of combinational state explosion when Finite State Machine methods are applied. We present an IGBT model with different fault modes including those of intermittent nature that can be used advantageously as predictive symptoms within a predictive maintenance strategy. Ageing stress tests have been experimentally applied to the IGBTs modules and intermittent faults are diagnosed as precursors of permanent failures. In addition, ageing is validated with morphological analysis (Scanning Electron Microscopy and semiqualitative analysis (Energy Dispersive Spectrometry.

  18. Real-Time Diagnosis of Faults Using a Bank of Kalman Filters

    Kobayashi, Takahisa; Simon, Donald L.

    2006-01-01

    A new robust method of automated real-time diagnosis of faults in an aircraft engine or a similar complex system involves the use of a bank of Kalman filters. In order to be highly reliable, a diagnostic system must be designed to account for the numerous failure conditions that an aircraft engine may encounter in operation. The method achieves this objective though the utilization of multiple Kalman filters, each of which is uniquely designed based on a specific failure hypothesis. A fault-detection-and-isolation (FDI) system, developed based on this method, is able to isolate faults in sensors and actuators while detecting component faults (abrupt degradation in engine component performance). By affording a capability for real-time identification of minor faults before they grow into major ones, the method promises to enhance safety and reduce operating costs. The robustness of this method is further enhanced by incorporating information regarding the aging condition of an engine. In general, real-time fault diagnostic methods use the nominal performance of a "healthy" new engine as a reference condition in the diagnostic process. Such an approach does not account for gradual changes in performance associated with aging of an otherwise healthy engine. By incorporating information on gradual, aging-related changes, the new method makes it possible to retain at least some of the sensitivity and accuracy needed to detect incipient faults while preventing false alarms that could result from erroneous interpretation of symptoms of aging as symptoms of failures. The figure schematically depicts an FDI system according to the new method. The FDI system is integrated with an engine, from which it accepts two sets of input signals: sensor readings and actuator commands. Two main parts of the FDI system are a bank of Kalman filters and a subsystem that implements FDI decision rules. Each Kalman filter is designed to detect a specific sensor or actuator fault. When a sensor

  19. Fault diagnosis for manifold absolute pressure sensor(MAP) of diesel engine based on Elman neural network observer

    Wang, Yingmin; Zhang, Fujun; Cui, Tao; Zhou, Jinlong

    2016-03-01

    Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can't be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015-0.017 5 and sample error is controlled within 0-0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis; the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

  20. Fault diagnosis for the heat exchanger of the aircraft environmental control system based on the strong tracking filter.

    Ma, Jian; Lu, Chen; Liu, Hongmei

    2015-01-01

    The aircraft environmental control system (ECS) is a critical aircraft system, which provides the appropriate environmental conditions to ensure the safe transport of air passengers and equipment. The functionality and reliability of ECS have received increasing attention in recent years. The heat exchanger is a particularly significant component of the ECS, because its failure decreases the system's efficiency, which can lead to catastrophic consequences. Fault diagnosis of the heat exchanger is necessary to prevent risks. However, two problems hinder the implementation of the heat exchanger fault diagnosis in practice. First, the actual measured parameter of the heat exchanger cannot effectively reflect the fault occurrence, whereas the heat exchanger faults are usually depicted by utilizing the corresponding fault-related state parameters that cannot be measured directly. Second, both the traditional Extended Kalman Filter (EKF) and the EKF-based Double Model Filter have certain disadvantages, such as sensitivity to modeling errors and difficulties in selection of initialization values. To solve the aforementioned problems, this paper presents a fault-related parameter adaptive estimation method based on strong tracking filter (STF) and Modified Bayes classification algorithm for fault detection and failure mode classification of the heat exchanger, respectively. Heat exchanger fault simulation is conducted to generate fault data, through which the proposed methods are validated. The results demonstrate that the proposed methods are capable of providing accurate, stable, and rapid fault diagnosis of the heat exchanger. PMID:25823010

  1. An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique

    This research presents a new intelligent fault diagnosis and condition monitoring system for classification of different conditions of cooling radiator using infrared thermal images. The system was adopted to classify six types of cooling radiator faults; radiator tubes blockage, radiator fins blockage, loose connection between fins and tubes, radiator door failure, coolant leakage, and normal conditions. The proposed system consists of several distinct procedures including thermal image acquisition, image pre-processing, image processing, two-dimensional discrete wavelet transform (2D-DWT), feature extraction, feature selection using a genetic algorithm (GA), and finally classification by artificial neural networks (ANNs). The 2D-DWT is implemented to decompose the thermal images. Subsequently, statistical texture features are extracted from the original images and are decomposed into thermal images. The significant selected features are used to enhance the performance of the designed ANN classifier for the 6 types of cooling radiator conditions (output layer) in the next stage. For the tested system, the input layer consisted of 16 neurons based on the feature selection operation. The best performance of ANN was obtained with a 16-6-6 topology. The classification results demonstrated that this system can be employed satisfactorily as an intelligent condition monitoring and fault diagnosis for a class of cooling radiator. - Highlights: • Intelligent fault diagnosis of cooling radiator using thermal image processing. • Thermal image processing in a multiscale representation structure by 2D-DWT. • Selection features based on a hybrid system that uses both GA and ANN. • Application of ANN as classifier. • Classification accuracy of fault detection up to 93.83%

  2. Techniques for Surveying Urban Active Faults by Seismic Methods

    Xu Mingcai; Gao Jinghua; Liu Jianxun; Rong Lixin

    2005-01-01

    Using the seismic method to detect active faults directly below cities is an irreplaceable prospecting technique. The seismic method can precisely determine the fault position. Seismic method itself can hardly determine the geological age of fault. However, by considering in connection with the borehole data and the standard geological cross-section of the surveyed area, the geological age of reflected wave group can be qualitatively (or semi-quantitatively)determined from the seismic depth profile. To determine the upper terminal point of active faults directly below city, it is necessary to use the high-resolution seismic reflection technique.To effectively determine the geometric feature of deep faults, especially to determine the relation between deep and shallow fracture structures, the seismic reflection method is better than the seismic refraction method.

  3. Quaternary seismo-tectonic activity of the Polochic Fault, Guatemala

    Authemayou, Christine; Brocard, Gilles; TEYSSIER, Christian; Suski, Barbara; Cosenza, Beatriz; Moran-Ical, Sergio; Gonzalez-Veliz, Claussen Walther; Aguilar-Hengstenberg, Miguel Angel; Holliger, Klaus

    2012-01-01

    The Polochic-Motagua fault system is part of the sinistral transform boundary between the North American and Caribbean plates in Guatemala and the associated seismic activity poses a threat to ∼70% of the country's population. The aim of this study is to constrain the Late Quaternary activity of the Polochic fault by determining the active structure geometry and quantifying recent displacement rates as well as paleo-seismic events. Slip rates have been estimated from offsets of Quaternary vol...

  4. Hierarchical Neural Networks Method for Fault Diagnosis of Large-Scale Analog Circuits

    TAN Yanghong; HE Yigang; FANG Gefeng

    2007-01-01

    A novel hierarchical neural networks (HNNs) method for fault diagnosis of large-scale circuits is proposed. The presented techniques using neural networks(NNs) approaches require a large amount of computation for simulating various faulty component possibilities. For large scale circuits, the number of possible faults, and hence the simulations, grow rapidly and become tedious and sometimes even impractical. Some NNs are distributed to the torn sub-blocks according to the proposed torn principles of large scale circuits. And the NNs are trained in batches by different patterns in the light of the presented rules of various patterns when the DC, AC and transient responses of the circuit are available. The method is characterized by decreasing the over-lapped feasible domains of responses of circuits with tolerance and leads to better performance and higher correct classification. The methodology is illustrated by means of diagnosis examples.

  5. A Parallel Decision Model Based on Support Vector Machines and Its Application to Fault Diagnosis

    Yan Weiwu(阎威武); Shao Huihe

    2004-01-01

    Many industrial process systems are becoming more and more complex and are characterized by distributed features. To ensure such a system to operate under working order, distributed parameter values are often inspected from subsystems or different points in order to judge working conditions of the system and make global decisions. In this paper, a parallel decision model based on Support Vector Machine (PDMSVM) is introduced and applied to the distributed fault diagnosis in industrial process. PDMSVM is convenient for information fusion of distributed system and it performs well in fault diagnosis with distributed features. PDMSVM makes decision based on synthetic information of subsystems and takes the advantage of Support Vector Machine. Therefore decisions made by PDMSVM are highly reliable and accurate.

  6. Fault diagnosis in nuclear power plants using an artificial neural network technique

    Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis

  7. An Expert Fault Diagnosis System for Vehicle Air Conditioning Product Development

    Tan, C. F.; Tee, B. T.; Khalil, S. N.; Chen, W.; Rauterberg, G. W. M.

    2015-09-01

    The paper describes the development of the vehicle air-conditioning fault diagnosis system in automotive industries with expert system shell. The main aim of the research is to diagnose the problem of new vehicle air-conditioning system development process and select the most suitable solution to the problems. In the vehicle air-conditioning manufacturing industry, process can be very costly where an expert and experience personnel needed in certain circumstances. The expert of in the industry will retire or resign from time to time. When the expert is absent, their experience and knowledge is difficult to retrieve or lost forever. Expert system is a convenient method to replace expert. By replacing the expert with expert system, the accuracy of the processes will be increased compared to the conventional way. Therefore, the quality of product services that are produced will be finer and better. The inputs for the fault diagnosis are based on design data and experience of the engineer.

  8. Fault detection and diagnosis of the deaerator level control system in nuclear power plants

    Kim, Kyung Youn; Lee, Yoon Joon [Cheju National Univ., Cheju (Korea, Republic of)

    2004-02-01

    The deaerator of a power plant is one of feedwater heaters in the secondary system, and it is located above the feedwater pumps. The feedwater pumps take the water from the deaerator storage tank, and the Net Positive Suction Head(NPSH) should always be ensured. To secure the sufficient NPSH, the deaerator tank is equipped with the level control system of which level sensors are critical items. And it is necessary to ascertain the sensor state on-line. For this, a model-based Fault Detection and Diagnosis(FDD) is introduced in this study. The dynamic control model is formulated from the relation of input-output flow rates and liquid-level of the deaerator storage tank. Then an adaptive state estimator is designed for the fault detection and diagnosis of sensors. The performance and effectiveness of the proposed FDD scheme are evaluated by applying the operation data of Yonggwang Units 3 and 4.

  9. A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

    2007-01-01

    Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine(SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

  10. NEW TECHNOLOGY FOR FAULT DIAGNOSIS BASED ON WAVELET DENOISING AND MODIFIED EXPONENTIAL TIME-FREQUENCY DISTRIBUTION

    2001-01-01

    Fast wavelet multi-resolution analysis (wavelet MRk)provides a effective tool for analyzing and canceling disturbing components in original signal. Because of its exponential frequency axis, this method isn't suitable for extracting harmonic components. The modified exponential time-frequency distribution(MED)overcomes the problems of Wigner distribution(WD), can suppress cross-terms and cancel noise further more. MED provides high resolution in both time and frequency domains, so it can make out weak period impulse components from signal with mighty harmonic components. According to the "time" behavior, together with "frequency" behavior in one figure, the essential structure of a signal is revealed clearly. According to the analysis of algorithm and fault diagnosis example, the joint of wavelet MRA and MED is a powerful tool for fault diagnosis.

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

    Huaqing Wang

    2009-04-01

    Full Text Available 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.

  12. Simultaneous State and Parameter Estimation Based Actuator Fault Detection and Diagnosis for an Unmanned Helicopter

    Wu Chong

    2015-03-01

    Full Text Available Simultaneous state and parameter estimation based actuator fault detection and diagnosis (FDD for single-rotor unmanned helicopters (UHs is investigated in this paper. A literature review of actuator FDD for UHs is given firstly. Based on actuator healthy coefficients (AHCs, which are introduced to represent actuator faults, a combined dynamic model is established with the augmented state containing both the flight state and AHCs. Then the actuator fault detection and diagnosis problem is transformed into a general nonlinear estimation one: given control inputs and the measured flight state contaminated by measurement noises, estimate both the flight state and AHCs recursively in each time-step, which is also known as the simultaneous state and parameter estimation problem. The estimated AHCs can further be used for fault tolerant control (FTC. Based on the existing widely used nonlinear estimation methods such as the unscented Kalman filter (UKF and the extended set-membership filter (ESMF, three kinds of adaptive schemes (KF-UKF, MIT-UKF and MIT-ESMF are proposed by our team to improve the actuator FDD performance. A comprehensive comparative study on these different estimation methods is given in detail to illustrate their advantages and disadvantages when applied to unmanned helicopter actuator FDD.

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

    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.

  14. Multiple faults diagnosis for sensors in air handling unit using Fisher discriminant analysis

    This paper presents a data-driven method based on principal component analysis and Fisher discriminant analysis to detect and diagnose multiple faults including fixed bias, drifting bias, complete failure of sensors, air damper stuck and water valve stuck occurred in the air handling units. Multi-level strategies are developed to improve the diagnosis efficiency. Firstly, system-level PCA model I based on energy balance is used to detect the abnormity in view of system. Then the local-level PCA model A and B based on supply air temperature and outdoor air flow rate control loops are used to further detect the occurrence of faults and pre-diagnose them into various locations. As a linear dimensionality reduction technique, moreover, Fisher discriminant analysis is presented to diagnose the fault source after pre-diagnosis. With Fisher transformation, all of the data classes including normal and faulty operation can be re-arrayed in a transformed data space and as a result separated. Comparing the Mahalanobis distances (MDs) of all the candidates, the least one can be identified as the fault source

  15. Fault diagnosis in spur gears based on genetic algorithm and random forest

    Cerrada, Mariela; Zurita, Grover; Cabrera, Diego; Sánchez, René-Vinicio; Artés, Mariano; Li, Chuan

    2016-03-01

    There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity.

  16. Fault Diagnosis of an Advanced Wind Turbine Benchmark using Interval-based ARRs and Observers

    Sardi, Hector Eloy Sanchez; Escobet, Teressa; Puig, Vicenc;

    2015-01-01

    This paper proposes a model-based fault diagnosis (FD) approach for wind turbines and its application to a realistic wind turbine FD benchmark. The proposed FD approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown but bo...... turbine using the National Renewable Energy Laboratory FAST simulator. The obtained results are presented and compared with that of other approaches proposed in the literature....

  17. Semiadaptive Fault Diagnosis via Variational Bayesian Mixture Factor Analysis with Application to Wastewater Treatment

    Hongjun Xiao; Yiqi Liu; Daoping Huang

    2016-01-01

    Mainly due to the hostile environment in wastewater plants (WWTPs), the reliability of sensors with respect to important qualities is often poor. In this work, we present the design of a semiadaptive fault diagnosis method based on the variational Bayesian mixture factor analysis (VBMFA) to support process monitoring. The proposed method is capable of capturing strong nonlinearity and the significant dynamic feature of WWTPs that seriously limit the application of conventional multivariate st...

  18. Structural Analysis Approach to Fault Diagnosis with Application to Fixed-wing Aircraft Motion

    Izadi-Zamanabadi, Roozbeh

    2001-01-01

    The paper presents a structural analysis based method for fault diagnosis purposes. The method uses the structural model of the system and utilizes the matching idea to extract system's inherent redundant information. The structural model is represented by a bipartite directed graph. FDI Possibil...... Possibilities are examined by further analysis of the obtained information. The method is illustrated by applying on the LTI model of motion of a fixed-wing aircraft....

  19. Structural Analysis Approach to Fault Diagnosis with Application to Fixed-wing Aircraft Motion

    Izadi-Zamanabadi, Roozbeh

    2002-01-01

    The paper presents a structural analysis based method for fault diagnosis purposes. The method uses the structural model of the system and utilizes the matching idea to extract system's inherent redundant information. The structural model is represented by a bipartite directed graph. FDI Possibil...... Possibilities are examined by further analysis of the obtained information. The method is illustrated by applying on the LTI model of motion of a fixed-wing aircraft....

  20. FAULT DETECTION AND DIAGNOSIS USING A HYBRID DYNAMIC SIMULATOR: APPLICATION TO INDUSTRIAL RISK PREVENTION

    Olivier-Maget, Nelly; HETREUX, Gilles

    2014-01-01

    The main tool for the development of hazardous chemical syntheses in the field of fine chemicals and pharmaceuticals remains the batch reactor. Nevertheless, even if it offers the required flexibility and versatility, this reactor presents technological limitations. In particular, poor transfer of the heat generated by exothermic chemical reactions is a serious problem with regard to safety. In this context, a simple failure is considered as prejudicial. So, fault detection and diagnosis are ...

  1. From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis

    Dai, Xuewu; Gao, Zhiwei

    2013-01-01

    This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing system on the basis of information redundancy, in which the data and human's understanding of the data are two fundamental elements. Human's understanding may be an explicit input-output model representing the relationship among the system's variables. It may also be represented as knowledge impli...

  2. AN ALGORITHM FOR FAULT DIAGNOSIS BASED ON COMBINATORIAL DESIGN APPROACH FOR TESTING

    Nie Changhai; Xu Baowen; Shi Liang

    2003-01-01

    Much research has been done mainly in testcase generation and its effect for com-binatorial design approach for testing. This letter presents an algorithm for fault diagnosis basedon the approach. It can conclude that the factors, which cause errors, must be in a very smallrange through analyzing the test cases after testing, and retesting with some complementary testcases. The algorithm can provide a very efficient and valuable guidance for the debugging andtesting of software.

  3. Research on fault diagnosis of nuclear power plants based on genetic algorithms and fuzzy logic

    Based on genetic algorithms and fuzzy logic and using expert knowledge, mini-knowledge tree model and standard signals from simulator, a new fuzzy-genetic method is developed to fault diagnosis in nuclear power plants. A new replacement method of genetic algorithms is adopted. Fuzzy logic is used to calculate the fitness of the strings in genetic algorithms. Experiments on the simulator show it can deal with the uncertainty and the fuzzy factor

  4. WSNs-Based Mechanical Equipment State Monitoring and Fault Diagnosis in China

    Jianfeng Huang; Guohua Chen; Lei Shu; Qinghua Zhang; Xiaoling Wu

    2015-01-01

    Wireless sensor networks (WSNs) have been used in the state monitoring and fault diagnosis (SMFD) of mechanical equipment as a new signal collection and transmission technology, which have attracted a lot of attention recently. By applying the WSNs to the SMFD, many problems existing in conventional wired monitoring are solved. This paper attempts to summarize and review the recent researches and developments of the SMFD in mechanical equipment based on WSNs, providing comprehensive reference...

  5. Fault Diagnosis System of Wind Turbine Generator Based on Petri Net

    Zhang, Han

    Petri net is an important tool for discrete event dynamic systems modeling and analysis. And it has great ability to handle concurrent phenomena and non-deterministic phenomena. Currently Petri nets used in wind turbine fault diagnosis have not participated in the actual system. This article will combine the existing fuzzy Petri net algorithms; build wind turbine control system simulation based on Siemens S7-1200 PLC, while making matlab gui interface for migration of the system to different platforms.

  6. Experimental Analysis of the Performance of the Fault Diagnosis System Based on the Signed Directed Graph

    Tsuge, Yoshifumi; Miura, Takamasa; TAKEDA, Kazuhiro

    2004-01-01

    Performance of fault diagnosis system based on signed directed graph (SDG) is experimentally analyzed. The performance of the system can be evaluated in terms of reliability, accuracy and speed, which are greatly influenced by the thresholds to be used for distinguishing abnormal from normal measurements. To search an optimal adjustment of thresholds is formulated as the maximization of a performance index which is the similarity to the ideal diagnostic result. Finally, we present a guideline...

  7. Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

    Tran, Tung; Yang, Bo-Suk; Oh, Myung-Suck; Tan, Andy Chit Chiow

    2009-01-01

    This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and le...

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

    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. Diagnosis of constant faults in iteration-free circuits over monotone basis

    Alrawaf, Saad Abdullah

    2014-03-01

    We show that for each iteration-free combinatorial circuit S over a basis B containing only monotone Boolean functions with at most five variables, there exists a decision tree for diagnosis of constant faults on inputs of gates with depth at most 7L(S) where L(S) is the number of gates in S. © 2013 Elsevier B.V. All rights reserved.

  10. Diagnosis of constant faults in read-once contact networks over finite bases

    Busbait, Monther I.

    2015-03-01

    We study the depth of decision trees for diagnosis of constant 0 and 1 faults in read-once contact networks over finite bases containing only indecomposable networks. For each basis, we obtain a linear upper bound on the minimum depth of decision trees depending on the number of edges in the networks. For bases containing networks with at most 10 edges we find coefficients for linear bounds which are close to sharp. © 2014 Elsevier B.V. All rights reserved.

  11. Electro-pump Fault Diagnosis of Marine Ship by Vibration Condition Monitoring

    Payman Salami

    2010-01-01

    The objective of this research is to investigate the correlation between vibration analysis and fault diagnosis. This was achieved by vibration analysis of an electro-pump of marine ship. The vibration analysis was initially run under regular interval during electro-pump life. Some series of tests were then conducted under the operating hours of stone crasher. Vibration data was regularly collected. The overall vibration data produced by vibration analysis was compared with previous data, in ...

  12. Development of a Simulation Model for Fault Diagnosis of a Diesel Fuelled Engine

    Jamiu Muhammed Ambali; B. O. Shittu; F. A. Taofeek-Ibrahim; O. N. Saliu

    2014-01-01

    Several researchers including Antonic, [1,2,3] have worked on diesel engine in the area of fault diagnosis using various base data (vibration, voltage, temperature, and so on) measured from the diesel engine. However, little attention has been paid to data obtained from diesel engine exhaust gases. Diesel engine exhaust contains carbon-based particles and other gaseous components in different proportion according to the working condition of the engine with particular reference to the diesel ...

  13. An expert system for fault diagnosis and control of a plant for nuclear physics investigations

    D' Antone, I. (Istituto Nazionale di Fisica Nucleare, Bologna (Italy)); Fortuna, L.; Nunnari, G. (Catania Univ. (Italy))

    1991-01-01

    The maintenance of a particle detector requires the knowledge of various human experts such as physicists and engineers. The integration of different types of knowledge and experiences can be easily obtained by using an Expert System. In this paper the Implementation of an Expert System allowing us to perform on-line fault diagnosis on a complex data acquisition system, designed for MACRO (Mono poles, Astrophysics and Cosmic Ray Observatory) physics investigation, is outlined. Besides on-line diagnosis, it also takes appropriate control actions in order to improve the reliability of the apparatus. MADIES (MACROS Diagnostic Expert System) is developed by using the NEXPERT commercial shell. (author).

  14. Two-Stage Fault Diagnosis Method Based on the Extension Theory for PV Power Systems

    Meng-Hui Wang; Mu-Jia Chen

    2012-01-01

    In order to shorten the maintenance time and make sure of the photovoltaic (PV) power generation system steadily in operation, a fault diagnosis system for photovoltaic power generation system was proposed in this paper. First, a PSIM software is used to simulate a 2.2 kW PV system, it can take the operating date of the PV system under different sunlight intensity and temperature conditions. In this paper, a two-stage diagnosis system based on the extension theory for PV power systems is prop...

  15. Rotor broken-bar fault diagnosis of induction motor based on HHT of the startup electromagnetic torque

    NIU Fa-liang; HUANG Jin; YANG Jia-qiang; CHEN Li-yuan; JIN Hai

    2006-01-01

    This paper presents a new method for rotor broken-bar fault diagnosis of induction motors.The asymmetry of the rotor caused by broken-bar fault will give rise to the appearance of additional frequency component of 2sfs (s is slip and fs is supply frequency) in the electromagnetic torque spectrum.The startup electromagnetic torque signal is decomposed into several intrinsic mode function (IMF) with empirical mode decomposition (EMD)based on the Hilbert-Huang Transform.Then,using the instantaneous frequency extraction principle of the Hilbert Transform, the rotor broken-bar fault characteristic frequency of 2sfs can be exactly extracted from the IMF component,which includes the rotor fault information.Moreover,the magnitude of the IMF which includes the rotor fault information can also give the number of rotor broken bars.Experimental results demonstrate that the proposed electromagnetic torque-based fault diagnosis method is feasible.

  16. An intelligent fault diagnosis method of rolling bearings based on regularized kernel Marginal Fisher analysis

    Jiang, Li; Shi, Tielin; Xuan, Jianping

    2012-05-01

    Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.

  17. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment

    Gu, F.; Shao, Y.; Hu, N.; Naid, A.; Ball, A. D.

    2011-01-01

    This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor based on bispectrum analysis. The theoretical basis is developed to understand the nonlinear characteristics of current signals when the motor undertakes a varying load under different faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is then adopted to combine both lower sidebands and higher sidebands simultaneously and hence characterise the current signal more accurately. Based on this new bispectrum analysis a more effective diagnostic feature, namely normalised bispectral peak, is developed for fault classification. In association with the kurtosis value of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from the other fault cases and different degrees of discharge valve leakage and inter-cooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.

  18. An intelligent fault diagnosis method of rolling bearings based on regularized kernel Marginal Fisher analysis

    Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.

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

    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

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

    Jinglong Chen

    2015-10-01

    Full Text Available 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.

  1. Auditory-model-based Feature Extraction Method for Mechanical Faults Diagnosis

    LI Yungong; ZHANG Jinping; DAI Li; ZHANG Zhanyi; LIU Jie

    2010-01-01

    It is well known that the human auditory system possesses remarkable capabilities to analyze and identify signals. Therefore, it would be significant to build an auditory model based on the mechanism of human auditory systems, which may improve the effects of mechanical signal analysis and enrich the methods of mechanical faults features extraction. However the existing methods are all based on explicit senses of mathematics or physics, and have some shortages on distinguishing different faults, stability, and suppressing the disturbance noise, etc. For the purpose of improving the performances of the work of feature extraction, an auditory model, early auditory(EA) model, is introduced for the first time. This auditory model transforms time domain signal into auditory spectrum via bandpass filtering, nonlinear compressing, and lateral inhibiting by simulating the principle of the human auditory system. The EA model is developed with the Gammatone filterbank as the basilar membrane. According to the characteristics of vibration signals, a method is proposed for determining the parameter of inner hair cells model of EA model. The performance of EA model is evaluated through experiments on four rotor faults, including misalignment, rotor-to-stator rubbing, oil film whirl, and pedestal looseness. The results show that the auditory spectrum, output of EA model, can effectively distinguish different faults with satisfactory stability and has the ability to suppress the disturbance noise. Then, it is feasible to apply auditory model, as a new method, to the feature extraction for mechanical faults diagnosis with effect.

  2. EXPERIMENT BASED FAULT DIAGNOSIS ON BOTTLE FILLING PLANT WITH LVQ ARTIFICIAL NEURAL NETWORK ALGORITHM

    Mustafa DEMETGÜL

    2008-01-01

    Full Text Available In this study, an artificial neural network is developed to find an error rapidly on pneumatic system. Also the ANN prevents the system versus the failure. The error on the experimental bottle filling plant can be defined without any interference using analog values taken from pressure sensors and linear potentiometers. The sensors and potentiometers are placed on different places of the plant. Neural network diagnosis faults on plant, where no bottle, cap closing cylinder B is not working, bottle cap closing cylinder C is not working, air pressure is not sufficient, water is not filling and low air pressure faults. The fault is diagnosed by artificial neural network with LVQ. It is possible to find an failure by using normal programming or PLC. The reason offing Artificial Neural Network is to give a information where the fault is. However, ANN can be used for different systems. The aim is to find the fault by using ANN simultaneously. In this situation, the error taken place on the pneumatic system is collected by a data acquisition card. It is observed that the algorithm is very capable program for many industrial plants which have mechatronic systems.

  3. Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools

    Ding, Steven X

    2013-01-01

    Guaranteeing a high system performance over a wide operating range is an important issue surrounding the design of automatic control systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification (FDI) is receiving considerable attention. This book introduces basic model-based FDI schemes, advanced analysis and design algorithms, and mathematical and control-theoretic tools. This second edition of Model-Based Fault Diagnosis Techniques contains: ·         new material on fault isolation and identification, and fault detection in feedback control loops; ·         extended and revised treatment of systematic threshold determination for systems with both deterministic unknown inputs and stochastic noises; addition of the continuously-stirred tank heater as a representative process-industrial benchmark; and ·         enhanced discussion of residual evaluation in stochastic processes. Model-based Fault Diagno...

  4. Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis

    Silvio Simani

    2014-11-01

    Full Text Available In order to improve the availability of wind turbines, thus improving theirefficiency, it is important to detect and isolate faults in their earlier occurrence. The mainproblem of model-based fault diagnosis applied to wind turbines is represented by thesystem complexity, as well as the reliability of the available measurements. In this work, adata-driven strategy relying on fuzzy models is presented, in order to build a fault diagnosissystem. Fuzzy theory jointly with the Frisch identification scheme for errors-in-variablemodels is exploited here, since it allows one to approximate unknown models and manageuncertain data. Moreover, the use of fuzzy models, which are directly identified from thewind turbine measurements, allows the design of the fault detection and isolation module.It is worth noting that, sometimes, the nonlinearity of a wind turbine system could lead toquite complex analytic solutions. However, IF-THEN fuzzy rules provide a simpler solution,important when on-line implementations have to be considered. The wind turbine benchmarkis used to validate the achieved performances of the suggested fault detection and isolationscheme. Finally, comparisons of the proposed methodology with respect to different faultdiagnosis methods serve to highlight the features of the suggested solution.

  5. Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis

    Wang, Dong; Tsui, Kwok-Leung; Zhou, Qiang

    2016-05-01

    Rolling element bearings are commonly used in machines to provide support for rotating shafts. Bearing failures may cause unexpected machine breakdowns and increase economic cost. To prevent machine breakdowns and reduce unnecessary economic loss, bearing faults should be detected as early as possible. Because wavelet transform can be used to highlight impulses caused by localized bearing faults, wavelet transform has been widely investigated and proven to be one of the most effective and efficient methods for bearing fault diagnosis. In this paper, a new Gauss-Hermite integration based Bayesian inference method is proposed to estimate the posterior distribution of wavelet parameters. The innovations of this paper are illustrated as follows. Firstly, a non-linear state space model of wavelet parameters is constructed to describe the relationship between wavelet parameters and hypothetical measurements. Secondly, the joint posterior probability density function of wavelet parameters and hypothetical measurements is assumed to follow a joint Gaussian distribution so as to generate Gaussian perturbations for the state space model. Thirdly, Gauss-Hermite integration is introduced to analytically predict and update moments of the joint Gaussian distribution, from which optimal wavelet parameters are derived. At last, an optimal wavelet filtering is conducted to extract bearing fault features and thus identify localized bearing faults. Two instances are investigated to illustrate how the proposed method works. Two comparisons with the fast kurtogram are used to demonstrate that the proposed method can achieve better visual inspection performances than the fast kurtogram.

  6. A new method for early fault detection and diagnosis of broken rotor bars

    A new method has been developed for the detection and diagnosis of broken rotor bars faults in three-phase induction motors under no-load conditions. Early detection of faults is made by using a sliding window constructed by Hilbert transforms of one of the phases of the thee-phase currents and the size of a fault is diagnosed by motor current signature analysis (MCSA) of the stored Hilbert transforms of several periods of one-phase current. The information entropy of a symbol tree generated by each sliding window is used as a fault index. The method was tested using healthy and damaged 0.37 kW induction motors under no-load conditions with applied voltages ranging from 220 V to 380 V. One and two broken rotor bars were detected under no-load conditions when supply voltages were 260 V and above. The results indicate that the method yields a high degree of accuracy in fault identification.

  7. Evidence against Late Quaternary activity along the Northern Karakoram Fault

    Robinson, A. C.; Owen, L. A.; Hedrick, K.; Blisniuk, K.; Sharp, W. D.; Chen, J.; Schoenbohm, L. M.; Imrecke, D. B.; Yuan, Z.; Li, W.

    2012-12-01

    Although the entire 1000 km long Karakoram fault has long been interpreted to be active, recent work based primarily on interpretation of satellite imagery suggests that the northern end of the fault, where it enters the Pamir mountains, is inactive. We present field observations and geochronologic data from the southern end of the Tashkurgan valley, in the Pamir, on the Karakoram fault where it splits into two identifiable strands; an eastern strand which is the main trace of the Karakoram fault, and a western strand called the Achiehkopai fault. These results support the interpretation that the northern Karakoram fault is currently inactive, and has been for at least 200 ka: 1) Near the village of Dabudaer in the southern Tashkurgan valley the main trace of the Karakoram fault is orthogonally cut by a narrow incised valley with no observed lateral offset across the fault. Within this valley, a strath terrace ~50 m above the active drainage which overlies the main trace of the Karakoram fault which is capped by a carbonate cemented conglomerate. U-series analyses of carbonate cement from a correlative deposit located several km away yields a minimum depositional age of 76±12 ka. This age is coeval with the local Tashkurgan glacial stage we dated using Be-10 surface exposure dating (66±10 ka; Owen et al., 2012, Quaternary Science Reviews) suggesting both the conglomerate and strath terrace formed during this glacial stage. 2) ~25 km south of Dabudar, the main trace of the Karakoram projects beneath Tashkurgan glacial stage moraine and fluvial-glacial deposits which similarly show no evidence of disturbance by strike-slip deformation. Both of the above results demonstrate the main trace of the Karakoram fault has been inactive since at least ~70 ka. 3) Both the Karakoram and Achiehkopai faults are overlain by older Dabudaer glacial stage moraine deposits which are interpreted to be at least as old as the penultimate glacial, but may be >200 ka based on our Be-10

  8. FPGA based robust open transistor fault diagnosis and fault tolerant sliding mode control of a five-phase PM motor drive

    Moreno Eguilaz, Juan Manuel; Salehi Arashloo Arashloo, Ramin

    2015-01-01

    The voltage-source inverters (VSI) supplying a motor drive are prone to open transistor faults. To address this issue in faulttolerant drives applicable to electric vehicles, a new open transistor fault diagnosis (FD) method is presented in this paper. According to the proposed method, in order to define the FD index, the phase angle of the converter output current is estimated by a simple trigonometric function. The proposed FD method is adaptable, simple, capable of detecting multiple open ...

  9. Can cosmic ray exposure dating reveal the normal faulting activity of the Cordillera Blanca Fault, Peru?

    L.L. Siame

    2006-12-01

    Full Text Available The build-up of in situ-produced cosmogenic 10Be within bedrock scarps and escarpments associated to the Cordillera Blanca Normal Fault, Peru, was measured to evaluate, through Cosmic Ray Exposure dating, its normal faulting activity. The highest mountain peaks in Peru belong to the 210 km-long, NW- striking, Cordillera Blanca. Along its western border, the Cordillera Blanca Normal Fault is responsible for a vertical relief over 4.4 km, whose prominent 2 km high escarpment is characterized by ~1 km-high triangular facets corresponding to vertical displacements cumulated during the last 1-2 million years. At a more detailed scale, this fault system exhibits continuous geomorphic evidence of repeated displacements, underlined by 2 to 70 m-high scarps, corresponding to vertical displacements cumulated since Late Pleistocene and Holocene periods. Although microseismicity occurs along the Cordillera Blanca Normal Fault, no major historical or instrumental earthquake has been recorded since the beginning of the Spanish settlement in the 16th century. To evaluate the vertical slip rate along the major 90 km-long central segment of the Cordillera Blanca Normal Fault, the Quaternary fault escarpment (i.e., triangular facet, as well as the bedrock fault scarp, have been sampled for 10Be Cosmic Ray Exposure dating. Even if the uppermost part of the triangular facets have been resurfaced by the Last Glacial Maximum glaciers, our results allow to estimate a vertical slip-rate of 3±1 mm/yr, and suggest at least 2 seismic events during the last 3000 years.

  10. A morphogram with the optimal selection of parameters used in morphological analysis for enhancing the ability in bearing fault diagnosis

    Morphological analysis is a signal processing method that extracts the local morphological features of a signal by intersecting it with a structuring element (SE). When a bearing suffers from a localized fault, an impulse-type cyclic signal is generated. The amplitude and the cyclic time interval of impacts could reflect the health status of the inspected bearing and the cause of defects, respectively. In this paper, an enhanced morphological analysis called ‘morphogram’ is presented for extracting the cyclic impacts caused by a certain bearing fault. Based on the theory of morphology, the morphogram is realized by simple mathematical operators, including Minkowski addition and subtraction. The morphogram is able to detect all possible fault intervals. The most likely fault-interval-based construction index (CI) is maximized to establish the optimal range of the flat SE for the extraction of bearing fault cyclic features so that the type and cause of bearing faults can be easily determined in a time domain. The morphogram has been validated by simulated bearing fault signals, real bearing faulty signals collected from a laboratorial rotary machine and an industrial bearing fault signal. The results show that the morphogram is able to detect all possible bearing fault intervals. Based on the most likely bearing fault interval shown on the morphogram, the CI is effective in determining the optimal parameters of the flat SE for the extraction of bearing fault cyclic features for bearing fault diagnosis. (paper)

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

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

    2014-01-01

    A fault diagnosis strategy based on the wayside acoustic monitoring technique is investigated for locomotive bearing fault diagnosis. Inspired by the transient modeling analysis method based on correlation filtering analysis, a so-called Parametric-Mother-Doppler-Wavelet (PMDW) is constructed with six parameters, including a center characteristic frequency and five kinematic model parameters. A Doppler effect eliminator containing a PMDW generator, a correlation filtering analysis module, ...

  12. Application of Analytic Redundancy-based Fault Diagnosis of Sensors to Onboard Maintenance System

    CHI Chengzhi; ZHANG Weiguo; LIU Xiaoxiong

    2012-01-01

    Analytic redundancy-based fault diagnosis technique (ARFDT) is applied to onboard maintenance system (OMS).The principle of the proposed ARFDT scheme is to design a redundancy configuration using ARFDT to enhance the functions of redundancy management and built in test equipment (BITE) monitor.Redundancy configuration for dual-redundancy and analytic redundancy is proposed,in which,the fault diagnosis includes detection and isolation.In order to keep the balance between rapid diagnosis and binary hypothesis,a filter together with an elapsed time limit is designed for sequential probability ratio test (SPRT) in the process of isolation.Diagnosis results would be submitted to central maintenance computer (CMC) together with BITE information.Moreover,by adopting reconstruction,the designed method not only provides analytic redundancy to help redundancy management,but also compensates the output when both of the sensors of the same type are faulty.Our scheme is applied to an aircraft's sensors in a simulation experiment,and the results show that the proposed filter SPRT (FSPRT) saves at least 50% of isolation time than Wald SPRT (WSPRT).Also,effectiveness,practicability and rapidity of the proposed scheme can be successfully achieved in OMS.

  13. Spatial radon anomalies on active faults in California

    Radon emanation has been observed to be anomalously high along active faults in many parts of the world. We tested this relationship by conducting and repeating soil-air radon surveys with a portable radon meter across several faults in California. The results confirm the existence of fault-associated radon anomalies, which show characteristic features that may be related to fault structures but vary in time due to other environmental changes, such as rainfall. Across two creeping faults in San Juan Bautista and Hollister, the radon anomalies showed prominent double peaks straddling the fault-gouge zone during dry summers, but the peak-to-background ratios diminished after significant rain fall during winter. Across a locked segment of the San Andreas fault near Olema, the anomaly has a single peak located several meters southwest of the slip zone associated with the 1906 San Francisco earthquake. Across two fault segments that ruptured during the magnitude 7.5 Landers earthquake in 1992, anomalously high radon concentration was found in the fractures three weeks after the earthquake. We attribute the fault-related anomalies to a slow vertical gas flow in or near the fault zones. Radon generated locally in subsurface soil has a concentration profile that increases three orders of magnitude from the surface to a depth of several meters; thus an upward flow that brings up deeper and radon-richer soil air to the detection level can cause a significantly higher concentration reading. This explanation is consistent with concentrations of carbon dioxide and oxygen, measured in soil-air samples collected during one of the surveys. (Author)

  14. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    Agustín Flores

    2014-01-01

    Full Text Available This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system.

  15. Color Segmentation Approach of Infrared Thermography Camera Image for Automatic Fault Diagnosis

    Predictive maintenance based on fault diagnosis becomes very important in current days to assure the availability and reliability of a system. The main purpose of this research is to configure a computer software for automatic fault diagnosis based on image model acquired from infrared thermography camera using color segmentation approach. This technique detects hot spots in equipment of the plants. Image acquired from camera is first converted to RGB (Red, Green, Blue) image model and then converted to CMYK (Cyan, Magenta, Yellow, Key for Black) image model. Assume that the yellow color in the image represented the hot spot in the equipment, the CMYK image model is then diagnosed using color segmentation model to estimate the fault. The software is configured utilizing Borland Delphi 7.0 computer programming language. The performance is then tested for 10 input infrared thermography images. The experimental result shows that the software capable to detect the faulty automatically with performance value of 80 % from 10 sheets of image input. (author)

  16. Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

    Ricks, Brian W.; Mengshoel, Ole J.

    2009-01-01

    Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.

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

    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.

  18. Application of statistics filter method and clustering analysis in fault diagnosis of roller bearings

    Condition diagnosis of roller bearings depends largely on the feature analysis of vibration signals. Spectrum statistics filter (SSF) method could adaptively reduce the noise. This method is based on hypothesis testing in the frequency domain to eliminate the identical component between the reference signal and the primary signal. This paper presents a statistical parameter namely similarity factor to evaluate the filtering performance. The performance of the method is compared with the classical method, band pass filter (BPF). Results show that statistics filter is preferable to BPF in vibration signal processing. Moreover, the significance level would be optimized by genetic algorithms. However, it is very difficult to identify fault states only from time domain waveform or frequency spectrum when the effect of the noise is so strong or fault feature is not obvious. Pattern recognition is then applied to fault diagnosis in this study through system clustering method. This paper processes experiment rig data that after statistics filter, and the accuracy of clustering analysis increases substantially.

  19. Phenomenological models of vibration signals for condition monitoring and fault diagnosis of epicyclic gearboxes

    Lei, Yaguo; Liu, Zongyao; Lin, Jing; Lu, Fanbo

    2016-05-01

    Condition monitoring and fault diagnosis of epicyclic gearboxes using vibration signals are not as straightforward as that of fixed-axis gearboxes since epicyclic gearboxes behave quite differently from fixed-axis gearboxes in many aspects, like spectral structures. Aiming to present the spectral structures of vibration signals of epicyclic gearboxes, phenomenological models of vibration signals of epicyclic gearboxes are developed by algebraic equations and spectral structures of these models are deduced using Fourier series analysis. In the phenomenological models, all the possible vibration transfer paths from gear meshing points to a fixed transducer and the effects of angular shifts of planet gears on the spectral structures are considered. Accordingly, time-varying vibration transfer paths from sun-planet/ring-planet gear meshing points to the fixed transducer due to carrier rotation are given by window functions with different amplitudes. And an angular shift in one planet gear position is introduced in the process of modeling. After the theoretical derivations, three experiments are conducted on an epicyclic gearbox test rig and the spectral structures of collected vibration signals are analyzed. As a result, the effects of angular shifts of planet gears are verified, and the phenomenological models of vibration signals when a local fault occurs on the sun gear and the planet gear are validated, respectively. The experiment results demonstrate that the established phenomenological models in this paper are helpful to the condition monitoring and fault diagnosis of epicyclic gearboxes.

  20. High Frequency Acceleration Envelope Power Spectrum for Fault Diagnosis on Journal Bearing using DEWESOFT

    T. Narendiranath Babu

    2014-09-01

    Full Text Available The aim of study is to apply the condition monitoring technique in the journal bearing to detect the faults at an early stage and to prevent the occurrence of catastrophic failures. This study presents fault diagnosis on journal bearing through the experimental investigation at high rotational speed. Journal bearings are widely used to support the shaft of industrial machinery with heavy loads, such as compressors, turbines and centrifugal pumps. The major problem in journal bearing is catastrophic failure due to corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. High frequency acceleration enveloping facilitates the extraction of low amplitude, high frequency signals associated with repetitive impacts in journal bearings, providing a key tool for early detection in the onset of bearing damage and similar machinery health problems when coupled with standard FFT analysis. The DEWESOFT software-based methods for implementing and interpreting high frequency acceleration enveloping are presented and compared. In this study the application of STFT (Short Time Fourier Transform and Autocorrelation through FFT are used for processing vibration signal to detect faults in journal bearing is presented. A bearing testing apparatus is used for experimental studies to obtain vibration signal from a healthy bearing and fault bearing.

  1. A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains

    It is very difficult to detect weak fault signatures due to the large amount of noise in a wind turbine system. Multiscale noise tuning stochastic resonance (MSTSR) has proved to be an effective way to extract weak signals buried in strong noise. However, the MSTSR method originally based on discrete wavelet transform (DWT) has disadvantages such as shift variance and the aliasing effects in engineering application. In this paper, the dual-tree complex wavelet transform (DTCWT) is introduced into the MSTSR method, which makes it possible to further improve the system output signal-to-noise ratio and the accuracy of fault diagnosis by the merits of DTCWT (nearly shift invariant and reduced aliasing effects). Moreover, this method utilizes the relationship between the two dual-tree wavelet basis functions, instead of matching the single wavelet basis function to the signal being analyzed, which may speed up the signal processing and be employed in on-line engineering monitoring. The proposed method is applied to the analysis of bearing outer ring and shaft coupling vibration signals carrying fault information. The results confirm that the method performs better in extracting the fault features than the original DWT-based MSTSR, the wavelet transform with post spectral analysis, and EMD-based spectral analysis methods. (paper)

  2. Diagnosis and Tolerant Strategy of an Open-Switch Fault for T-type Three-Level Inverter Systems

    Choi, Uimin; Lee, Kyo Beum; Blaabjerg, Frede

    2014-01-01

    -tolerant strategy is explained by dividing into two cases: the faulty condition of half-bridge switches and the neutral-point switches. The performance of the T-type inverter system improves considerably by the proposed fault tolerant algorithm when a switch fails. The roposed method does not require additional......This paper proposes a new diagnosis method of an open-switch fault and fault-tolerant control strategy for T-type three-level inverter systems. The location of faulty switch can be identified by the average of normalized phase current and the change of the neutral-point voltage. The proposed fault...

  3. Diagnosis of Short-Circuit Fault in Large-Scale Permanent-Magnet Wind Power Generator Based on CMAC

    Chin-Tsung Hsieh

    2013-01-01

    Full Text Available This study proposes a method based on the cerebellar model arithmetic controller (CMAC for fault diagnosis of large-scale permanent-magnet wind power generators and compares the results with Error Back Propagation (EBP. The diagnosis is based on the short-circuit faults in permanent-magnet wind power generators, magnetic field change, and temperature change. Since CMAC is characterized by inductive ability, associative ability, quick response, and similar input signals exciting similar memories, it has an excellent effect as an intelligent fault diagnosis implement. The experimental results suggest that faults can be diagnosed effectively after only training CMAC 10 times. In comparison to training 151 times for EBP, CMAC is better than EBP in terms of training speed.

  4. Research on fault diagnosis method for rotating machinery vibration based on wavelet transformation and probabilistic neutral network

    Based on wavelet transformation and Neural Network Data Fusion, a Fault Diagnosis Technology is proposed. Fault feature extraction is carried out using wavelet decomposition, probabilistic neural network fault diagnosis technologies by optimizing the selection, and by the MATLAB Simulation, The simulation and results verify that using wavelet decomposition extract fault characteristics of the energy vector, which has strong generalization ability and anti-noise ability to adapt to Wide dynamic range and small sample, and building the adaptive probabilistic neural network is a good anti-noise capability, classification advantage of the high rate of diagnostic accuracy. Integration of the wavelet and neural network application will provide a better classification of diagnosis results, and better reliability and accuracy. (authors)

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

    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.

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

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

    2016-01-01

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

  7. FAULT DIAGNOSIS WITH MULTI-STATE ALARMS IN A NUCLEAR POWER CONTROL SIMULATOR

    Austin Ragsdale; Roger Lew; Brian P. Dyre; Ronald L. Boring

    2012-10-01

    This research addresses how alarm systems can increase operator performance within nuclear power plant operations. The experiment examined the effect of two types of alarm systems (two-state and three-state alarms) on alarm compliance and diagnosis for two types of faults differing in complexity. We hypothesized three-state alarms would improve performance in alarm recognition and fault diagnoses over that of two-state alarms. We used sensitivity and criterion based on Signal Detection Theory to measure performance. We further hypothesized that operator trust would be highest when using three-state alarms. The findings from this research showed participants performed better and had more trust in three-state alarms compared to two-state alarms. Furthermore, these findings have significant theoretical implications and practical applications as they apply to improving the efficiency and effectiveness of nuclear power plant operations.

  8. Fault Diagnosis of Rolling Bearing Based on Fast Nonlocal Means and Envelop Spectrum

    Yong Lv

    2015-01-01

    Full Text Available The nonlocal means (NL-Means method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures.

  9. Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter

    Li Sun

    2014-01-01

    Full Text Available Fluidized bed combustion (FBC boilers have received increasing attention in recent decades. The erosion issue on the water wall is one of the most common and serious faults for FBC boilers. Unlike direct measurement of tube thickness used by ultrasonic methods, the wastage of water wall is reconsidered equally as the variation of the overall heat transfer coefficient in the furnace. In this paper, a model-based approach is presented to estimate internal states and heat transfer coefficient dually from the noisy measurable outputs. The estimated parameter is compared with the normal value. Then the modified Bayesian algorithm is adopted for fault detection and diagnosis (FDD. The simulation results demonstrate that the approach is feasible and effective.

  10. Fault Diagnosis with Multi-State Alarms in a Nuclear Power Control Simulation

    Stuart A. Ragsdale; Roger Lew; Ronald L. Boring

    2014-09-01

    This research addresses how alarm systems can increase operator performance within nuclear power plant operations. The experiment examined the effects of two types of alarm systems (two-state and three-state alarms) on alarm compliance and diagnosis for two types of faults differing in complexity. We hypothesized the use of three-state alarms would improve performance in alarm recognition and fault diagnoses over that of two-state alarms. Sensitivity and criterion based on the Signal Detection Theory were used to measure performance. We further hypothesized that operator trust would be highest when using three-state alarms. The findings from this research showed participants performed better and had more trust in three-state alarms compared to two-state alarms. Furthermore, these findings have significant theoretical implications and practical applications as they apply to improving the efficiency and effectiveness of nuclear power plant operations.

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

    Jonguk Lee

    2016-04-01

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

  12. Wireless power transfer and fault diagnosis of high-voltage power line via robotic bird

    Liu, Chunhua; Chau, K. T.; Zhang, Zhen; Qiu, Chun; Li, Wenlong; Ching, T. W.

    2015-05-01

    This paper presents a new idea of wireless power transfer (WPT) and fault diagnosis (FD) of high-voltage power line via robotic bird. The key is to present the conceptual robotic bird with WPT coupling coil for detecting and capturing the energy from the high-voltage power line. If the power line works in normal condition, the robotic bird is able to stand on the power line and extract energy from it. If fault occurs on the power line, the corresponding magnetic field distribution will become different from that in the normal situation. By analyzing the magnetic field distribution of the power line, the WPT to the robotic bird and the FD by the robotic bird are performed and verified.

  13. High-Speed Spindle Fault Diagnosis with the Empirical Mode Decomposition and Multiscale Entropy Method

    Nan-Kai Hsieh

    2015-04-01

    Full Text Available The root mean square (RMS value of a vibration signal is an important indicator used to represent the amplitude of vibrations in evaluating the quality of high-speed spindles. However, RMS is unable to detect a number of common fault characteristics that occur prior to bearing failure. Extending the operational life and quality of spindles requires reliable fault diagnosis techniques for the analysis of vibration signals from three axes. This study used empirical mode decomposition to decompose signals into intrinsic mode functions containing a zero-crossing rate and energy to represent the characteristics of rotating elements. The MSE curve was then used to identify a number of characteristic defects. The purpose of this research was to obtain vibration signals along three axes with the aim of extending the operational life of devices included in the product line of an actual spindle manufacturing company.

  14. Multi-scale morphology analysis of acoustic emission signal and quantitative diagnosis for bearing fault

    Wang, Wen-Jing; Cui, Ling-Li; Chen, Dao-Yun

    2016-04-01

    Monitoring of potential bearing faults in operation is of critical importance to safe operation of high speed trains. One of the major challenges is how to differentiate relevant signals to operational conditions of bearings from noises emitted from the surrounding environment. In this work, we report a procedure for analyzing acoustic emission signals collected from rolling bearings for diagnosis of bearing health conditions by examining their morphological pattern spectrum (MPS) through a multi-scale morphology analysis procedure. The results show that acoustic emission signals resulted from a given type of bearing faults share rather similar MPS curves. Further examinations in terms of sample entropy and Lempel-Ziv complexity of MPS curves suggest that these two parameters can be utilized to determine damage modes.

  15. Quaternary seismo-tectonic activity of the Polochic Fault, Guatemala

    Authemayou, Christine; Brocard, Gilles; Teyssier, Christian; Suski, Barbara; Cosenza, Beatriz; MoráN-Ical, Sergio; GonzáLez-VéLiz, Claussen Walther; Aguilar-Hengstenberg, Miguel Angel; Holliger, Klaus

    2012-07-01

    The Polochic-Motagua fault system is part of the sinistral transform boundary between the North American and Caribbean plates in Guatemala and the associated seismic activity poses a threat to ˜70% of the country's population. The aim of this study is to constrain the Late Quaternary activity of the Polochic fault by determining the active structure geometry and quantifying recent displacement rates as well as paleo-seismic events. Slip rates have been estimated from offsets of Quaternary volcanic markers and alluvial fan using in situ cosmogenic 36Cl exposure dating. Holocene left-lateral slip rate and Mid-Pleistocene vertical slip rate have been estimated to 4.8 ± 2.3 mm/y and 0.3 ± 0.06 mm/y, respectively, on the central part of the Polochic fault. The horizontal slip rate is within the range of longer-term geological slip rates and short-term GPS-based estimates. In addition, the non-negligible vertical motion participates in the uplift of the block north of the fault and seems to be a manifestation of the regional, far-field stress regime. We excavated the first trench for paleo-seismological study on the Polochic fault in which we distinguish four large paleo-seismic events since 17 ky during which the Polochic fault ruptured the ground surface.

  16. Stochastic Resonance with a Joint Woods-Saxon and Gaussian Potential for Bearing Fault Diagnosis

    Haibin Zhang

    2014-01-01

    Full Text Available This work aims for a new stochastic resonance (SR model which performs well in bearing fault diagnosis. Different from the traditional bistable SR system, we realize the SR based on the joint of Woods-Saxon potential (WSP and Gaussian potential (GP instead of a reflection-symmetric quartic potential. With this potential model, all the parameters in the Woods-Saxon and Gaussian SR (WSGSR system are not coupled when compared to the traditional one, so the output signal-to-noise ratio (SNR can be optimized much more easily by tuning the system parameters. Besides, a smoother potential bottom and steeper potential wall lead to a stable particle motion within each potential well and avoid the unexpected noise. Different from the SR with only WSP which is a monostable system, we improve it into a bistable one as a general form offering a higher SNR and a wider bandwidth. Finally, the proposed model is verified to be outstanding in weak signal detection for bearing fault diagnosis and the strategy offers us a more effective and feasible diagnosis conclusion.

  17. Research on Remote Monitoring and Fault Diagnosis Technology of Numerical Control Machine

    ZHANG Jianyu; GAO Lixin; CUI Lingli; LI Xianghui; WANG Yingwang

    2006-01-01

    Based on the internet technology, it has become possible to complete remote monitoring and fault diagnosis for the numerical control machine. In order to capture the micro-shock signal induced by the incipient fault on the rotating parts, the resonance demodulation technology is utilized in the system. As a subsystem of the remote monitoring system, the embedded data acquisition instrument not only integrates the demodulation board but also complete the collection and preprocess of monitoring data from different machines. Furthermore, through connecting to the internet, the data can be transferred to the remote diagnosis center and data reading and writing function can be finished in the database. At the same time, the problem of the IP address floating in the dial-up of web server is solved by the dynamic DNS technology. Finally, the remote diagnosis software developed on the LabVIEW platform can analyze the monitoring data from manufacturing field. The research results have indicated that the equipment status can be monitored by the system effectively.

  18. Hierarchical Fault Diagnosis for a Hybrid System Based on a Multidomain Model

    Jiming Ma

    2015-01-01

    Full Text Available The diagnosis procedure is performed by integrating three steps: multidomain modeling, event identification, and failure event classification. Multidomain model can describe the normal and fault behaviors of hybrid systems efficiently and can meet the diagnosis requirements of hybrid systems. Then the multidomain model is used to simulate and obtain responses under different failure events; the responses are further utilized as a priori information when training the event identification library. Finally, a brushless DC motor is selected as the study case. The experimental result indicates that the proposed method could identify the known and unknown failure events of the studied system. In particular, for a system with less response information under a failure event, the accuracy of diagnosis seems to be higher. The presented method integrates the advantages of current quantitative and qualitative diagnostic procedures and can distinguish between failures caused by parametric and abrupt structure faults. Another advantage of our method is that it can remember unknown failure types and automatically extend the adaptive resonance theory neural network library, which is extremely useful for complex hybrid systems.

  19. Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

    Songrong Luo

    2015-01-01

    Full Text Available Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs by using local characteristic-scale decomposition (LCD method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM classifier based on Chemical Reaction Optimization (CRO algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.

  20. Diagnosis of faults in EDF power plants: from monitoring to diagnosis

    Electricite de France is constantly is search of means to improve safety and availability in its nuclear power plants. To this end, EDF has designed new monitoring systems for the major components of its units: for turbogenerator and inlet valves monitoring, for reactor coolant pumps monitoring, for internal structures monitoring and for loose parts detection. New techniques for signal acquisition and processing for diagnosis are used and all these monitoring systems are designed with the same general concept on monitoring. Simultaneously, a workstation for monitoring and aid in diagnosis (PSAD) is under development. It will integrate every monitoring system and will constitute an indispensable tool for plant personnel, enabling them to diagnose the condition of plant equipment, and providing them with high efficiency and user-friendly tools. The PSAD will have a flexible architecture, guaranteeing optimum distribution of computing power to make it available where it is needed. (author). 5 figs., 4 refs

  1. Development of Fault Detection and Diagnosis Schemes for Industrial Refrigeration Systems

    Thybo, C.; Izadi-Zamanabadi, Roozbeh

    2004-01-01

    The success of a fault detection and diagnosis (FDD) scheme depends not alone on developing an advanced detection scheme. To enable successful deployment in industrial applications, an economically optimal development of FDD schemes are required. This paper reviews and discusses the gained...... experiences achieved by employing a combination of various techniques, methods, and algorithms, which are proposed by academia, on an industrial application. The main focus is on sharing the "lessons learned" from developing and employing Faulttolerant functionalities to a controlled process in order to meet...... the industrial needs while satisfying economically motivated constraints....

  2. Study on unknown input reduced-order observer for fault diagnosis

    2007-01-01

    A novel unknown input reduced-order observer (UIRO) design scheme is presented. It is proved that unknown input appearing in measurement can be eliminated by a simple algebraic transformation. Then, a new UIRO design scheme is proposed via a transformation under no unknown input existing in measurement.Compared with other known results, the condition is weaker than others. So it was further reasonable. The design procedure proposed is simple and straightforward enough to be applied. An example is given to show its efficiency in fault diagnosis.

  3. Fuelex: a knowledge based system for research reactor fueling operations and fault diagnosis

    An off-line knowledge based system FUELEX being developed for a 100 MW (thermal) research reactor fueling operations is described. The operational procedure execution of fueling machine, fault diagnosis and trouble shooting have been implemented as three generic tasks in the system. The method of inference used in the system is rule based deduction with priority factor. After identification of lapses or abnormal functioning, the system identifies the procedure to mitigate the consequences of unusual occurrences and assists the operator by confirming the success of the action taken. (author). 7 refs., 8 figs

  4. Study on fault diagnosis technology for nuclear power plants based on time series data mining

    Time series data mining is applied to the fault diagnosis for nuclear power plants. dow method is used to convert the problem to a standard supervised learning problem. Simulation experiment is carried out by LOCA. The simulation results show that the diagnostic accuracy has certain improvement when the sliding-window method is applied. Furthermore, extracting the feature of the time series data in the sliding-window, the diagnostic accuracy is improved greatly. Some problems which can not be solved by classical algorithm can be diagnosed by time series data mining method. (authors)

  5. The Parameters Selection of PSO Algorithm influencing On performance of Fault Diagnosis

    He Yan

    2016-01-01

    Full Text Available The particle swarm optimization (PSO is an optimization algorithm based on intelligent optimization. Parameters selection of PSO will play an important role in performance and efficiency of the algorithm. In this paper, the performance of PSO is analyzed when the control parameters vary, including particle number, accelerate constant, inertia weight and maximum limited velocity. And then PSO with dynamic parameters has been applied on the neural network training for gearbox fault diagnosis, the results with different parameters of PSO are compared and analyzed. At last some suggestions for parameters selection are proposed to improve the performance of PSO.

  6. Analysis and Study of Modern Fault Diagnosis Methods of Mechanical Equipments

    HOU Rong-tao

    2008-01-01

    Fast Fourier Transform(FFT)fiequeney spectrum analysis,signal decomposing and reconstruction by wavelet analysis,fractal theory and chaos theory are hot research topics for fault diagnosis and prediction of complex machinery so far.In this paper,the characteristics of the FFT method.wavelet method,fractal method,and largest Lyapunov exponent method are studied and analyzed in detail.The advantages and shortcomings of these methods are pointed out respectively.Some unsolved problems are presented here.

  7. Fault Diagnosis Based on Fuzzy Observers for Wind Energy Conversion Systems

    Kamal, Elkhatib; Aitouche, Abdel; Bayart, Mireille

    2011-01-01

    This paper addresses the design of fault diagnosis for Wind Energy Conversion System (WECS). In this approach, the residual signal is generated by a fuzzy observer which is based on Takagi-Sugeno (TS) fuzzy models based on Lyapunov function. The approach presented takes into account the stability and design of non-linear fuzzy inference systems based also on TS fuzzy models. The paper derives the necessary conditions for the assignability of eigenvalues to a region in the s-plane and the nece...

  8. STUDY ON NATURAL LANGUAGE INTERFACE OF NETWORK FAULT DIAGNOSIS EXPERT SYSTEM

    2006-01-01

    Since Internet was born,the computer net workhas developed at a highspeedinits structure and ap-plication.Facing the huge and complex computernet work,we think it hard to deal with the variousfaults of the net work with manual method.Alongwiththe development of AI,the latest achievementof expert system(ES)has provided a new way forthe fault diagnosis and analysis of net work.Butthese ES don't provide a good interface for user.The interface of ES al ways uses command,menuand windows at present.It li mits the...

  9. The Lake Edgar Fault: an active fault in Southwestern Tasmania, Australia, with repeated displacement in the Quaternary

    Jensen, V; Gibson, G; R. Van Dissen; McCue, K.; Boreham, B.

    2003-01-01

    The Lake Edgar Fault in Western Tasmania, Australia is marked by a prominent fault scarp and is a recently reactivated fault initially of Cambrian age. The scarp has a northerly trend and passes through the western abutment of the Edgar Dam, a saddle dam on Lake Pedder. The active fault segment displaces geologically young river and glacial deposits. It is 29 ± 4 km long, and dips to the west. Movement on the fault has ruptured the ground surface at least twice within the ...

  10. Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace%Multi-Class Classification Methods of Cost-Conscious LS-SVM for Fault Diagnosis of Blast Furnace

    LIU Li-mei; WANG An-na; SHA Mo; ZHAO Feng-yun

    2011-01-01

    Aiming at the limitations of rapid fault diagnosis of blast furnace, a novel strategy based on cost-conscious least squares support vector machine (LS-SVM) is proposed to solve this problem. Firstly, modified discrete particle swarm optimization is applied to optimize the feature selection and the LS-SVM parameters. Secondly, cost-con- scious formula is presented for fitness function and it contains in detail training time, recognition accuracy and the feature selection. The CLS-SVM algorithm is presented to increase the performance of the LS-SVM classifier. The new method can select the best fault features in much shorter time and have fewer support vectbrs and better general- ization performance in the application of fault diagnosis of the blast furnace. Thirdly, a gradual change binary tree is established for blast furnace faults diagnosis. It is a multi-class classification method based on center-of-gravity formula distance of cluster. A gradual change classification percentage ia used to select sample randomly. The proposed new metbod raises the sped of diagnosis, optimizes the classifieation scraraey and has good generalization ability for fault diagnosis of the application of blast furnace.

  11. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

    Jia, Feng; Lei, Yaguo; Lin, Jing; Zhou, Xin; Lu, Na

    2016-05-01

    Aiming to promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rotating machinery. Among these studies, the methods based on artificial neural networks (ANNs) are commonly used, which employ signal processing techniques for extracting features and further input the features to ANNs for classifying faults. Though these methods did work in intelligent fault diagnosis of rotating machinery, they still have two deficiencies. (1) The features are manually extracted depending on much prior knowledge about signal processing techniques and diagnostic expertise. In addition, these manual features are extracted according to a specific diagnosis issue and probably unsuitable for other issues. (2) The ANNs adopted in these methods have shallow architectures, which limits the capacity of ANNs to learn the complex non-linear relationships in fault diagnosis issues. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome the aforementioned deficiencies. Through deep learning, deep neural networks (DNNs) with deep architectures, instead of shallow ones, could be established to mine the useful information from raw data and approximate complex non-linear functions. Based on DNNs, a novel intelligent method is proposed in this paper to overcome the deficiencies of the aforementioned intelligent diagnosis methods. The effectiveness of the proposed method is validated using datasets from rolling element bearings and planetary gearboxes. These datasets contain massive measured signals involving different health conditions under various operating conditions. The diagnosis results show that the proposed method is able to not only adaptively mine available fault characteristics from the measured signals, but also obtain superior diagnosis accuracy compared with the existing methods.

  12. Fault Estimation

    Stoustrup, Jakob; Niemann, H.

    2002-01-01

    This paper presents a range of optimization based approaches to fault diagnosis. A variety of fault diagnosis prob-lems are reformulated in the so-called standard problem setup introduced in the literature on robust control. Once the standard problem formulations are given, the fault diagnosis pr...... problems can be solved by standard optimization tech-niques. The proposed methods include: (1) fault diagnosis (fault estimation, (FE)) for systems with model uncertainties; (2) FE for systems with parametric faults, and (3) FE for a class of nonlinear systems....

  13. Active tectonics west of New Zealand's Alpine Fault: South Westland Fault Zone activity shows Australian Plate instability

    De Pascale, Gregory P.; Chandler-Yates, Nicholas; Dela Pena, Federico; Wilson, Pam; May, Elijah; Twiss, Amber; Cheng, Che

    2016-04-01

    The 300 km long South Westland Fault Zone (SWFZ) is within the footwall of the Central Alpine Fault (<20 km away) and has 3500 m of dip-slip displacement, but it has been unknown if the fault is active. Here the first evidence for SWFZ thrust faulting in the "stable" Australian Plate is shown with cumulative dip-slip displacements up to 5.9 m (with 3 m throw) on Pleistocene and Holocene sediments and gentle hanging wall anticlinal folding. Cone penetration test (CPT) stratigraphy shows repeated sequences within the fault scarp (consistent with thrusting). Optically stimulated luminescence (OSL) dating constrains the most recent rupture post-12.1 ± 1.7 ka with evidence for three to four events during earthquakes of at least Mw 6.8. This study shows significant deformation is accommodated on poorly characterized Australian Plate structures northwest of the Alpine Fault and demonstrates that major active and seismogenic structures remain uncharacterized in densely forested regions on Earth.

  14. Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

    Yi-Hung Liao; Jung-Chuan Chou; Chin-Yi Lin

    2013-01-01

    Fault diagnosis (FD) and data fusion (DF) technologies implemented in the LabVIEW program were used for a ruthenium dioxide pH sensor array. The purpose of the fault diagnosis and data fusion technologies is to increase the reliability of measured data. Data fusion is a very useful statistical method used for sensor arrays in many fields. Fault diagnosis is used to avoid sensor faults and to measure errors in the electrochemical measurement system, therefore, in this study, we use fault diagn...

  15. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    Wang, Weiying; Xu, Zhiqiang; Tang, Rui; Li, Shuying; Wu, Wei

    2014-01-01

    Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms. PMID:25258726

  16. Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

    Weiying Wang

    2014-01-01

    Full Text Available Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms.

  17. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

    Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

    2016-03-01

    As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Numerous publications about the study and applications of WT for RMFD have been presented to academic journals, technical reports and conference proceedings. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation and field experiments. Then the development process of WT based on inner product is concluded and the applications of major developments in RMFD are also summarized. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed. It is expected that this paper can offer an in-depth and comprehensive references for researchers and help them with finding out further research topics.

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

    Xu, Jiwei; Wu, Huijuan; Xiao, Shunkun

    2014-12-01

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

  19. Signal de-noising methods for fault diagnosis and troubleshooting at CANDU® stations

    Highlights: • Fault modelling using a Fault Semantic Network (FSN). • Intelligent filtering techniques for signal de-noise in NPP. • Signal feature extraction is applied as integrated with FSN. • Increase signal-to-noise ratio (SNR). - Abstract: Over the past several years a number of domestic CANDU® stations have experienced issues with neutron detection systems that challenged safety and operation. Intelligent troubleshooting methodology is required to aid in making risk-informed decisions related to design and operational activities, which can aid current stations and be used for the future generation of CANDU® designs. Fault modelling approach using Fault Semantic Network (FSN) with risk estimation is proposed for this purpose. One major challenge in troubleshooting is the determination of accurate data. It is typical to have missing, incomplete or corrupted data points in large process data sets from dynamically changing systems. Therefore, it is expected that quality of obtained data will have a direct impact on the system's ability to recognize developing trends in the process upset situations. In order to enable fault detection process, intelligent filtering techniques are required to de-noise process data and extract valuable signal features in the presence of background noise. In this study, the impact of applying an optimized and intelligent filtering of process signals prior to data analysis is discussed. This is particularly important for neutronic signals in order to increase signal-to-noise ratio (SNR) which suffers the most during start-ups and low power operation. This work is complimentary to the previously published studies on FSN-based fault modelling in CANDU stations. The main objective of this work is to explore the potential research methods using a specific case study and, based on the results and outcomes from this work, to note the possible future improvements and innovation areas

  20. DEVELOPMENT AND TESTING OF FAULT-DIAGNOSIS ALGORITHMS FOR REACTOR PLANT SYSTEMS

    Grelle, Austin L.; Park, Young S.; Vilim, Richard B.

    2016-06-26

    Argonne National Laboratory is further developing fault diagnosis algorithms for use by the operator of a nuclear plant to aid in improved monitoring of overall plant condition and performance. The objective is better management of plant upsets through more timely, informed decisions on control actions with the ultimate goal of improved plant safety, production, and cost management. Integration of these algorithms with visual aids for operators is taking place through a collaboration under the concept of an operator advisory system. This is a software entity whose purpose is to manage and distill the enormous amount of information an operator must process to understand the plant state, particularly in off-normal situations, and how the state trajectory will unfold in time. The fault diagnosis algorithms were exhaustively tested using computer simulations of twenty different faults introduced into the chemical and volume control system (CVCS) of a pressurized water reactor (PWR). The algorithms are unique in that each new application to a facility requires providing only the piping and instrumentation diagram (PID) and no other plant-specific information; a subject-matter expert is not needed to install and maintain each instance of an application. The testing approach followed accepted procedures for verifying and validating software. It was shown that the code satisfies its functional requirement which is to accept sensor information, identify process variable trends based on this sensor information, and then to return an accurate diagnosis based on chains of rules related to these trends. The validation and verification exercise made use of GPASS, a one-dimensional systems code, for simulating CVCS operation. Plant components were failed and the code generated the resulting plant response. Parametric studies with respect to the severity of the fault, the richness of the plant sensor set, and the accuracy of sensors were performed as part of the validation

  1. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  2. Time-frequency manifold for nonlinear feature extraction in machinery fault diagnosis

    He, Qingbo

    2013-02-01

    Time-frequency feature is beneficial to representation of non-stationary signals for effective machinery fault diagnosis. The time-frequency distribution (TFD) is a major tool to reveal the synthetic time-frequency pattern. However, the TFD will also face noise corruption and dimensionality reduction issues in engineering applications. This paper proposes a novel nonlinear time-frequency feature based on a time-frequency manifold (TFM) technique. The new TFM feature is generated by mainly addressing manifold learning on the TFDs in a reconstructed phase space. It combines the non-stationary information and the nonlinear information of analyzed signals, and hence exhibits valuable properties. Specifically, the new feature is a quantitative low-dimensional representation, and reveals the intrinsic time-frequency pattern related to machinery health, which can effectively overcome the effects of noise and condition variance issues in sampling signals. The effectiveness and the merits of the proposed TFM feature are confirmed by case study on gear wear diagnosis, bearing defect identification and defect severity evaluation. Results show the value and potential of the new feature in machinery fault pattern representation and classification.

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

    Reifman, Jaques; Wei, Thomas Y. C.

    1995-01-01

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

  4. A Class of Nonlinear Unknown Input Observer for Fault Diagnosis: Application to Fault Tolerant Control of an Autonomous Spacecraft

    Fonod, Robert; Henry, David; Charbonnel, Catherine; Bornschlegl, Eric

    2014-01-01

    In this paper, the problem of Nonlinear Unknown Input Observer (NUIO) based Fault Detection and Isolation (FDI) scheme design for a class of nonlinear Lipschitz systems is studied. The proposed FDI method is applied to detect, isolate and accommodate thruster faults of an autonomous spacecraft involved in the rendezvous phase of the Mars Sample Return (MSR) mission. Considered fault scenarios represent fully closed thruster and thruster efficiency loss. The FDI scheme consists of a bank of NU...

  5. GENERATOR VIBRATION FAULT DIAGNOSIS METHOD BASED ON ROTOR VIBRATION AND STATOR WINDING PARALLEL BRANCHES CIRCULATING CURRENT CHARACTERISTICS

    Wan Shuting; Li Heming; Li Yonggang; Tang Guiji

    2005-01-01

    Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or imbalance fault, and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs. Next, the characteristics of the stator winding parallel branches circulating current are analyzed, which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault. Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults, a method of generator vibration fault diagnosis, based on rotor vibration and circulating current characteristics, is developed. Finally, the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.

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

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

    2016-04-01

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

  7. Numerical simulation of fractal interface effect of mining-caused activation of fault

    Xie Heping; Zhao Jianfeng; Yu Guangming

    2002-01-01

    Mining-caused activation of fault is an important research subject in mining science. In the past, the influences of geometrical morphology of fault surface on the activation have not been revealed. In view of the fractal character of fault surface, the self-affine fractal curves and geological-mining models with these kinds of fractal fault surface are constructed in order to numerically simulate the mining-caused activation phenomenon of fractal fault surface, and the law of influence of fr...

  8. Fault diagnosis and fault-tolerant finite control set-model predictive control of a multiphase voltage-source inverter supplying BLDC motor.

    Salehifar, Mehdi; Moreno-Equilaz, Manuel

    2016-01-01

    Due to its fault tolerance, a multiphase brushless direct current (BLDC) motor can meet high reliability demand for application in electric vehicles. The voltage-source inverter (VSI) supplying the motor is subjected to open circuit faults. Therefore, it is necessary to design a fault-tolerant (FT) control algorithm with an embedded fault diagnosis (FD) block. In this paper, finite control set-model predictive control (FCS-MPC) is developed to implement the fault-tolerant control algorithm of a five-phase BLDC motor. The developed control method is fast, simple, and flexible. A FD method based on available information from the control block is proposed; this method is simple, robust to common transients in motor and able to localize multiple open circuit faults. The proposed FD and FT control algorithm are embedded in a five-phase BLDC motor drive. In order to validate the theory presented, simulation and experimental results are conducted on a five-phase two-level VSI supplying a five-phase BLDC motor. PMID:26549566

  9. A Novel Association Rule Mining with IEC Ratio Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers

    Ms. Kanika Shrivastava

    2012-06-01

    Full Text Available Dissolved gas Analysis (DGA is the most importantcomponent of finding fault in large oil filledtransformers. Early detection of incipient faults intransformers reduces costly unplanned outages. Themost sensitive and reliable technique for evaluatingthe core of transformer is dissolved gas analysis. Inthis paper we evaluate different transformercondition on different cases. This paper usesdissolved gas analysis to study the history ofdifferent transformers in service, from whichdissolved combustible gases (DCG in oil are usedas a diagnostic tool for evaluating the condition ofthe transformer. Oil quality and dissolved gassestests are comparatively used for this purpose. In thispaper we present a novel approach which is basedon association rule mining and IEC ratio method.By using data mining concept we can categorizefaults based on single and multiple associations andalso map the percentage of fault. This is an efficientapproach for fault diagnosis of power transformerswhere we can find the fault in all obviousconditions. We use java for programming andcomparative study.

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

    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.

  11. Fault Prognosis and Diagnosis of an Automotive Rear Axle Gear Using a RBF-BP Neural Network

    The rear axle gear is one of the key parts of transmission system for automobiles. Its healthy state directly influences the security and reliability of the automotives. However, non-stationary and nonlinear characteristics of gear vibration due to load and speed fluctuations, makes it difficult to detect and diagnosis the faults from the transmission gear. To solve this problem a fault prognosis and diagnosis method based on a combination of radial basis function(RBF) and back-propagation (BP) neural networks is proposed in this paper. Firstly, a moving average pretreatment is used to suppress the time series fluctuation of vibration characteristic parameter tie series and reduce the interference of random noise. Then, the RBF network is applied to the pretreated parameter sequences for fault prognosis. Furthermore, based on self-learning ability of neural networks, characteristic parameters for different common faults are learned by a BP network. Then the trained BP neural network is utilized for fault diagnosis of the rear axle gear. The results show that the proposed method has a good performance in prognosing and diagnosing different faults from the rear axle gear.

  12. Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix

    Time-frequency analysis is an effective tool to extract machinery health information contained in non-stationary vibration signals. Various time-frequency analysis methods have been proposed and successfully applied to machinery fault diagnosis. However, little research has been done on bearing fault diagnosis using texture features extracted from time-frequency representations (TFRs), although they may contain plenty of sensitive information highly related to fault pattern. Therefore, to make full use of the textural information contained in the TFRs, this paper proposes a novel fault diagnosis method based on S transform, gray level co-occurrence matrix (GLCM) and multi-class support vector machine (Multi-SVM). Firstly, S transform is chosen to generate the TFRs due to its advantages of providing frequency-dependent resolution while keeping a direct relationship with the Fourier spectrum. Secondly, the famous GLCM-based texture features are extracted for capturing fault pattern information. Finally, as a classifier which has good discrimination and generalization abilities, Multi-SVM is used for the classification. Experimental results indicate that the GLCM-based texture features extracted from TFRs can identify bearing fault patterns accurately, and provide higher accuracies than the traditional time-domain and frequency-domain features, wavelet packet node energy or two-direction 2D linear discriminant analysis based features of the same TFRs in most cases. (paper)

  13. Fault diagnosis of rolling element bearing based on S transform and gray level co-occurrence matrix

    Zhao, Minghang; Tang, Baoping; Tan, Qian

    2015-08-01

    Time-frequency analysis is an effective tool to extract machinery health information contained in non-stationary vibration signals. Various time-frequency analysis methods have been proposed and successfully applied to machinery fault diagnosis. However, little research has been done on bearing fault diagnosis using texture features extracted from time-frequency representations (TFRs), although they may contain plenty of sensitive information highly related to fault pattern. Therefore, to make full use of the textural information contained in the TFRs, this paper proposes a novel fault diagnosis method based on S transform, gray level co-occurrence matrix (GLCM) and multi-class support vector machine (Multi-SVM). Firstly, S transform is chosen to generate the TFRs due to its advantages of providing frequency-dependent resolution while keeping a direct relationship with the Fourier spectrum. Secondly, the famous GLCM-based texture features are extracted for capturing fault pattern information. Finally, as a classifier which has good discrimination and generalization abilities, Multi-SVM is used for the classification. Experimental results indicate that the GLCM-based texture features extracted from TFRs can identify bearing fault patterns accurately, and provide higher accuracies than the traditional time-domain and frequency-domain features, wavelet packet node energy or two-direction 2D linear discriminant analysis based features of the same TFRs in most cases.

  14. High Frequency Monitoring of the Aigion Fault Activity

    Cornet, Francois; Bourouis, Seid

    2013-04-01

    In 2007, a high frequency monitoring system was deployed in the 1000 m deep AIG10 well that intersects the Aigion fault at a depth of 760 m. This active 15 km long fault is located on the south shore of the Corinth rift, some 40 km east from Patras, in western central Greece. The borehole intersects quaternary sediments down to 495 m, then cretaceous and tertiary heavily tectonized deposits from the Pindos nappe. Below the fault encountered at 760 m, the borehole remains within karstic limestone of the Gavrovo Tripolitza nappe. The monitoring system involved two geophones located some 15 m above the fault, and two hydrophones located respectively at depths equal to 500 m and 250 m. The frequency domain for the data acquisition system ranged from a few Hz to 2500 Hz. The seismic velocity structure close to the borehole was determined through both sonic logs and vertical seismic profiles. This monitoring system has been active during slightly over six months and has recorded signals from microseismic events that occurred in the rift, the location of which was determined thanks to the local 11 stations, three components, short period (2 Hz), monitoring system. In addition, the borehole monitoring system has recorded more than 1000 events not identified with the regional network. Events were precisely correlated with pressure variations associated with two human interventions. These extremely low magnitude events occurred at distances that reached at least up to 1500 m from the well. They were associated, some ten days later, with some local rift activity. A tentative model is proposed that associates local short slip instabilities in the upper part of the fault close to the well, with a longer duration pore pressure diffusion process. Results demonstrate that the Aigion fault is continuously creeping down to a depth at least equal to 5 km but probably deeper.

  15. Fault Diagnosis for a Diesel Valve Train Based on Time-Frequency Analysis and Probabilistic Neural Networks

    WANG Cheng-dong; WEI Rui-xuan; ZHANG You-yun; XIA Yong

    2004-01-01

    The cone-shaped kernel distributions of vibration acceleration signals, which were acquired from the cylinder head in eight different states of a valve train, were calculated and displayed in grey images. Probabilistic Neural Networks (PNN) was used to classify the images directly after the images were normalized. By this way, the problem of fault diagnosis for a valve train was transferred to the classification of time-frequeacy images. As there is no need to extract features from time-frequency images before classification, the fault diagnosis process is highly simplified. The experimental results show that the vibration signals can be classified accurately by the proposed methods.

  16. FAULT DIAGNOSIS BASED ON INTE- GRATION OF CLUSTER ANALYSIS,ROUGH SET METHOD AND FUZZY NEURAL NETWORK

    Feng Zhipeng; Song Xigeng; Chu Fulei

    2004-01-01

    In order to increase the efficiency and decrease the cost of machinery diagnosis, a hybrid system of computational intelligence methods is presented. Firstly, the continuous attributes in diagnosis decision system are discretized with the self-organizing map (SOM) neural network. Then, dynamic reducts are computed based on rough set method, and the key conditions for diagnosis are found according to the maximum cluster ratio. Lastly, according to the optimal reduct, the adaptive neuro-fuzzy inference system (ANFIS) is designed for fault identification. The diagnosis of a diesel verifies the feasibility of engineering applications.

  17. Case Study on Fault Diagnosis of the Actual Operating Transformer by FRA

    Sano, Takahiro; Ogawa, Yoshiharu; Shimonosono, Takaaki; Wada, Tadayuki

    A high voltage, large capacity power transformer is one of the most important equipment in electric power system. If a failure occurs in such a transformer, stable power supply may become impossible. In addition, efficient power system operation may become difficult because it takes long time to replace the transformer. To prevent failures that may occur, effective external diagnosis must be performed and the defective portions must be correctly identified. However, such anomalous phenomena are complicated in many cases, and their causes cannot be identified in some cases by using conventional techniques. This paper reports a case study on the fault diagnosis of an oil-immersed power transformer that had a tendency to increase in the total combustible gas (TCG) during a regular operation. Specifying the faulty parts became possible by applying various case of Frequency Response Analysis (FRA) diagnosis though it was impossible by the electrical tests, DGA (Dissolved Gas Analysis), and so on. This transformer was disassembled to investigate the condition and was replaced without causing failure.

  18. Research on Fault Diagnosis System of a Diesel Engine Based on Wavelet Analysis and LabVIEW Software

    Eidam Ahmed Hebiel

    2014-05-01

    Full Text Available Experiment presented in this study, used vibration data obtained from a four-stroke, 295 diesel engine. Fault of the internal-combustion engine was detected by using the vibration signals of the cylinder head. The fault diagnosis system was designed and constructed for inspecting the status and fault diagnosis of a diesel engine based on discrete wavelet analysis and LabVIEW software. The cylinder-head vibration signals were captured through a piezoelectric acceleration sensor, that was attached to a surface of the cylinder head of the engine, while the engine was running at two speeds (620 and 1300 rpm and two loads (15 and 45 N•m. Data was gathered from five different conditions, associated with the cylinder head such as single cylinder shortage, double cylinders shortage, intake manifold obstruction, exhaust manifold obstruction and normal condition. After decomposing the vibration signals into some of the details and approximations coefficients with db5 mother wavelet and decomposition level 5, the energies were extracted from each frequency sub-band of healthy and unhealthy conditions as a feature of engine fault diagnosis. By doing so, normal and abnormal conditions behavior could be effectively distinguished by comparing the energy accumulations of each sub-band. The results showed that detection of fault by discrete wavelet analysis is practicable. Finally, two techniques, Back-Propagation Neural Network (BPNN and Support Victor Machine (SVM were applied to the signal that was collected from the diesel engine head. The experimental results showed that BPNN was more effective in fault diagnosis of the internal-combustion engine, with various fault conditions, than SVM.

  19. Exhumation history of an active fault to constrain a fault-based seismic hazard scenario: the Pizzalto fault (central Apennines, Italy) example.

    Tesson, Jim; Pace, Bruno; Benedetti, Lucilla; Visini, Francesco; Delli Rocioli, Mattia; Didier, Bourles; Karim, keddadouche; Gorges, Aumaitre

    2016-04-01

    A prerequisite to constrain fault-based and time-dependent earthquake rupture forecast models is to acquire data on the past large earthquake frequency on an individual seismogenic source and to compare all the recorded occurrences in the active fault-system. We investigated the Holocene seismic history of the Pizzalto normal fault, a 13 km long fault segment belonging to the Pizzalto-Rotella-Aremogna fault system in the Apennines (Italy). We collected 44 samples on the Holocene exhumed Pizzalto fault plane and analyzed their 36Cl and rare earth elements content. Conjointly used, the 36Cl and REE concentrations show that at least 6 events have exhumed 4.4 m of the fault scarp between 3 and 1 ka BP, the slip per event ranging from 0.3 to 1.2 m. No major events have been detected over the last 1 ka. The Rotella-Aremogna-Pizzalto fault system has a clustered earthquake behaviour with a mean recurrence time of 1.2 ka and a low to moderate probability (ranging from 4% to 26%) of earthquake occurrence over the next 50 years. We observed similarities between seismic histories of several faults belonging to two adjacent fault systems. This could again attest that non-random processes occurring in the release of the strain accumulated on faults, commonly referred to as fault interactions and leading to apparent synchronization. If these processes were determined as being the main parameter controlling the occurrence of earthquakes, it would be crucial to take them into account in seismic hazard models.

  20. APU Fault Diagnosis Research Based on Fault Tree Analysis Method%基于故障树分析法的APU故障诊断研究

    聂继锋

    2012-01-01

    Fault tree analysis (FTA) is a widely -used method for failure analysis and fault diagnosis. Combining with APU' s working characters, the author analyzes the causes of starting difficukies, builds APU starting difficulty fault tree, and also gives a qualitative and quantitative analysis of basic causes of fault tree. It finally points out the APU fault suggestions to predict and prevent failure occurring.%故障树分析(FTA)是故障分析和故障诊断中广泛应用的一种方法。结合APU的工作特性,对APU启动困难的原因进行了分析,建立APU启动困难故障树,并对故障树的基本原因事件进行了定性、定量分析,进而提出了排除APU故障的方法,以达到预测与预防故障发生的目的。

  1. Diagnosis of Gearbox Typical Fault in Rolling Mills Based on the Wavelet Packets Technology

    CUI Lingli; GAO Lixin; ZHANG Jianyu; DING Fang

    2006-01-01

    The early impulse fault diagnosis of the gearbox in rolling mills is often difficult and labour intensive because the gearbox of that high speed machine is multi-shafting transmission system, in which many gearsets and rolling bears work together at the same time and there are much complex frequency structure and various disturb. A new time-frequency method based on the wavelet packets technique was developed and used to extract the impact feature from signals collected from faulty data of one rolling mills gearbox. The method improves the signal to noise ration so that results obtained using this method represents features with fine resolution in both low-frequency and the high frequency bands. The results of analysis indicate the validity and the practicability of the method proposed here.

  2. OPAD: An expert system for research reactor operations and fault diagnosis using probabilistic safety assessment tools

    A prototype Knowledge Based (KB) operator Adviser (OPAD) system has been developed for 100 MW(th) Heavy Water moderated, cooled and Natural Uranium fueled research reactor. The development objective of this system is to improve reliability of operator action and hence the reactor safety at the time of crises as well as normal operation. The jobs performed by this system include alarm analysis, transient identification, reactor safety status monitoring, qualitative fault diagnosis and procedure generation in reactor operation. In order to address safety objectives at various stages of the Operator Adviser (OPAD) system development the Knowledge has been structured using PSA tools/information in an shell environment. To demonstrate the feasibility of using a combination of KB approach with PSA for operator adviser system, salient features of some of the important modules (viz. FUELEX, LOOPEX and LOCAEX) have been discussed. It has been found that this system can serve as an efficient operator support system

  3. Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors

    David Camarena-Martinez

    2014-01-01

    Full Text Available Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE-based frequency estimator and a feed forward neural network (FFNN-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications.

  4. OPERATIONAL FAULT DIAGNOSIS IN INDUSTRIAL HYDRAULIC SYSTEMS THROUGH MODELING THE INTERNAL LEAKAGE OF ITS COMPONENTS

    P. Athanasatos

    2013-01-01

    Full Text Available In this study, a model of a high pressure hydraulic system was developed using the bond graph method to investigate the effect of the internal leakage of its main components (pump, cylinder and 4/2 way valve on the operational characteristics of the system under various loads. All the main aspects of the hydraulic circuit (like the internal leakages, the compressibility of the fluid, the hydraulic pressure drop, the inertia of moving masses and the friction of the spool were taken into consideration. The results of this modeling were compared with the experimental data taken from the literature and from an actual test platform installed in the laboratory. Modeling and experimental data curves correlate very well in form, magnitude and response times for all the system’s main parameters. This proves that the present method can be used to accurately model the response and operation of hydraulic systems and can thus be used for operational fault diagnosis in many cases, especially in simulating fault scenarios when the defective component is not obvious. This is very important in industrial production systems where unpredictable shutdowns of the hydraulic machinery have a considerable negative economic impact on cost.

  5. Empirical mode decomposition and neural networks on FPGA for fault diagnosis in induction motors.

    Camarena-Martinez, David; Valtierra-Rodriguez, Martin; Garcia-Perez, Arturo; Osornio-Rios, Roque Alfredo; Romero-Troncoso, Rene de Jesus

    2014-01-01

    Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intelligent methodology for the automatic diagnosis during the startup transient of motor faults such as: one and two broken rotor bars, bearing defects, and unbalance. Moreover, the overall methodology implementation into a field-programmable gate array (FPGA) allows an online and real-time operation, thanks to its parallelism and high-performance capabilities as a system-on-a-chip (SoC) solution. The detection and classification results show the effectiveness of the proposed fused techniques; besides, the high precision and minimum resource usage of the developed digital structures make them a suitable and low-cost solution for this and many other industrial applications. PMID:24678281

  6. A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis

    Since machinery fault vibration signals are usually multicomponent modulation signals, how to decompose complex signals into a set of mono-components whose instantaneous frequency (IF) has physical sense has become a key issue. Local mean decomposition (LMD) is a new kind of time–frequency analysis approach which can decompose a signal adaptively into a set of product function (PF) components. In this paper, a modulation feature extraction method-based LMD is proposed. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. The computed IF and IA are displayed together in the form of time–frequency representation (TFR). Modulation features can be extracted from the spectrum analysis of the IA and IF. In order to make the IF have physical meaning, the phase-unwrapping algorithm and IF processing method of extrema are presented in detail along with a simulation FM signal example. Besides, the dependence of the LMD method on the signal-to-noise ratio (SNR) is also investigated by analyzing synthetic signals which are added with Gaussian noise. As a result, the recommended critical SNRs for PF decomposition and IF extraction are given according to the practical application. Successful fault diagnosis on a rolling bearing and gear of locomotive bogies shows that LMD has better identification capacity for modulation signal processing and is very suitable for failure detection in rotating machinery

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

    Žvokelj, Matej; Zupan, Samo; Prebil, Ivan

    2016-05-01

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

  8. Novel indices for broken rotor bars fault diagnosis in induction motors using wavelet transform

    Ebrahimi, Bashir Mahdi; Faiz, Jawad; Lotfi-fard, S.; Pillay, P.

    2012-07-01

    This paper introduces novel indices for broken rotor bars diagnosis in three-phase induction motors based on wavelet coefficients of stator current in a specific frequency band. These indices enable to diagnose occurrence and determine number of broken bars in different loads precisely. Besides thanks to the suitability of wavelet transform in transient conditions, it is possible to detect the fault during the start-up of the motor. This is important in the case of start-up of large induction motors with long starting time and also motors with frequent start-up. Furthermore, broken rotor bars in induction motor are detected using spectra analysis of the stator current. It is also shown that rise of number of broken bars and load levels increases amplitude of the particular side-band components of the stator currents in the faulty case. An induction motor with 1, 2, 3 and 4 broken bars at the rated load and the motor with 4 broken bars at no-load, 33%, 66%, 100% and 133% rated load are investigated. Time stepping finite element method is used for modeling broken rotor bars faults in induction motors. In this modeling, effects of the stator winding distribution, stator and rotor slots, geometrical and physical characteristics of different parts of the motor and non-linearity of the core materials are taken into account. The simulation results are are verified by the experimental results.

  9. Maximum margin classification based on flexible convex hulls for fault diagnosis of roller bearings

    Zeng, Ming; Yang, Yu; Zheng, Jinde; Cheng, Junsheng

    2016-01-01

    A maximum margin classification based on flexible convex hulls (MMC-FCH) is proposed and applied to fault diagnosis of roller bearings. In this method, the class region of each sample set is approximated by a flexible convex hull of its training samples, and then an optimal separating hyper-plane that maximizes the geometric margin between flexible convex hulls is constructed by solving a closest pair of points problem. By using the kernel trick, MMC-FCH can be extended to nonlinear cases. In addition, multi-class classification problems can be processed by constructing binary pairwise classifiers as in support vector machine (SVM). Actually, the classical SVM also can be regarded as a maximum margin classification based on convex hulls (MMC-CH), which approximates each class region with a convex hull. The convex hull is a special case of the flexible convex hull. To train a MMC-FCH classifier, time-domain and frequency-domain statistical parameters are extracted not only from raw vibration signals but also from the resulting intrinsic mode functions (IMFs) by performing empirical mode decomposition (EMD) on the raw signals, and then the distance evaluation technique (DET) is used to select salient features from the whole statistical features. The experiments on bearing datasets show that the proposed method can reliably recognize different bearing faults.

  10. Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

    Shuang Pan

    2016-01-01

    Full Text Available An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults. The first five most important features are selected from the feature vectors and classified using support vector machine. The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig. The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy. It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.

  11. Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis

    A novel method for enhancing gearbox fault diagnosis and prognosis is developed by fusion of multiple health indicators through support vector data description. First, the Comblet transform is used to identify gear residual error signals from the raw signal. Second, based on the observation of gear residual error signals, a total of 11 gear health indicators are identified, and are categorized into two types of indicators. The first and second types of indicators are for fault diagnosis and prognosis, respectively. The first type has six indicators, which are sensitive to impulsive signals triggered by anomalous impacts. The second type has five indicators, which are suitable for tracking degradation of faults. Third, through the support vector data description, the first six health indicators are fused into type one indicators for fault diagnosis. The remaining five indicators are fused into type two indicators for fault prognosis. Finally, a Gaussian kernel is designed to enhance the performance of type one and two indicators by optimal range of width size. The effectiveness of the proposed method is validated through experiments. The new method has been proven to be superior to methods that use unfused indicators individually

  12. Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

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

    2014-01-01

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

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

    Fang Liu

    2014-05-01

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

  14. A Proposition for Geodetic Recording of Active Fault Zones

    Ladislav Placer; Božo Koler

    2007-01-01

    Establishing recent displacements along faults is an important and delicate task. Larger faults are accompanied by broader fault zones that require a specific approach to geodetic measurements of fault block displacements. The vector of fault block displacements, or resultant, is a vector sum of differential displacements within the fault zone. For the purposes of recording the displacements we propose the stabilization of a geodetic network of points positioned in fault blocks...

  15. Finding Active Faults in a Glaciated and Forested Landscape: the Southern Whidbey Island Fault, Washington

    Blakely, R. J.; Sherrod, B. L.; Wells, R. E.; Weaver, C. S.

    2004-12-01

    The Puget Lowland, Washington, lies within the Cascadia forearc and is underlain by at least six seismically active and regionally significant crustal faults that together accommodate several mm/yr of net north-south shortening. The surface expression of pre-15-ka slip on Puget Lowland faults has been largely scoured away or covered by glacial deposits, and younger fault geomorphology is often concealed by vegetation and urban development. High-resolution aeromagnetic and lidar surveys, followed by geologic site investigations, have identified and confirmed late Holocene deformation on each of these mostly concealed but potentially hazardous faults. Most geomorphic features identified in lidar data are closely associated with linear magnetic anomalies that reflect the underlying basement structure of the fault and help map its full extent. The southern Whidbey Island fault (SWIF) is a case in point. The northwest-striking SWIF was mapped previously using borehole data and potential-field anomalies on Whidbey Island and marine seismic-reflection surveys beneath surrounding waterways. Gravity inversions and aeromagnetic mapping suggest that the SWIF extends at least 50 km southeast, from Vancouver Island to the Washington mainland, and transitions along its length from northeast-side-down beneath Puget Sound to northeast-side-up on the mainland. Abrupt subsidence at a coastal marsh on south-central Whidbey Island suggests that the SWIF experienced a MW 6.5 to 7.0 earthquake about 3 ka. Southeast of Whidbey Island, a hypothesized southeastward projection of the SWIF makes landfall between the cities of Seattle and Everett. Linear, northwest-striking magnetic anomalies in this mainland region do coincide with this hypothesized projection, are low in amplitude, and are best illuminated in residual magnetic fields. The most prominent of the residual magnetic anomalies extends at least 16 km, lies approximately on strike with the SWIF on Whidbey Island, and passes within

  16. 往复式压缩机故障诊断方法研究%Fault diagnosis for reciprocating compressor

    鹿钦鹤; 孙超; 郭长滨; 孙颖

    2015-01-01

    本文通过对往复式压缩机的应用研究,论述了其发展历程及工作原理,并对其故障现象及种类和原因进行了分析,列出常用的故障诊断方法及其不足,提出往复式压缩机故障诊断方法和研究方向。%The development process and principles of reciprocating compressor are discussed based on application of it. The phenomena,types and reasons of its faults are analyzed as well. The common fault diagnosis and the insufficiency are listed. The fault diagnosis methods and research direction of it are proposed in the end.

  17. Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

    Jianfeng Zhang

    2015-01-01

    Full Text Available During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD. Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs. Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

  18. Application to induction motor faults diagnosis of the amplitude recovery method combined with FFT

    Liu, Yukun; Guo, Liwei; Wang, Qixiang; An, Guoqing; Guo, Ming; Lian, Hao

    2010-11-01

    This paper presents a signal processing method - amplitude recovery method (abbreviated to ARM) - that can be used as the signal pre-processing for fast Fourier transform (FFT) in order to analyze the spectrum of the other-order harmonics rather than the fundamental frequency in stator currents and diagnose subtle faults in induction motors. In this situation, the ARM functions as a filter that can filter out the component of the fundamental frequency from three phases of stator currents of the induction motor. The filtering result of the ARM can be provided to FFT to do further spectrum analysis. In this way, the amplitudes of other-order frequencies can be extracted and analyzed independently. If the FFT is used without the ARM pre-processing and the components of other-order frequencies, compared to the fundamental frequency, are fainter, the amplitudes of other-order frequencies are not able easily to extract out from stator currents. The reason is when the FFT is used direct to analyze the original signal, all the frequencies in the spectrum analysis of original stator current signal have the same weight. The ARM is capable of separating the other-order part in stator currents from the fundamental-order part. Compared to the existent digital filters, the ARM has the benefits, including its stop-band narrow enough just to stop the fundamental frequency, its simple operations of algebra and trigonometry without any integration, and its deduction direct from mathematics equations without any artificial adjustment. The ARM can be also used by itself as a coarse-grained diagnosis of faults in induction motors when they are working. These features can be applied to monitor and diagnose the subtle faults in induction motors to guard them from some damages when they are in operation. The diagnosis application of ARM combined with FFT is also displayed in this paper with the experimented induction motor. The test results verify the rationality and feasibility of the

  19. Generalized stepwise demodulation transform and synchrosqueezing for time-frequency analysis and bearing fault diagnosis

    Shi, Juanjuan; Liang, Ming; Necsulescu, Dan-Sorin; Guan, Yunpeng

    2016-04-01

    for bearing condition monitoring under variable speed conditions include: (a) it can simultaneously improve energy concentration level of signals of interest and remove interferences in the TFR, (b) it is resampling-free and hence can avoid the resampling related errors, and (c) it yields instantaneous frequencies for fault and shaft rotation and thus can carry out both fault detection and diagnosis tasks.

  20. Research on method of nuclear power plant operation fault diagnosis based on a combined artificial neural network

    To solve the online real-time diagnosis problem of the nuclear power plant in operating condition, a method based on a combined artificial neural network is put forward in the paper. Its main principle is: using the BP neural network for the fast group diagnosis, and then using the RBF neural network for distinguishing and verifying the diagnostic result. The accuracy of the method is verified using the simulation values of the key parameters in normal status and malfunction status of a nuclear power plant. The results show that the method combining the advantages of the two neural networks can not only diagnose the learned faults in similar power level of the nuclear power plant quickly and accurately, but also can identify the faults in different power status, as well as the unlearned faults. The outputs of the diagnosis system are in form of the reliability of the faults, and are changing with the lasting of the operation time of the plant. This makes the diagnosis results be more acceptable to operators. (authors)