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Sample records for based fault diagnosis

  1. Performance based fault diagnosis

    DEFF Research Database (Denmark)

    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...

  2. Information Based Fault Diagnosis

    DEFF Research Database (Denmark)

    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. Fault tolerant control based on active fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2005-01-01

    An active fault diagnosis (AFD) method will be considered in this paper in connection with a Fault Tolerant Control (FTC) architecture based on the YJBK parameterization of all stabilizing controllers. The architecture consists of a fault diagnosis (FD) part and a controller reconfiguration (CR...

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

    Directory of Open Access Journals (Sweden)

    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.

  5. Fault diagnosis

    Science.gov (United States)

    Abbott, Kathy

    1990-01-01

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

  6. Fault diagnosis and fault-tolerant control based on adaptive control approach

    CERN Document Server

    Shen, Qikun; Shi, Peng

    2017-01-01

    This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering. .

  7. Application of the fault diagnosis strategy based on hierarchical information fusion in motors fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    This paper has analyzed merits and demerits of both neural network technique and of the information fusion methods based on the D-S (dempster-shafer evidence) Theory as well as their complementarity, proposed the hierarchical information fusion fault diagnosis strategy by combining the neural network technique and the fused decision diagnosis based on D-S Theory, and established a corresponding functional model. Thus, we can not only solve a series of problems caused by rapid growth in size and complexity of neural network structure with diagnosis parameters increasing, but also can provide effective method for basic probability assignment in D-S Theory. The application of the strategy to diagnosing faults of motor bearings has proved that this method is of fairly high accuracy and reliability in fault diagnosis.

  8. Parity space-based fault diagnosis of CCBII braking system

    Institute of Scientific and Technical Information of China (English)

    黄志武; 杨迎泽; 王晶; 李赟

    2013-01-01

    Fault diagnosis is a key issue of the CCBII(computer controlled brake II) braking system, because the CCBII braking system is very complicated and nonlinear, which may exhibit isolated and multi-component coupled faults. A parity space-based method was proposed for fault diagnosis of CCBII braking systems. Firstly, the mathematical models were established according to three function modules of CCBII braking systems where the air fluid theory was utilized. Then, parity vector and threshold function were designed for each output of the system so as to identify more system faults. Fault character matrix was built based on the causal relationship between the output and the fault according to the system function and internal structure. Finally, fault detection and isolation can be realized by the comparison of the observed system output and the fault character matrix. Simulation results show that the proposed method is entirely feasible and effective.

  9. Fault diagnosis based on continuous simulation models

    Science.gov (United States)

    Feyock, Stefan

    1987-01-01

    The results are described of an investigation of techniques for using continuous simulation models as basis for reasoning about physical systems, with emphasis on the diagnosis of system faults. It is assumed that a continuous simulation model of the properly operating system is available. Malfunctions are diagnosed by posing the question: how can we make the model behave like that. The adjustments that must be made to the model to produce the observed behavior usually provide definitive clues to the nature of the malfunction. A novel application of Dijkstra's weakest precondition predicate transformer is used to derive the preconditions for producing the required model behavior. To minimize the size of the search space, an envisionment generator based on interval mathematics was developed. In addition to its intended application, the ability to generate qualitative state spaces automatically from quantitative simulations proved to be a fruitful avenue of investigation in its own right. Implementations of the Dijkstra transform and the envisionment generator are reproduced in the Appendix.

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

    CERN Document Server

    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. Online Fault Diagnosis Method Based on Nonlinear Spectral Analysis

    Institute of Scientific and Technical Information of China (English)

    WEI Rui-xuan; WU Li-xun; WANG Yong-chang; HAN Chong-zhao

    2005-01-01

    The fault diagnosis based on nonlinear spectral analysis is a new technique for the nonlinear fault diagnosis, but its online application could be limited because of the enormous compution requirements for the estimation of general frequency response functions. Based on the fully decoupled Volterra identification algorithm, a new online fault diagnosis method based on nonlinear spectral analysis is presented, which can availably reduce the online compution requirements of general frequency response functions. The composition and working principle of the method are described, the test experiments have been done for damping spring of a vehicle suspension system by utilizing the new method, and the results indicate that the method is efficient.

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

    Energy Technology Data Exchange (ETDEWEB)

    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)

  13. Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

    Directory of Open Access Journals (Sweden)

    Shoubin Wang

    2014-01-01

    Full Text Available As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance matrix as a descriptor of the time sequence. Riemannian distance is used as the similarity measure and the statistical process control diagram is applied to detect the abnormity of multivariate time series. And the visualization of the covariance matrix distribution is used to detect the abnormity of mechanical equipment, leading to realize the fault diagnosis. With wind turbine gearbox faults as the experiment object, the fault diagnosis method is verified and the results show that the method is reasonable and effective.

  14. Rule - based Fault Diagnosis Expert System for Wind Turbine

    Directory of Open Access Journals (Sweden)

    Deng Xiao-Wen

    2017-01-01

    Full Text Available Under the trend of increasing installed capacity of wind power, the intelligent fault diagnosis of wind turbine is of great significance to the safe and efficient operation of wind farms. Based on the knowledge of fault diagnosis of wind turbines, this paper builds expert system diagnostic knowledge base by using confidence production rules and expert system self-learning method. In Visual Studio 2013 platform, C # language is selected and ADO.NET technology is used to access the database. Development of Fault Diagnosis Expert System for Wind Turbine. The purpose of this paper is to realize on-line diagnosis of wind turbine fault through human-computer interaction, and to improve the diagnostic capability of the system through the continuous improvement of the knowledge base.

  15. Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

    Directory of Open Access Journals (Sweden)

    Xiang Wang

    2015-07-01

    Full Text Available Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD, and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

  16. Fault Diagnosis for Fuel Cell Based on Naive Bayesian Classification

    Directory of Open Access Journals (Sweden)

    Liping Fan

    2013-07-01

    Full Text Available Many kinds of uncertain factors may exist in the process of fault diagnosis and affect diagnostic results. Bayesian network is one of the most effective theoretical models for uncertain knowledge expression and reasoning. The method of naive Bayesian classification is used in this paper in fault diagnosis of a proton exchange membrane fuel cell (PEMFC system. Based on the model of PEMFC, fault data are obtained through simulation experiment, learning and training of the naive Bayesian classification are finished, and some testing samples are selected to validate this method. Simulation results demonstrate that the method is feasible.    

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

    Institute of Scientific and Technical Information of China (English)

    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.

  18. Wind Turbine Gearbox Fault Diagnosis Method Based on Riemannian Manifold

    OpenAIRE

    Shoubin Wang; Xiaogang Sun; Chengwei Li

    2014-01-01

    As multivariate time series problems widely exist in social production and life, fault diagnosis method has provided people with a lot of valuable information in the finance, hydrology, meteorology, earthquake, video surveillance, medical science, and other fields. In order to find faults in time sequence quickly and efficiently, this paper presents a multivariate time series processing method based on Riemannian manifold. This method is based on the sliding window and uses the covariance mat...

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

    Science.gov (United States)

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

    2016-01-18

    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.

  20. Fault diagnosis based on controller modification

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2015-01-01

    Detection and isolation of parametric faults in closed-loop systems will be considered in this paper. A major problem is that a feedback controller will in general reduce the effects from variations in the systems including parametric faults on the controlled output from the system. Parametric...... time. A negative effect of increasing the amplitude of the auxiliary input is that the disturbances in the external output will be increased and consequently reduce the closed-loop performance. This problem can be handled by using a modification of the feedback controller. Applying the YJBK......-parameterization (after Youla, Jabr, Bongiorno and Kucera) for the controller, it is possible to modify the feedback controller with a minor effect on the closed-loop performance in the fault-free case and at the same time optimize the detection and isolation in a faulty case. Controller modification in connection...

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

    Science.gov (United States)

    Ortiz, José; Carrasco, Rodrigo A.

    2016-07-01

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

  2. Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Jinde Zheng

    2014-01-01

    Full Text Available 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 employed to extract the nonlinear fault characteristics from the bearing vibration signal in different scales. Besides, the SVM is utilized to accomplish the fault feature classification to fulfill diagnostic procedure automatically. Meanwhile, in order to avoid a high dimension of features, the Laplacian score (LS is used to refine the feature vector by ranking the features according to their importance and correlations with the main fault information. Finally, the rolling bearing fault diagnosis method based on MPE, LS, and SVM is proposed and applied to the experimental data. The experimental data analysis results indicate that the proposed method could identify the fault categories effectively.

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

    Science.gov (United States)

    Wang, H.; Jing, X. J.

    2017-07-01

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

  4. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.

    Science.gov (United States)

    Deng, Xiaogang; Tian, Xuemin; Chen, Sheng; Harris, Chris J

    2016-12-22

    Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

  5. Model-Based Methods for Fault Diagnosis: Some Guide-Lines

    DEFF Research Database (Denmark)

    Patton, R.J.; Chen, J.; Nielsen, S.B.

    1995-01-01

    This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties.......This paper provides a review of model-based fault diagnosis techniques. Starting from basic principles, the properties....

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

    Directory of Open Access Journals (Sweden)

    Mouna Karmani

    2011-09-01

    Full Text Available In this paper, we propose a simulation-before-test (SBT fault diagnosis methodology based on the use of a fault dictionary approach. This technique allows the detection and localization of the most likely defects of open-circuit type occurring in Complementary Metal–Oxide–Semiconductor (CMOS analog integrated circuits (ICs interconnects. The fault dictionary is built by simulating the most likely defects causing the faults to be detected at the layout level. Then, for each injected fault, the spectre’s frequency responses and the 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 a considered defect. When testing, the circuit under test is excited with the same stimulus, and the responses obtained are compared to the stored ones. To prove the efficiency of the proposed technique, a full custom CMOS operational amplifier is implemented in 0.25 μm technology and the most likely faults of open circuit type are deliberately injected and simulated at the layout level.

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

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  11. Fault Diagnosis of Machine Based on Fuzzy Reliability Theory

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    According to life analysis in reliability theory, certain diagnosis rules can be used to diagnose machines' faults. On this basis, considering the indefiniteness in machine working states, the accurate diagnosis rule was extended to fuzzy diagnosis rule by using basic concepts and methods of fuzzy mathematics. The formulas of fault probability under different conditions were deduced. In the end, an example is given and the results of two methods were compared.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  13. Research of Multi-Agent System based satellite fault diagnosis technology

    Institute of Scientific and Technical Information of China (English)

    范显峰; 姜兴渭; 黄文虎; 谷吉海

    2002-01-01

    Following the theory of Multi-Agent System (MAS) and using series-wound structure and shunt-wound structure of Agents, the performance of Agent was improved to satisfy the need of satellite fault diagno-sis, and a tridimensional MAS model of satellite fault diagnosis was thus established for the MAS based planardiagnosis system, which decentralizes the whole diagnosing task into subtasks to be performed by different func-tional Agents to make the complicated fault diagnosis very simple and the diagnosis system more intelligent.This method improved the reliability and accuracy of diagnosis and made the maintenance and upgrading of thesatellite fault diagnosis system very easy as well.

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

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    KAUST Repository

    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.

  17. Artificial immunity-based induction motor bearing fault diagnosis

    OpenAIRE

    Hakan ÇALIŞ; ÇAKIR, Abdülkadir; Emre DANDIL

    2013-01-01

    In this study, the artificial immunity of the negative selection algorithm is used for bearing fault detection. It is implemented in MATLAB-based graphical user interface software. The developed software uses amplitudes of the vibration signal in the time and frequency domains. Outer, inner, and ball defects in the bearings of the induction motor are detected by anomaly monitoring. The time instants of the fault occurrence and fault level are determined according to the number of a...

  18. Fault Diagnosis of a Rotary Machine Based on Information Entropy and Rough Set

    Institute of Scientific and Technical Information of China (English)

    LI Jian-lan; HUANG Shu-hong

    2007-01-01

    There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.

  19. Multiple Local Reconstruction Model-based Fault Diagnosis for Continuous Processes

    Institute of Scientific and Technical Information of China (English)

    ZHAO Chun-Hui; LI Wen-Qing; SUN You-Xian; GAO Fu-Rong

    2013-01-01

    In the present work,the multiplicity of fault characteristics is proposed and analyzed to improve the fault diagnosis performance.It is based on the following recognition that the underlying fault characteristics in general do not stay constant but will present changes along the time direction.That is,the fault process reveals different variable correlations across different time periods.To analyze the multiplicity of fault characteristics,a fault division algorithm is developed to divide the fault process into multiple local time periods where the fault characteristics are deemed similar within the same local time period.Then a representative fault decomposition model is built in each local time period to reveal the relationships between the fault and normal operation status.In this way,these different fault characteristics can be modeled respectively.The proposed method gives an interesting insight into the fault evolvement behaviors and a more accurate from-fault-to-normal reconstruction result can be expected for fault diagnosis.The feasibility and performance of the proposed fault diagnosis method are illustrated with the Tennessee Eastman process.

  20. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

    Directory of Open Access Journals (Sweden)

    Hong Yin

    2014-01-01

    Full Text Available The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

  1. Network Fault Diagnosis Using DSM

    Institute of Scientific and Technical Information of China (English)

    Jiang Hao; Yan Pu-liu; Chen Xiao; Wu Jing

    2004-01-01

    Difference similitude matrix (DSM) is effective in reducing information system with its higher reduction rate and higher validity. We use DSM method to analyze the fault data of computer networks and obtain the fault diagnosis rules. Through discretizing the relative value of fault data, we get the information system of the fault data. DSM method reduces the information system and gets the diagnosis rules. The simulation with the actual scenario shows that the fault diagnosis based on DSM can obtain few and effective rules.

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

    Science.gov (United States)

    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.

  3. Fault Diagnosis of Nonlinear Systems Based on Hybrid PSOSA Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    Ling-Lai Li; Dong-Hua Zhou; Ling Wang

    2007-01-01

    Fault diagnosis of nonlinear systems is of great importance in theory and practice, and the parameter estimation method is an effective strategy. Based on the framework of moving horizon estimation, fault parameters are identified by a proposed intelligent optimization algorithm called PSOSA, which could avoid premature convergence of standard particle swarm optimization (PSO) by introducing the probabilistic jumping property of simulated annealing (SA). Simulations on a three-tank system show the effectiveness of this optimization based fault diagnosis strategy.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  5. Fuzzy Concurrent Object Oriented Expert System for Fault Diagnosis in 8085 Microprocessor Based System Board

    Directory of Open Access Journals (Sweden)

    Mr.D. V. Kodavade

    2014-09-01

    Full Text Available With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations were handled using fuzzy mathematics properties. Fuzzy inference mechanism is demonstrated using one case study. Some typical faults in 8085 microprocessor board and diagnostic procedures used is presented in this paper.

  6. Study on Fault Diagnosis of Rolling Bearing Based on Time-Frequency Generalized Dimension

    Directory of Open Access Journals (Sweden)

    Yu Yuan

    2015-01-01

    Full Text Available The condition monitoring technology and fault diagnosis technology of mechanical equipment played an important role in the modern engineering. Rolling bearing is the most common component of mechanical equipment which sustains and transfers the load. Therefore, fault diagnosis of rolling bearings has great significance. Fractal theory provides an effective method to describe the complexity and irregularity of the vibration signals of rolling bearings. In this paper a novel multifractal fault diagnosis approach based on time-frequency domain signals was proposed. The method and numerical algorithm of Multi-fractal analysis in time-frequency domain were provided. According to grid type J and order parameter q in algorithm, the value range of J and the cut-off condition of q were optimized based on the effect on the dimension calculation. Simulation experiments demonstrated that the effective signal identification could be complete by multifractal method in time-frequency domain, which is related to the factors such as signal energy and distribution. And the further fault diagnosis experiments of bearings showed that the multifractal method in time-frequency domain can complete the fault diagnosis, such as the fault judgment and fault types. And the fault detection can be done in the early stage of fault. Therefore, the multifractal method in time-frequency domain used in fault diagnosis of bearing is a practicable method.

  7. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    Science.gov (United States)

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

  8. Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition

    Directory of Open Access Journals (Sweden)

    Bo Zhou

    2016-01-01

    Full Text Available Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT, is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.

  9. Fault-diagnosis applications. Model-based condition monitoring. Acutators, drives, machinery, plants, sensors, and fault-tolerant systems

    Energy Technology Data Exchange (ETDEWEB)

    Isermann, Rolf [Technische Univ. Darmstadt (DE). Inst. fuer Automatisierungstechnik (IAT)

    2011-07-01

    Supervision, condition-monitoring, fault detection, fault diagnosis and fault management play an increasing role for technical processes and vehicles in order to improve reliability, availability, maintenance and lifetime. For safety-related processes fault-tolerant systems with redundancy are required in order to reach comprehensive system integrity. This book is a sequel of the book ''Fault-Diagnosis Systems'' published in 2006, where the basic methods were described. After a short introduction into fault-detection and fault-diagnosis methods the book shows how these methods can be applied for a selection of 20 real technical components and processes as examples, such as: Electrical drives (DC, AC) Electrical actuators Fluidic actuators (hydraulic, pneumatic) Centrifugal and reciprocating pumps Pipelines (leak detection) Industrial robots Machine tools (main and feed drive, drilling, milling, grinding) Heat exchangers Also realized fault-tolerant systems for electrical drives, actuators and sensors are presented. The book describes why and how the various signal-model-based and process-model-based methods were applied and which experimental results could be achieved. In several cases a combination of different methods was most successful. The book is dedicated to graduate students of electrical, mechanical, chemical engineering and computer science and for engineers. (orig.)

  10. Wavelet neural network based fault diagnosis in nonlinear analog circuits

    Institute of Scientific and Technical Information of China (English)

    Yin Shirong; Chen Guangju; Xie Yongle

    2006-01-01

    The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation(BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

  11. Semi-supervised weighted kernel clustering based on gravitational search for fault diagnosis.

    Science.gov (United States)

    Li, Chaoshun; Zhou, Jianzhong

    2014-09-01

    Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this paper, a semi-supervised kernel clustering model is designed to diagnose known and unknown faults. At first, a novel semi-supervised weighted kernel clustering algorithm based on gravitational search (SWKC-GS) is proposed for clustering of dataset composed of labeled and unlabeled fault samples. The clustering model of SWKC-GS is defined based on wrong classification rate of labeled samples and fuzzy clustering index on the whole dataset. Gravitational search algorithm (GSA) is used to solve the clustering model, while centers of clusters, feature weights and parameter of kernel function are selected as optimization variables. And then, new fault samples are identified and diagnosed by calculating the weighted kernel distance between them and the fault cluster centers. If the fault samples are unknown, they will be added in historical dataset and the SWKC-GS is used to partition the mixed dataset and update the clustering results for diagnosing new fault. In experiments, the proposed method has been applied in fault diagnosis for rotatory bearing, while SWKC-GS has been compared not only with traditional clustering methods, but also with SVM and neural network, for known fault diagnosis. In addition, the proposed method has also been applied in unknown fault diagnosis. The results have shown effectiveness of the proposed method in achieving expected diagnosis accuracy for both known and unknown faults of rotatory bearing.

  12. A Fault Diagnosis Method of Power Systems Based on Gray System Theory

    Directory of Open Access Journals (Sweden)

    Huang Darong

    2015-01-01

    Full Text Available To provide some decision-making suggestions for fault diagnosis in power systems, a new model for identifying fault component is constructed by using Gray theory. Firstly, the basic concepts of Gray theory are introduced and explained in detail. And then the recognition algorithm of the power supply interrupted districts and the assignment principle of fault state vectors are depicted according to the working principle of protective relays (PRs and circuit breakers (CBs. Secondly, based on the concept of the Gray correlation degree, the fault information explanation degree model is constructed and the judging method of malfunction and rejection for PRs and CBs is established. Meanwhile, to achieve the goal of the fault diagnosis, the fault diagnosis procedure that determined which components malfunction is designed for power systems. Finally, some simple experiments have already verified that the proposed method and model are effective and reasonable and the trend of further research is analyzed and summarized.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  14. GEARBOX FAULT DIAGNOSIS BASED ON EMPIRICAL MODE DECOMPOSITION

    Institute of Scientific and Technical Information of China (English)

    Shen Guoji; Tao Limin; Chen Zhongsheng

    2004-01-01

    Time synchronous averaging of vibration data is a fundament technique for gearbox diagnosis. Currently, this technique relies on hardware tachometer to give phase synchronous information. Empirical mode decomposition (EMD) is introduced to replace time synchronous averaging of gearbox vibration signal. With it, any complicated dataset can be decomposed into a finite and often small number of intrinsic mode functions (IMF). The key problem is how to assure that vibration signals deduced by gear defects could be sifted out by EMD. The characteristic vibration signals of gear defects are proved IMFs, which makes it possible to utilize EMD for the diagnosis of gearbox faults. The method is validated by data from recordings of the vibration of a single-stage spiral bevel gearbox with fatigue pitting. The results show EMD is powerful to extract characteristic information from noisy vibration signals.

  15. Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    TIAN Huifang; SUN Shanxia

    2006-01-01

    By introducing Rough Set Theory and the principle of Support vector machine, a gear fault diagnosis method based on them is proposed. Firstly, diagnostic decision-making is reduced based on rough set theory, and the noise and redundancy in the sample are removed, then, according to the chosen reduction, a support vector machine multi-classifier is designed for gear fault diagnosis. Therefore, SVM' training data can be reduced and running speed can quicken. Test shows its accuracy and efficiency of gear fault diagnosis.

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

    Science.gov (United States)

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

    2016-08-01

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

  17. Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Kuo-Nan Yu

    2014-01-01

    Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    Costamagna, Paola; De Giorgi, Andrea; Gotelli, Alberto; Magistri, Loredana; Moser, Gabriele; Sciaccaluga, Emanuele; Trucco, Andrea

    2016-08-22

    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.

  20. Research of the Fault Diagnosis Method for the Thruster of AUV Based on Information Fusion

    Science.gov (United States)

    Wang, Yu-Jia; Zhang, Ming-Jun; Wu, Juan

    Aiming at the problem of thruster fault diagnosis of AUV, the motion condition model of AUV based on the improved dynamic recursive Elman neural network, and the performance model of thruster based on the Radial Basis Function network were established. And the fault fusion diagnosis method was proposed according to the overall and local fault detection. Through comparing the output value of motion condition model with the measured value of actual speed and angle, it obtained the overall fault information. Also, it obtained the direct fault information through analyzing the residual which was produced by comparing the output of the performance model with the measured value of the actual voltage and current of the each thruster. According to the decision level information fusion of two kinds of information, it realized the fault diagnosis of thrusters and analyzed the fault degree and reliability. The results of the fault-simulation experiment show that the proposed fault fusion diagnosis method for the thruster of AUV is feasible and effective.

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

    Directory of Open Access Journals (Sweden)

    Jian-Jiun Ding

    2012-07-01

    Full Text Available 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 demonstrated that the proposed method is a very powerful algorithm for bearing fault diagnosis and has much better performance than the methods based on single scale permutation entropy (PE and multiscale entropy (MSE.

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

    DEFF Research Database (Denmark)

    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 i...

  3. Fast EEMD Based AM-Correntropy Matrix and Its Application on Roller Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Yunxiao Fu

    2016-06-01

    Full Text Available Roller bearing plays a significant role in industrial sectors. To improve the ability of roller bearing fault diagnosis under multi-rotating situation, this paper proposes a novel roller bearing fault characteristic: the Amplitude Modulation (AM based correntropy extracted from the Intrinsic Mode Functions (IMFs, which are decomposed by Fast Ensemble Empirical mode decomposition (FEEMD and employ Least Square Support Vector Machine (LSSVM to implement intelligent fault identification. Firstly, the roller bearing vibration acceleration signal is decomposed by FEEMD to extract IMFs. Secondly, IMF correntropy matrix (IMFCM as the fault feature matrix is calculated from the AM-correntropy model of the primary vibration signal and IMFs. Furthermore, depending on LSSVM, the fault identification results of the roller bearing are obtained. Through the bearing identification experiments in stationary rotating conditions, it was verified that IMFCM generates more stable and higher diagnosis accuracy than conventional fault features such as energy moment, fuzzy entropy, and spectral kurtosis. Additionally, it proves that IMFCM has more diagnosis robustness than conventional fault features under cross-mixed roller bearing operating conditions. The diagnosis accuracy was more than 84% for the cross-mixed operating condition, which is much higher than the traditional features. In conclusion, it was proven that FEEMD-IMFCM-LSSVM is a reliable technology for roller bearing fault diagnosis under the constant or multi-positioned operating conditions, and as such, it possesses potential prospects for a broad application of uses.

  4. Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes

    Directory of Open Access Journals (Sweden)

    Jose M. Bernal-de-Lázaro

    2016-05-01

    Full Text Available This article summarizes the main contributions of the PhD thesis titled: "Application of learning techniques based on kernel methods for the fault diagnosis in Industrial processes". This thesis focuses on the analysis and design of fault diagnosis systems (DDF based on historical data. Specifically this thesis provides: (1 new criteria for adjustment of the kernel methods used to select features with a high discriminative capacity for the fault diagnosis tasks, (2 a proposed approach process monitoring using statistical techniques multivariate that incorporates a reinforced information concerning to the dynamics of the Hotelling's T2 and SPE statistics, whose combination with kernel methods improves the detection of small-magnitude faults; (3 an robustness index to compare the diagnosis classifiers performance taking into account their insensitivity to possible noise and disturbance on historical data.

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

    Directory of Open Access Journals (Sweden)

    Xianfeng Yuan

    2015-01-01

    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.

  6. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    Directory of Open Access Journals (Sweden)

    Zengkai Liu

    Full Text Available This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  9. Diagnosis and Early Warning of Wind Turbine Faults Based on Cluster Analysis Theory and Modified ANFIS

    Directory of Open Access Journals (Sweden)

    Quan Zhou

    2017-07-01

    Full Text Available The construction of large-scale wind farms results in a dramatic increase of wind turbine (WT faults. The failure mode is also becoming increasingly complex. This study proposes a new model for early warning and diagnosis of WT faults to solve the problem of Supervisory Control And Data Acquisition (SCADA systems, given that the traditional threshold method cannot provide timely warning. First, the characteristic quantity of fault early warning and diagnosis analyzed by clustering analysis can obtain in advance abnormal data in the normal threshold range by considering the effects of wind speed. Based on domain knowledge, Adaptive Neuro-fuzzy Inference System (ANFIS is then modified to establish the fault early warning and diagnosis model. This approach improves the accuracy of the model under the condition of absent and sparse training data. Case analysis shows that the effect of the early warning and diagnosis model in this study is better than that of the traditional threshold method.

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

    Directory of Open Access Journals (Sweden)

    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.

  11. EMD and Wavelet Transform Based Fault Diagnosis for Wind Turbine Gear Box

    Directory of Open Access Journals (Sweden)

    Qingyu Yang

    2013-01-01

    Full Text Available Wind turbines are mainly located in harsh environment, and the maintenance is therefore very difficult. The wind turbine faults are mostly from the gear box, and the fault signal is generally nonlinear and nonstationary. The traditional fault diagnosis methods such as Fast Fourier transform (FFT and the inverted frequency spectrum identification method based on FFT are not satisfactory in processing this kind of signal. This paper proposes a hybrid fault diagnosis method which combines the empirical mode decomposition (EMD and wavelet transform. The vibration signal is analyzed through wavelet transform, and the aliasing in high-frequency signals is then addressed by conducting EMD to the original signals. The experimental results based on a specific wind turbine gear box demonstrate that this method can diagnose the faults and locate their positions accurately.

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

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  14. Fault Diagnosis for Rolling Bearings under Variable Conditions Based on Visual Cognition

    Directory of Open Access Journals (Sweden)

    Yujie Cheng

    2017-05-01

    Full Text Available Fault diagnosis for rolling bearings has attracted increasing attention in recent years. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper introduces a fault diagnosis method for rolling bearings under variable conditions based on visual cognition. The proposed method includes the following steps. First, the vibration signal data are transformed into a recurrence plot (RP, which is a two-dimensional image. Then, inspired by the visual invariance characteristic of the human visual system (HVS, we utilize speed up robust feature to extract fault features from the two-dimensional RP and generate a 64-dimensional feature vector, which is invariant to image translation, rotation, scaling variation, etc. Third, based on the manifold perception characteristic of HVS, isometric mapping, a manifold learning method that can reflect the intrinsic manifold embedded in the high-dimensional space, is employed to obtain a low-dimensional feature vector. Finally, a classical classification method, support vector machine, is utilized to realize fault diagnosis. Verification data were collected from Case Western Reserve University Bearing Data Center, and the experimental result indicates that the proposed fault diagnosis method based on visual cognition is highly effective for rolling bearings under variable conditions, thus providing a promising approach from the cognitive computing field.

  15. Compressive sensing-based feature extraction for bearing fault diagnosis using a heuristic neural network

    Science.gov (United States)

    Yuan, Haiying; Wang, Xiuyu; Sun, Xun; Ju, Zijian

    2017-06-01

    Bearing fault diagnosis collects massive amounts of vibration data about a rotating machinery system, whose fault classification largely depends on feature extraction. Features reflecting bearing work states are directly extracted using time-frequency analysis of vibration signals, which leads to high dimensional feature data. To address the problem of feature dimension reduction, a compressive sensing-based feature extraction algorithm is developed to construct a concise fault feature set. Next, a heuristic PSO-BP neural network, whose learning process perfectly combines particle swarm optimization and the Levenberg-Marquardt algorithm, is constructed for fault classification. Numerical simulation experiments are conducted on four datasets sampled under different severity levels and load conditions, which verify that the proposed fault diagnosis method achieves efficient feature extraction and high classification accuracy.

  16. Thruster fault diagnosis method based on Gaussian particle filter for autonomous underwater vehicles

    Directory of Open Access Journals (Sweden)

    Yu-shan Sun

    2016-05-01

    Full Text Available Autonomous Underwater Vehicles (AUVs generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

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

    Directory of Open Access Journals (Sweden)

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

    Science.gov (United States)

    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.

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

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  1. Power transformer fault diagnosis model based on rough set theory with fuzzy representation

    Institute of Scientific and Technical Information of China (English)

    Li Minghua; Dong Ming; Yan Zhang

    2007-01-01

    Objective Due to the incompleteness and complexity of fault diagnosis for power transformers, a comprehensive rough-fuzzy scheme for solving fault diagnosis problems is presented. Fuzzy set theory is used both for representation of incipient faults' indications and producing a fuzzy granulation of the feature space. Rough set theory is used to obtain dependency rules that model indicative regions in the granulated feature space. The fuzzy membership functions corresponding to the indicative regions, modelled by rules, are stored as cases. Results Diagnostic conclusions are made using a similarity measure based on these membership functions. Each case involves only a reduced number of relevant features making this scheme suitable for fault diagnosis. Conclusion Superiority of this method in terms of classification accuracy and case generation is demonstrated.

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

    KAUST Repository

    Busbait, Monther I.

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  5. Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

    Directory of Open Access Journals (Sweden)

    Jie Tao

    2016-01-01

    Full Text Available In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN. By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    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.

  8. Gearbox fault diagnosis based on time-frequency domain synchronous averaging and feature extraction technique

    Science.gov (United States)

    Zhang, Shengli; Tang, Jiong

    2016-04-01

    Gearbox is one of the most vulnerable subsystems in wind turbines. Its healthy status significantly affects the efficiency and function of the entire system. Vibration based fault diagnosis methods are prevalently applied nowadays. However, vibration signals are always contaminated by noise that comes from data acquisition errors, structure geometric errors, operation errors, etc. As a result, it is difficult to identify potential gear failures directly from vibration signals, especially for the early stage faults. This paper utilizes synchronous averaging technique in time-frequency domain to remove the non-synchronous noise and enhance the fault related time-frequency features. The enhanced time-frequency information is further employed in gear fault classification and identification through feature extraction algorithms including Kernel Principal Component Analysis (KPCA), Multilinear Principal Component Analysis (MPCA), and Locally Linear Embedding (LLE). Results show that the LLE approach is the most effective to classify and identify different gear faults.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    CERN Document Server

    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...

  11. Fault Diagnosis of Train Axle Box Bearing Based on Multifeature Parameters

    Directory of Open Access Journals (Sweden)

    Xiaofeng Li

    2015-01-01

    Full Text Available Failure of the train axle box bearing will cause great loss. Now, condition-based maintenance of train axle box bearing has been a research hotspot around the world. Vibration signals generated by train axle box bearing have nonlinear and nonstationary characteristics. The methods used in traditional bearing fault diagnosis do not work well with the train axle box. To solve this problem, an effective method of axle box bearing fault diagnosis based on multifeature parameters is presented in this paper. This method can be divided into three parts, namely, weak fault signal extraction, feature extraction, and fault recognition. In the first part, a db4 wavelet is employed for denoising the original signals from the vibration sensors. In the second part, five time-domain parameters, five IMF energy-torque features, and two amplitude-ratio features are extracted. The latter seven frequency domain features are calculated based on the empirical mode decomposition and envelope spectrum analysis. In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. A series of tests are carried out to verify the proposed method, which show that the accuracy is above 90%.

  12. Investigation of candidate data structures and search algorithms to support a knowledge based fault diagnosis system

    Science.gov (United States)

    Bosworth, Edward L., Jr.

    1987-01-01

    The focus of this research is the investigation of data structures and associated search algorithms for automated fault diagnosis of complex systems such as the Hubble Space Telescope. Such data structures and algorithms will form the basis of a more sophisticated Knowledge Based Fault Diagnosis System. As a part of the research, several prototypes were written in VAXLISP and implemented on one of the VAX-11/780's at the Marshall Space Flight Center. This report describes and gives the rationale for both the data structures and algorithms selected. A brief discussion of a user interface is also included.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  14. Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches

    KAUST Repository

    Harrou, Fouzi

    2017-09-18

    This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one-diode model and those of the univariate and multivariate exponentially weighted moving average (EWMA) charts to better detect faults. Specifically, we generate array\\'s residuals of current, voltage and power using measured temperature and irradiance. These residuals capture the difference between the measurements and the predictions MPP for the current, voltage and power from the one-diode model, and use them as fault indicators. Then, we apply the multivariate EWMA (MEWMA) monitoring chart to the residuals to detect faults. However, a MEWMA scheme cannot identify the type of fault. Once a fault is detected in MEWMA chart, the univariate EWMA chart based on current and voltage indicators is used to identify the type of fault (e.g., short-circuit, open-circuit and shading faults). We applied this strategy to real data from the grid-connected PV system installed at the Renewable Energy Development Center, Algeria. Results show the capacity of the proposed strategy to monitors the DC side of PV systems and detects partial shading.

  15. Distribution Network Fault Diagnosis Method Based on Granular Computing-BP

    Directory of Open Access Journals (Sweden)

    CHEN Zhong-xiao

    2013-01-01

    Full Text Available To deal with the complexity and uncertainty of distribution network fault information, a method of fault diagnosis based on granular computing and BP is proposed. This method uses attribute reduction advantages of granular computing theory and self-learning and knowledge acquisition ability of BP neural network. It put granular computing theory as the front-end processor of the BP neural network, namely simplify primitive information making use of granular computing reduction, and according to the concepts of relative granularity and significance of attributes based on binary granular computing are proposed to select input of BP, thereby reducing solving scale, and then construct neural network based on the minimum attribute sets, using BP neural network to model and parameter identify, reduce the BP study training time, improve the accuracy of the fault diagnosis. The distribution network example verifies the rationality and effectiveness of the proposed method.

  16. Method of gear fault diagnosis based on EEMD and improved Elman neural network

    Science.gov (United States)

    Zhang, Qi; Zhao, Wei; Xiao, Shungen; Song, Mengmeng

    2017-05-01

    Aiming at crack and wear and so on of gears Fault information is difficult to diagnose usually due to its weak, a gear fault diagnosis method that is based on EEMD and improved Elman neural network fusion is proposed. A number of IMF components are obtained by decomposing denoised all kinds of fault signals with EEMD, and the pseudo IMF components is eliminated by using the correlation coefficient method to obtain the effective IMF component. The energy characteristic value of each effective component is calculated as the input feature quantity of Elman neural network, and the improved Elman neural network is based on standard network by adding a feedback factor. The fault data of normal gear, broken teeth, cracked gear and attrited gear were collected by field collecting. The results were analyzed by the diagnostic method proposed in this paper. The results show that compared with the standard Elman neural network, Improved Elman neural network has the advantages of high diagnostic efficiency.

  17. Support vector machine based on chaos particle swarm optimization for fault diagnosis of rotating machine

    Institute of Scientific and Technical Information of China (English)

    TANG Xian-lun; ZHUANG Ling; QIU Guo-qing; CAI Jun

    2009-01-01

    The performance of the support vector machine models depends on a proper setting of its parameters to a great extent. A novel method of searching the optimal parameters of support vector machine based on chaos particle swarm optimization is proposed. A multi-fault classification model based on SVM optimized by chaos particle swarm optimization is established and applied to the fault diagnosis of rotating machines. The results show that the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, and the precision and reliability of the fault classification results can meet the requirement of practical application. It indicates that chaos particle swarm optimization is a suitable method for searching the optimal parameters of support vector machine.

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

    DEFF Research Database (Denmark)

    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...

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

    Institute of Scientific and Technical Information of China (English)

    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.

  20. Chaos Synchronization Based Novel Real-Time Intelligent Fault Diagnosis for Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Chin-Tsung Hsieh

    2014-01-01

    Full Text Available The traditional solar photovoltaic fault diagnosis system needs two to three sets of sensing elements to capture fault signals as fault features and many fault diagnosis methods cannot be applied with real time. The fault diagnosis method proposed in this study needs only one set of sensing elements to intercept the fault features of the system, which can be real-time-diagnosed by creating the fault data of only one set of sensors. The aforesaid two points reduce the cost and fault diagnosis time. It can improve the construction of the huge database. This study used Matlab to simulate the faults in the solar photovoltaic system. The maximum power point tracker (MPPT is used to keep a stable power supply to the system when the system has faults. The characteristic signal of system fault voltage is captured and recorded, and the dynamic error of the fault voltage signal is extracted by chaos synchronization. Then, the extension engineering is used to implement the fault diagnosis. Finally, the overall fault diagnosis system only needs to capture the voltage signal of the solar photovoltaic system, and the fault type can be diagnosed instantly.

  1. Fault diagnosis for tilting-pad journal bearing based on SVD and LMD

    Directory of Open Access Journals (Sweden)

    Zhang Xiaotao

    2016-01-01

    Full Text Available Aiming at fault diagnosis for tilting-pad journal bearing with fluid support developed recently, a new method based on singular value decomposition (SVD and local mean decomposition (LMD is proposed. First, the phase space reconstruction of Hankel matrix and SVD method are used as pre-filter process unit to reduce the random noises in the original signal. Then the purified signal is decomposed by LMD into a series of production functions (PFs. Based on PFs, time frequency map and marginal spectrum can be obtained for fault diagnosis. Finally, this method is applied to numerical simulation and practical experiment data. The results show that the proposed method can effectively detect fault features of tilting-pad journal bearing.

  2. Sensor fault diagnosis of nonlinear processes based on structured kernel principal component analysis

    Institute of Scientific and Technical Information of China (English)

    Kechang FU; Liankui DAI; Tiejun WU; Ming ZHU

    2009-01-01

    A new sensor fault diagnosis method based on structured kernel principal component analysis (KPCA) is proposed for nonlinear processes.By performing KPCA on subsets of variables,a set of structured residuals,i.e.,scaled powers of KPCA,can be obtained in the same way as partial PCA.The structured residuals are utilized in composing an isolation scheme for sensor fault diagnosis,according to a properly designed incidence matrix.Sensor fault sensitivity and critical sensitivity are defined,based on which an incidence matrix optimization algorithm is proposed to improve the performance of the structured KPCA.The effectiveness of the proposed method is demonstrated on the simulated continuous stirred tank reactor (CSTR) process.

  3. Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm

    Science.gov (United States)

    Yuan, Shengfa; Chu, Fulei

    2007-04-01

    Support vector machines (SVM) is a new general machine-learning tool based on the structural risk minimisation principle that exhibits good generalisation when fault samples are few, it is especially fit for classification, forecasting and estimation in small-sample cases such as fault diagnosis, but some parameters in SVM are selected by man's experience, this has hampered its efficiency in practical application. Artificial immunisation algorithm (AIA) is used to optimise the parameters in SVM in this paper. The AIA is a new optimisation method based on the biologic immune principle of human being and other living beings. It can effectively avoid the premature convergence and guarantees the variety of solution. With the parameters optimised by AIA, the total capability of the SVM classifier is improved. The fault diagnosis of turbo pump rotor shows that the SVM optimised by AIA can give higher recognition accuracy than the normal SVM.

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

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Science.gov (United States)

    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.

  6. Rolling element bearing faults diagnosis based on kurtogram and frequency domain correlated kurtosis

    Science.gov (United States)

    Gu, Xiaohui; Yang, Shaopu; Liu, Yongqiang; Hao, Rujiang

    2016-12-01

    Envelope analysis is one of the most useful methods in localized fault diagnosis of rolling element bearings. However, there is a challenge in selecting the optimal resonance band. In this paper, a novel method based on kurtogram and frequency domain correlated kurtosis is proposed. To obtain the correct relationship between the node and frequency band in wavelet packet transform, a vital process named frequency ordering is conducted to solve the frequency folding problem due to down sampling. Correlated kurtosis of envelope spectrum instead of correlated kurtosis of envelope signal or kurtosis of envelope spectrum is utilized to generate the kurtogram, in which the maximum value can indicate the optimal band for envelope analysis. Several cases of experimental bearing fault signals are used to evaluate the immunity of the proposed method to strong noise interference. The improved performance has also been compared with two previous developed methods. The results demonstrate the effectiveness and robustness of the method in fault diagnosis of rolling element bearings.

  7. Fault Diagnosis in Transformer Based on Weighted Degree of Grey Slope Incidence of Optimized Entropy

    Directory of Open Access Journals (Sweden)

    Zhang Anping

    2016-01-01

    Full Text Available Dissolved gas analysis (DGA is an important method to find the hidden or incipient insulation faults of oil-immersed power transformer. However, code deficiency exists in the gas ratio methods specified by the IEC standard and complexity of fault diagnosis for power transformer. Hence a new model based on optimized weighted degree of grey slope incidence was put forward. Firstly, the entropy weight is used to determine objective weight of indices; then the model fault types are obtained by weighted degree of grey slope incidence. The combination of entropy weight with grey slope incidence analysis can fully utilize over all information of DGA and give full play to the superiority of grey slope incidence, which overcomes shortcomings of original grey slope incidence analysis. The experimental results also demonstrate that the improved method has higher accuracy compared with three-ratio method and general grey slope incidence analysis method. The diagnosis accuracy is 92.8%.

  8. Diagnosis and fault-tolerant control

    CERN Document Server

    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...

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

    Science.gov (United States)

    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.

  10. A fault diagnosis based reconfigurable longitudinal control system for managing loss of air data sensors for a civil aircraft

    OpenAIRE

    Varga, Andreas; Ossmann, Daniel; Joos, Hans-Dieter

    2014-01-01

    An integrated fault diagnosis based fault tolerant longitudinal control system architecture is proposed for civil aircraft which can accommodate partial or total losses of angle of attack and/or calibrated airspeed sensors. A triplex sensor redundancy is assumed for the normal operation of the aircraft using a gain scheduled longitudinal normal control law. The fault isolation functionality is provided by a bank of 6 fault detection filters, which individually monitor each of the 6 sensors us...

  11. A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine

    Directory of Open Access Journals (Sweden)

    Xiao-hui He

    2016-01-01

    Full Text Available To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM. Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form representation of the distribution underlying the training data, and it is very convenient for modeling high-dimensional real-valued data. Experiments on 10 different data sets verify the performance of the proposed method. The superiority of Gaussian RBM classifier is also confirmed by comparing with other classifiers, such as extreme learning machine, support vector machine, and deep belief network. The robustness of the proposed method is also studied in this paper. It can be concluded that the proposed method can realize the bearing fault diagnosis accurately and effectively.

  12. Active fault diagnosis by controller modification

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    2010-01-01

    Two active fault diagnosis methods for additive or parametric faults are proposed. Both methods are based on controller reconfiguration rather than on requiring an exogenous excitation signal, as it is otherwise common in active fault diagnosis. For the first method, it is assumed that the system...... in a way that guarantees the continuity of transition and global stability using a recent result on observer parameterization. An illustrative example inspired by a field study of a drag racing vehicle is given. For the second method, an active fault diagnosis method for parametric faults is proposed...

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

    Directory of Open Access Journals (Sweden)

    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.

  14. Forward and backward models for fault diagnosis based on parallel genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Yi LIU; Ying LI; Yi-jia CAO; Chuang-xin GUO

    2008-01-01

    In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.

  15. Diagnosis method based on wavelet coefficient scale relativity correlation dimension for fault

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Correlation dimension as a tool to describe machinery condition is introduced.Vibration signals of the fan under different working conditions are analyzed using a threshold filtering algorithm based on the region relativity of the wavelet coefficients for reducing noise.The result shows that the characteristics of the signal could be preserved completely.The correlation dimension is able to identify conditions of the fan with faults compared with the normal condition,thereby providing an effective technology for condition monitoring and fault diagnosis of mechanical equipment.

  16. Data-based hybrid tension estimation and fault diagnosis of cold rolling continuous annealing processes.

    Science.gov (United States)

    Liu, Qiang; Chai, Tianyou; Wang, Hong; Qin, Si-Zhao Joe

    2011-12-01

    The continuous annealing process line (CAPL) of cold rolling is an important unit to improve the mechanical properties of steel strips in steel making. In continuous annealing processes, strip tension is an important factor, which indicates whether the line operates steadily. Abnormal tension profile distribution along the production line can lead to strip break and roll slippage. Therefore, it is essential to estimate the whole tension profile in order to prevent the occurrence of faults. However, in real annealing processes, only a limited number of strip tension sensors are installed along the machine direction. Since the effects of strip temperature, gas flow, bearing friction, strip inertia, and roll eccentricity can lead to nonlinear tension dynamics, it is difficult to apply the first-principles induced model to estimate the tension profile distribution. In this paper, a novel data-based hybrid tension estimation and fault diagnosis method is proposed to estimate the unmeasured tension between two neighboring rolls. The main model is established by an observer-based method using a limited number of measured tensions, speeds, and currents of each roll, where the tension error compensation model is designed by applying neural networks principal component regression. The corresponding tension fault diagnosis method is designed using the estimated tensions. Finally, the proposed tension estimation and fault diagnosis method was applied to a real CAPL in a steel-making company, demonstrating the effectiveness of the proposed method.

  17. FAULT DIAGNOSIS APPROACH BASED ON HIDDEN MARKOV MODEL AND SUPPORT VECTOR MACHINE

    Institute of Scientific and Technical Information of China (English)

    LIU Guanjun; LIU Xinmin; QIU Jing; HU Niaoqing

    2007-01-01

    Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model (HMM) and support vector machine (SVM). HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox's faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.

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

    Science.gov (United States)

    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.

  19. A Novel Approach for Multi Class Fault Diagnosis in Induction Machine Based on Statistical Time Features and Random Forest Classifier

    Science.gov (United States)

    Sonje, M. Deepak; Kundu, P.; Chowdhury, A.

    2017-08-01

    Fault diagnosis and detection is the important area in health monitoring of electrical machines. This paper proposes the recently developed machine learning classifier for multi class fault diagnosis in induction machine. The classification is based on random forest (RF) algorithm. Initially, stator currents are acquired from the induction machine under various conditions. After preprocessing the currents, fourteen statistical time features are estimated for each phase of the current. These parameters are considered as inputs to the classifier. The main scope of the paper is to evaluate effectiveness of RF classifier for individual and mixed fault diagnosis in induction machine. The stator, rotor and mixed faults (stator and rotor faults) are classified using the proposed classifier. The obtained performance measures are compared with the multilayer perceptron neural network (MLPNN) classifier. The results show the much better performance measures and more accurate than MLPNN classifier. For demonstration of planned fault diagnosis algorithm, experimentally obtained results are considered to build the classifier more practical.

  20. Transformer fault diagnosis based on chemical reaction optimization algorithm and relevance vector machine

    Directory of Open Access Journals (Sweden)

    Luo Wei

    2017-01-01

    Full Text Available Power transformer is one of the most important equipment in power system. In order to predict the potential fault of power transformer and identify the fault types correctly, we proposed a transformer fault intelligent diagnosis model based on chemical reaction optimization (CRO algorithm and relevance vector machine(RVM. RVM is a powerful machine learning method, which can solve nonlinear, high-dimensional classification problems with a limited number of samples. CRO algorithm has well global optimization and simple calculation, so it is suitable to solve parameter optimization problems. In this paper, firstly, a multi-layer RVM classification model was built by binary tree recognition strategy. Secondly, CRO algorithm was adopted to optimize the kernel function parameters which could enhance the performance of RVM classifiers. Compared with IEC three-ratio method and the RVM model, the CRO-RVM model not only overcomes the coding defect problem of IEC three-ratio method, but also has higher classification accuracy than the RVM model. Finally, the new method was applied to analyze a transformer fault case, Its predicted result accord well with the real situation. The research provides a practical method for transformer fault intelligent diagnosis and prediction.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    KAUST Repository

    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.

  3. Diagnosis of stator faults in induction motor based on zero sequence voltage after switch-off

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    To improve the accuracy of the stator winding fault diagnosis in induction motor, a new diagnostic method based on the Hilbert-Huang transform (HHT) was proposed. The ratio of fundamental zero sequence voltage to positive sequence voltage after switch-offwas selected as the stator fault characteristic, which could effectively avoid the influence of the supply unbalance and the load fluctuation, and directly represent the asymmetry in the stator. Using the empirical mode decomposition (EMD) based on HHT, the zero sequence voltage after switch-off was decomposed and the fundamental component was extracted. Then, the fault characteristic can be acquired. Experimental results on a 4-kW induction motor demonstrate the feasibility and effectiveness of this method.

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

    Directory of Open Access Journals (Sweden)

    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.

  5. Induction motor rotor fault diagnosis method based on double PQ transformation

    Institute of Scientific and Technical Information of China (English)

    HUANG Jin; NIU Faliang; YANG Jiaqiang

    2007-01-01

    This Paper presents a new rotor fault diagnosis method for induction motors which is based on the double PQ transformation.We construct the PQ transformation matrix with the positive sequence fundamental voltage components and their Hilbert transformation as elements.The active power P and the reactive power Q are obtained through the PO transformation of the stator currents.As both P and Q are constant for a healthy motor,they are represented by a dot on the PQ plane.Whereas the P and Q for a rotor broken bar motor are represented by an ellipse because they comprise an additional frequency component 2sfs (s is the slip and js is the supply frequency).Thus,by distinguishing these two different patterns.the rotor broken bar fault is detected.We use the major radius of the ellipse as the fault indicator and the distance between the point of no-load condition and the center of the ellipse on the PQ plane as its normalization value.We thus arrive at the fault severity factor which is fairly independent of the load level and the inertia value of the induction motors.Experimental results have demonstrated that the proposed method is effective in identifying the rotor-broken-bars fault and at determining the severity of the fault.

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

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  8. Mechanical Fault Diagnosis of High Voltage Circuit Breakers with Unknown Fault Type Using Hybrid Classifier Based on LMD and Time Segmentation Energy Entropy

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-09-01

    Full Text Available In order to improve the identification accuracy of the high voltage circuit breakers’ (HVCBs mechanical fault types without training samples, a novel mechanical fault diagnosis method of HVCBs using a hybrid classifier constructed with Support Vector Data Description (SVDD and fuzzy c-means (FCM clustering method based on Local Mean Decomposition (LMD and time segmentation energy entropy (TSEE is proposed. Firstly, LMD is used to decompose nonlinear and non-stationary vibration signals of HVCBs into a series of product functions (PFs. Secondly, TSEE is chosen as feature vectors with the superiority of energy entropy and characteristics of time-delay faults of HVCBs. Then, SVDD trained with normal samples is applied to judge mechanical faults of HVCBs. If the mechanical fault is confirmed, the new fault sample and all known fault samples are clustered by FCM with the cluster number of known fault types. Finally, another SVDD trained by the specific fault samples is used to judge whether the fault sample belongs to an unknown type or not. The results of experiments carried on a real SF6 HVCB validate that the proposed fault-detection method is effective for the known faults with training samples and unknown faults without training samples.

  9. A new rolling bearing fault diagnosis method based on GFT impulse component extraction

    Science.gov (United States)

    Ou, Lu; Yu, Dejie; Yang, Hanjian

    2016-12-01

    Periodic impulses are vital indicators of rolling bearing faults. The extraction of impulse components from rolling bearing vibration signals is of great importance for fault diagnosis. In this paper, vibration signals are taken as the path graph signals in a manifold perspective, and the Graph Fourier Transform (GFT) of vibration signals are investigated from the graph spectrum domain, which are both introduced into the vibration signal analysis. To extract the impulse components efficiently, a new adjacency weight matrix is defined, and then the GFT of the impulse component and harmonic component in the rolling bearing vibration signals are analyzed. Furthermore, as the GFT graph spectrum of the impulse component is mainly concentrated in the high-order region, a new rolling bearing fault diagnosis method based on GFT impulse component extraction is proposed. In the proposed method, the GFT of a vibration signal is firstly performed, and its graph spectrum coefficients in the high-order region are extracted to reconstruct different impulse components. Next, the Hilbert envelope spectra of these impulse components are calculated, and the envelope spectrum values at the fault characteristic frequency are arranged in order. Furthermore, the envelope spectrum with the maximum value at the fault characteristic frequency is selected as the final result, from which the rolling bearing fault can be diagnosed. Finally, an index KR, which is the product of the kurtosis and Hilbert envelope spectrum fault feature ratio of the extracted impulse component, is put forward to measure the performance of the proposed method. Simulations and experiments are utilized to demonstrate the feasibility and effectiveness of the proposed method.

  10. Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-11-01

    Full Text Available Mechanical fault diagnosis of high-voltage circuit breakers (HVCBs based on vibration signal analysis is one of the most significant issues in improving the reliability and reducing the outage cost for power systems. The limitation of training samples and types of machine faults in HVCBs causes the existing mechanical fault diagnostic methods to recognize new types of machine faults easily without training samples as either a normal condition or a wrong fault type. A new mechanical fault diagnosis method for HVCBs based on variational mode decomposition (VMD and multi-layer classifier (MLC is proposed to improve the accuracy of fault diagnosis. First, HVCB vibration signals during operation are measured using an acceleration sensor. Second, a VMD algorithm is used to decompose the vibration signals into several intrinsic mode functions (IMFs. The IMF matrix is divided into submatrices to compute the local singular values (LSV. The maximum singular values of each submatrix are selected as the feature vectors for fault diagnosis. Finally, a MLC composed of two one-class support vector machines (OCSVMs and a support vector machine (SVM is constructed to identify the fault type. Two layers of independent OCSVM are adopted to distinguish normal or fault conditions with known or unknown fault types, respectively. On this basis, SVM recognizes the specific fault type. Real diagnostic experiments are conducted with a real SF6 HVCB with normal and fault states. Three different faults (i.e., jam fault of the iron core, looseness of the base screw, and poor lubrication of the connecting lever are simulated in a field experiment on a real HVCB to test the feasibility of the proposed method. Results show that the classification accuracy of the new method is superior to other traditional methods.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  12. Online Fault Diagnosis for Biochemical Process Based on FCM and SVM.

    Science.gov (United States)

    Wang, Xianfang; Du, Haoze; Tan, Jinglu

    2016-12-01

    Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis. In this study, a glutamic acid fermentation process is chosen as an example to diagnose the fault by this method, the result shows that the diagnosis time is largely shortened, and the accuracy is extremely improved by comparing to a single SVM method.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    Wang, Huaqing; Chen, Peng

    2009-01-01

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

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

    Directory of Open Access Journals (Sweden)

    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.

  16. Model-Based Fault Diagnosis Techniques Design Schemes, Algorithms and Tools

    CERN Document Server

    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...

  17. Fault Diagnosis Method Based on Fractal Theory and Its Application in Wind Power Systems

    Institute of Scientific and Technical Information of China (English)

    赵玲; 黄大荣; 宋军

    2012-01-01

    The non-linear dynamic theory brought a new method for recognizing and predicting complex non-linear dynamic behaviors. The non-linear behavior of vibration signals can be described by using fractal dimension quantitatively. In this paper, a fractal dimension calculation method for discrete signals in the fractal theory was applied to extract the fractal di- mension feature vectors and classified various fault types. Based on the wavelet packet transform, the energy feature vectors were extracted after the vibration signal was decomposed and reconstructed. Then, a wavelet neural network was used to recognize the mechanical faults. Finally, the fault diagnosis for a wind power system was taken as an example to show the method' s feasibility.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  19. Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis

    Science.gov (United States)

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

    2016-12-01

    The bearing failure, generating harmful vibrations, is one of the most frequent reasons for machine breakdowns. Thus, performing bearing fault diagnosis is an essential procedure to improve the reliability of the mechanical system and reduce its operating expenses. Most of the previous studies focused on rolling bearing fault diagnosis could be categorized into two main families, kurtosis-based filter method and wavelet-based shrinkage method. Although tremendous progresses have been made, their effectiveness suffers from three potential drawbacks: firstly, fault information is often decomposed into proximal frequency bands and results in impulsive feature frequency band splitting (IFFBS) phenomenon, which significantly degrades the performance of capturing the optimal information band; secondly, noise energy spreads throughout all frequency bins and contaminates fault information in the information band, especially under the heavy noisy circumstance; thirdly, wavelet coefficients are shrunk equally to satisfy the sparsity constraints and most of the feature information energy are thus eliminated unreasonably. Therefore, exploiting two pieces of prior information (i.e., one is that the coefficient sequences of fault information in the wavelet basis is sparse, and the other is that the kurtosis of the envelope spectrum could evaluate accurately the information capacity of rolling bearing faults), a novel weighted sparse model and its corresponding framework for bearing fault diagnosis is proposed in this paper, coined KurWSD. KurWSD formulates the prior information into weighted sparse regularization terms and then obtains a nonsmooth convex optimization problem. The alternating direction method of multipliers (ADMM) is sequentially employed to solve this problem and the fault information is extracted through the estimated wavelet coefficients. Compared with state-of-the-art methods, KurWSD overcomes the three drawbacks and utilizes the advantages of both family

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

    Institute of Scientific and Technical Information of China (English)

    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.

  1. Fault Diagnosis and Prognosis Based on Lebesgue Sampling

    Science.gov (United States)

    2014-10-02

    has been physically reached and is com- pared with the RUL estimation from prognosis. Traditional ways to design FDP algorithms adopt periodic sampling...also called “ Riemann sampling (RS)”) where sam- ples are taken in a periodic manner and the diagnostic and prognostic algorithms are executed at the...executed on an “as-needed” basis and is promising in reducing the computational cost compared with the traditional Riemann sampling-based FDP (RS-FDP

  2. Auditory-model-based Feature Extraction Method for Mechanical Faults Diagnosis

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  4. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    Science.gov (United States)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  5. Robust fault diagnosis for non-Gaussian stochastic systems based on the rational square-root approximation model

    Institute of Scientific and Technical Information of China (English)

    YAO LiNa; WANG Hong

    2008-01-01

    The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the ra-tional square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Conver-gency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demon-strate the effectiveness of the proposed algorithm.

  6. A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhongliang Lv

    2016-01-01

    Full Text Available A novel fault diagnosis method based on variational mode decomposition (VMD and multikernel support vector machine (MKSVM optimized by Immune Genetic Algorithm (IGA is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.

  7. Fault Diagnosis Approach of Local Ventilation System in Coal Mines Based on Multidisciplinary Technology

    Institute of Scientific and Technical Information of China (English)

    GONG Xiao-yan; XUE He; TAO Xin-li; HU Ning

    2006-01-01

    In order to reduce the probability of fault occurrence of local ventilation system in coal mine and prevent gas from exceeding the standard limit, an approach incorporating the reliability analysis, rough set theory, genetic algorithm (GA), and intelligent decision support system (IDSS) was used to establish and develop a fault diagnosis system of local ventilation in coal mine. Fault tree model was established and its reliability analysis was performed. The algorithms and software of key fault symptom and fault diagnosis rule acquiring were also analyzed and developed. Finally, a prototype system was developed and demonstrated by a mine instance. The research results indicate that the proposed approach in this paper can accurately and quickly find the fault reason in a local ventilation system of coal mines and can reduce difficulty of the fault diagnosis of the local ventilation system, which is significant to decrease gas exploding accidents in coal mines.

  8. Iterative learning based fault diagnosis for discrete linear uncer tain systems

    Institute of Scientific and Technical Information of China (English)

    Wei Cao; Ming Sun

    2014-01-01

    In order to detect and estimate faults in discrete lin-ear time-varying uncertain systems, the discrete iterative learning strategy is applied in fault diagnosis, and a novel fault detection and estimation algorithm is proposed. And the threshold limited tech-nology is adopted in the proposed algorithm. Within the chosen optimal time region, residual signals are used in the proposed algo-rithm to correct the introduced virtual faults with iterative learning rules, making the virtual faults close to these occurred in practical systems. And the same method is repeated in the rest optimal time regions, thereby reaching the aim of fault diagnosis. The proposed algorithm not only completes fault detection and estimation for dis-crete linear time-varying uncertain systems, but also improves the reliability of fault detection and decreases the false alarm rate. The final simulation results verify the validity of the proposed algorithm.

  9. Diagnosis Method for Analog Circuit Hard fault and Soft Fault

    Directory of Open Access Journals (Sweden)

    Baoru Han

    2013-09-01

    Full Text Available Because the traditional BP neural network slow convergence speed, easily falling in local minimum and the learning process will appear oscillation phenomena. This paper introduces a tolerance analog circuit hard fault and soft fault diagnosis method based on adaptive learning rate and the additional momentum algorithm BP neural network. Firstly, tolerance analog circuit is simulated by OrCAD / Pspice circuit simulation software, accurately extracts fault waveform data by matlab program automatically. Secondly, using the adaptive learning rate and momentum BP algorithm to train neural network, and then applies it to analog circuit hard fault and soft fault diagnosis. With shorter training time, high precision and global convergence effectively reduces the misjudgment, missing, it can improve the accuracy of fault diagnosis and fast.  

  10. Diagnosis and Fault-tolerant Control

    DEFF Research Database (Denmark)

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

    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...... the 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...

  11. Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis

    Science.gov (United States)

    Liu, Hongmei; Wang, Xuan; Lu, Chen

    2015-08-01

    A rolling bearing vibration signal is nonlinear and non-stationary and has multiple components and multifractal properties. A rolling-bearing fault-diagnosis method based on Local Characteristic-scale Decomposition-Teager Energy Operator (LCD-TEO) and multifractal detrended fluctuation analysis (MF-DFA) is first proposed in this paper. First, the vibration signal was decomposed into several intrinsic scale components (ISCs) by using LCD, which is a newly developed signal decomposition method. Second, the instantaneous amplitude was obtained by applying the TEO to each major ISC for demodulation. Third, the intrinsic multifractality features hidden in each major ISC were extracted by using MF-DFA, among which the generalized Hurst exponents are selected as the multifractal feature in this paper. Finally, the feature vectors were obtained by applying principal components analysis (PCA) to the extracted multifractality features, thus reducing the dimension of the multifractal features and obtaining the fault feature insensitive to variation in working conditions, further enhancing the accuracy of diagnosis. According to the extracted feature vector, rolling bearing faults can be diagnosed under variable working conditions. The experimental results demonstrate its desirable diagnostic performance under both different working conditions and different fault severities. Simultaneously, the results of comparison show that the performance of the proposed diagnostic method outperforms that of Hilbert-Huang transform (HHT) combined with MF-DFA or LCD-TEO combined with mono-fractal analysis.

  12. A computer-vision-based rotating speed estimation method for motor bearing fault diagnosis

    Science.gov (United States)

    Wang, Xiaoxian; Guo, Jie; Lu, Siliang; Shen, Changqing; He, Qingbo

    2017-06-01

    Diagnosis of motor bearing faults under variable speed is a problem. In this study, a new computer-vision-based order tracking method is proposed to address this problem. First, a video recorded by a high-speed camera is analyzed with the speeded-up robust feature extraction and matching algorithm to obtain the instantaneous rotating speed (IRS) of the motor. Subsequently, an audio signal recorded by a microphone is equi-angle resampled for order tracking in accordance with the IRS curve, through which the frequency-domain signal is transferred to an angular-domain one. The envelope order spectrum is then calculated to determine the fault characteristic order, and finally the bearing fault pattern is determined. The effectiveness and robustness of the proposed method are verified with two brushless direct-current motor test rigs, in which two defective bearings and a healthy bearing are tested separately. This study provides a new noninvasive measurement approach that simultaneously avoids the installation of a tachometer and overcomes the disadvantages of tacholess order tracking methods for motor bearing fault diagnosis under variable speed.

  13. Study on Immune Relevant Vector Machine Based Intelligent Fault Detection and Diagnosis Algorithm

    Directory of Open Access Journals (Sweden)

    Zhong-hua Miao

    2013-01-01

    Full Text Available An immune relevant vector machine (IRVM based intelligent classification method is proposed by combining the random real-valued negative selection (RRNS algorithm and the relevant vector machine (RVM algorithm. The method proposed is aimed to handle the training problem of missing or incomplete fault sampling data and is inspired by the “self/nonself” recognition principle in the artificial immune systems. The detectors, generated by the RRNS, are treated as the “nonself” training samples and used to train the RVM model together with the “self” training samples. After the training succeeds, the “nonself” detection model, which requires only the “self” training samples, is obtained for the fault detection and diagnosis. It provides a general way solving the problems of this type and can be applied for both fault detection and fault diagnosis. The standard Fisher's Iris flower dataset is used to experimentally testify the proposed method, and the results are compared with those from the support vector data description (SVDD method. Experimental results have shown the validity and practicability of the proposed method.

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

    Directory of Open Access Journals (Sweden)

    Zeguang YI

    2015-08-01

    Full Text Available 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 used to construct improved multi-scale morphological filters which is applicable to mechanical failure in order to weaken the background noises; combining the structural parameters of parts to build a reference signal, complex components blind separation is carried out on the signals after noise reduction paragraph by paragraph by reference signal unit semi-blind extraction algorithm; then the improved KL-distance of complex independent components is employed as distance measure to resolve the permutation, and finally the mechanical fault characteristic signals are extracted and separated. The actual acoustic diagnosis of rolling bearing fault in sound field environment results proves the effectiveness of this algorithm.

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

    Science.gov (United States)

    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.

  16. Fault Diagnosis for Manifold Absolute Pressure Sensor(MAP) of Diesel Engine Based on Elman Neural Network Observer

    Institute of Scientific and Technical Information of China (English)

    WANG Yingmin; ZHANG Fujun; CUI Tao; ZHOU Jinlong

    2016-01-01

    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.

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

    Science.gov (United States)

    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.

  18. Diagnosis and Fault-tolerant Control

    DEFF Research Database (Denmark)

    Blanke, Mogens; Kinnaert, Michel; Lunze, Jan

    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. 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. Four case studies on pilot processes show......-tolerant control....

  19. A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

    Directory of Open Access Journals (Sweden)

    HungLinh Ao

    2014-01-01

    Full Text Available This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs. Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

  20. Neural Network Based Fault Detection and Diagnosis System for Three-Phase Inverter in Variable Speed Drive with Induction Motor

    Directory of Open Access Journals (Sweden)

    Furqan Asghar

    2016-01-01

    Full Text Available Recently, electrical drives generally associate inverter and induction machine. Therefore, inverter must be taken into consideration along with induction motor in order to provide a relevant and efficient diagnosis of these systems. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduces overall efficiency. In this paper, fault detection and diagnosis based on features extraction and neural network technique for three-phase inverter is presented. Basic purpose of this fault detection and diagnosis system is to detect single or multiple faults efficiently. Several features are extracted from the Clarke transformed output current and used in neural network as input for fault detection and diagnosis. Hence, some simulation study as well as hardware implementation and experimentation is carried out to verify the feasibility of the proposed scheme. Results show that the designed system not only detects faults easily, but also can effectively differentiate between multiple faults. These results prove the credibility and show the satisfactory performance of designed system. Results prove the supremacy of designed system over previous feature extraction fault systems as it can detect and diagnose faults in a single cycle as compared to previous multicycles detection with high accuracy.

  1. Active fault diagnosis in closed-loop uncertain systems

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2006-01-01

    Fault diagnosis of parametric faults in closed-loop uncertain systems by using an auxiliary input vector is considered in this paper, i.e. active fault diagnosis (AFD). The active fault diagnosis is based directly on the socalled fault signature matrix, related to the YJBK (Youla, Jabr, Bongiorno...... and Kucera) parameterization. Conditions are given for exact detection and isolation of parametric faults in closed-loop uncertain systems....

  2. Aero-Engine Fault Diagnosis Using Improved Local Discriminant Bases and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Jianwei Cui

    2014-01-01

    Full Text Available This paper presents an effective approach for aero-engine fault diagnosis with focus on rub-impact, through combination of improved local discriminant bases (LDB with support vector machine (SVM. The improved LDB algorithm, using both the normalized energy difference and the relative entropy as quantification measures, is applied to choose the optimal set of orthogonal subspaces for wavelet packet transform- (WPT- based signal decomposition. Then two optimal sets of orthogonal subspaces have been obtained and the energy features extracted from those subspaces appearing in both sets will be selected as input to a SVM classifier to diagnose aero-engine faults. Experiment studies conducted on an aero-engine rub-impact test system have verified the effectiveness of the proposed approach for classifying working conditions of aero-engines.

  3. FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    LI Ru-qiang; CHEN Jin; WU Xing

    2006-01-01

    A novel knowledge-based fuzzy neural network (KBFNN) for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery.Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

  4. Sensor fault diagnosis of time-delay systems based on adaptive observer

    Institute of Scientific and Technical Information of China (English)

    YOU Fu-qiang; TIAN Zuo-hua; SHI Song-jiao

    2006-01-01

    Presents a novel approach for the sensor fault diagnosis of time-delay systems by using an adaptive observer technique. The sensor fault is modeled as an additive perturbation described by a time varying function. 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 stability of fault diagnosis system is proved. Finally, a numerical example is given to illustrate the efficiency of the proposed method.

  5. Gearbox Fault Diagnosis in a Wind Turbine Using Single Sensor Based Blind Source Separation

    Directory of Open Access Journals (Sweden)

    Yuning Qian

    2016-01-01

    Full Text Available This paper presents a single sensor based blind source separation approach, namely, the wavelet-assisted stationary subspace analysis (WSSA, for gearbox fault diagnosis in a wind turbine. Continuous wavelet transform (CWT is used as a preprocessing tool to decompose a single sensor measurement data into a set of wavelet coefficients to meet the multidimensional requirement of the stationary subspace analysis (SSA. The SSA is a blind source separation technique that can separate the multidimensional signals into stationary and nonstationary source components without the need for independency and prior information of the source signals. After that, the separated nonstationary source component with the maximum kurtosis value is analyzed by the enveloping spectral analysis to identify potential fault-related characteristic frequencies. Case studies performed on a wind turbine gearbox test system verify the effectiveness of the WSSA approach and indicate that it outperforms independent component analysis (ICA and empirical mode decomposition (EMD, as well as the spectral-kurtosis-based enveloping, for wind turbine gearbox fault diagnosis.

  6. Static-deformation based fault diagnosis for damping spring of large vibrating screen

    Institute of Scientific and Technical Information of China (English)

    彭利平; 刘初升; 李珺; 王宏

    2014-01-01

    Based on the statics theory, a novel and feasible twice-suspended-mass method (TSMM) was proposed to deal with the seldom-studied issue of fault diagnosis for damping springs of large vibrating screen (LVS). With the static balance characteristic of the screen body/surface as well as the deformation compatibility relation of springs considered, static model of the screen surface under a certain load was established to calculate compression deformation of each spring. Accuracy of the model was validated by both an experiment based on the suspended mass method and the properties of the 3D deformation space in a numerical simulation. Furthermore, by adopting the Taylor formula and the control variate method, quantitative relationship between the change of damping spring deformation and the change of spring stiffness, defined as the deformation sensitive coefficient (DSC), was derived mathematically, from which principle of the TSMM for spring fault diagnosis is clarified. In the end, an experiment was carried out and results show that the TSMM is applicable for diagnosing the fault of single spring in a LVS.

  7. Wear Fault Diagnosis of Machinery Based on Neural Networks and Gray Relationships

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    In this paper, the regular characteristic of wear particles related to fault type of machines based on condition monitoring of reciprocal machinery is discussed. The typi-cal wear particles spectrum is established according to the equipment structure, friction and wear rule and the characteristic of wear particles; The identification technology of wear particles is proposed based on neural networks and a gray relationship; an intelligent wear particles identification system is designed. The diagnosis example shows that this system can promote the accuracy and the speed of wear particles identification.

  8. Application of Analytic Redundancy-based Fault Diagnosis of Sensors to Onboard Maintenance System

    Institute of Scientific and Technical Information of China (English)

    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.

  9. Vibration sensor-based bearing fault diagnosis using ellipsoid-ARTMAP and differential evolution algorithms.

    Science.gov (United States)

    Liu, Chang; Wang, Guofeng; Xie, Qinglu; Zhang, Yanchao

    2014-06-16

    Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.

  10. Vibration Sensor-Based Bearing Fault Diagnosis Using Ellipsoid-ARTMAP and Differential Evolution Algorithms

    Directory of Open Access Journals (Sweden)

    Chang Liu

    2014-06-01

    Full Text Available Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM and a differential evolution (DE algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately.

  11. Research of converter transformer fault diagnosis based on improved PSO-BP algorithm

    Science.gov (United States)

    Long, Qi; Guo, Shuyong; Li, Qing; Sun, Yong; Li, Yi; Fan, Youping

    2017-09-01

    To overcome those disadvantages that BP (Back Propagation) neural network and conventional Particle Swarm Optimization (PSO) converge at the global best particle repeatedly in early stage and is easy trapped in local optima and with low diagnosis accuracy when being applied in converter transformer fault diagnosis, we come up with the improved PSO-BP neural network to improve the accuracy rate. This algorithm improves the inertia weight Equation by using the attenuation strategy based on concave function to avoid the premature convergence of PSO algorithm and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. At last the simulation results prove that the proposed approach has a better ability in optimizing BP neural network in terms of network output error, global searching performance and diagnosis accuracy.

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

    OpenAIRE

    Li Guoping; Zhang Qingwei; Ma Xiao

    2013-01-01

    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...

  13. Fault Tree Based Diagnosis with Optimal Test Sequencing for Field Service Engineers

    Science.gov (United States)

    Iverson, David L.; George, Laurence L.; Patterson-Hine, F. A.; Lum, Henry, Jr. (Technical Monitor)

    1994-01-01

    When field service engineers go to customer sites to service equipment, they want to diagnose and repair failures quickly and cost effectively. Symptoms exhibited by failed equipment frequently suggest several possible causes which require different approaches to diagnosis. This can lead the engineer to follow several fruitless paths in the diagnostic process before they find the actual failure. To assist in this situation, we have developed the Fault Tree Diagnosis and Optimal Test Sequence (FTDOTS) software system that performs automated diagnosis and ranks diagnostic hypotheses based on failure probability and the time or cost required to isolate and repair each failure. FTDOTS first finds a set of possible failures that explain exhibited symptoms by using a fault tree reliability model as a diagnostic knowledge to rank the hypothesized failures based on how likely they are and how long it would take or how much it would cost to isolate and repair them. This ordering suggests an optimal sequence for the field service engineer to investigate the hypothesized failures in order to minimize the time or cost required to accomplish the repair task. Previously, field service personnel would arrive at the customer site and choose which components to investigate based on past experience and service manuals. Using FTDOTS running on a portable computer, they can now enter a set of symptoms and get a list of possible failures ordered in an optimal test sequence to help them in their decisions. If facilities are available, the field engineer can connect the portable computer to the malfunctioning device for automated data gathering. FTDOTS is currently being applied to field service of medical test equipment. The techniques are flexible enough to use for many different types of devices. If a fault tree model of the equipment and information about component failure probabilities and isolation times or costs are available, a diagnostic knowledge base for that device can be

  14. Fuzzy fault diagnosis system of MCFC

    Institute of Scientific and Technical Information of China (English)

    Wang Zhenlei; Qian Feng; Cao Guangyi

    2005-01-01

    A kind of fault diagnosis system of molten carbonate fuel cell (MCFC) stack is proposed in this paper. It is composed of a fuzzy neural network (FNN) and a fault diagnosis element. FNN is able to deal with the information of the expert knowledge and the experiment data efficiently. It also has the ability to approximate any smooth system. FNN is used to identify the fault diagnosis model of MCFC stack. The fuzzy fault decision element can diagnose the state of the MCFC generating system, normal or fault, and can decide the type of the fault based on the outputs of FNN model and the MCFC system. Some simulation experiment results are demonstrated in this paper.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  16. A setup for active fault diagnosis

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik

    2006-01-01

    A setup for active fault diagnosis (AFD) of parametric faults in dynamic systems is formulated in this paper. It is shown that it is possible to use the same setup for both open loop systems, closed loop systems based on a nominal feedback controller as well as for closed loop systems based...

  17. Fault Diagnosis in Deaerator Using Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    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. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

    Science.gov (United States)

    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.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  20. Fault Diagnosis in Chemical Process Based on Self-organizing Map Integrated with Fisher Discriminant Analysis

    Institute of Scientific and Technical Information of China (English)

    CHEN Xinyi; YAN Xuefeng

    2013-01-01

    Fault diagnosis and monitoring are very important for complex chemical process.There are numerous methods that have been studied in this field,in which the effective visualization method is still challenging.In order to get a better visualization effect,a novel fault diagnosis method which combines self-organizing map (SOM) with Fisher discriminant analysis (FDA) is proposed.FDA can reduce the dimension of the data in terms of maximizing the separability of the classes.After feature extraction by FDA,SOM can distinguish the different states on the output map clearly and it can also be employed to monitor abnormal states.Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method.The result shows that the SOM integrated with FDA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

  1. Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Junsheng Cheng

    2009-01-01

    Full Text Available Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD techniques, the SVD technique based on empirical mode decomposition (EMD is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

  2. A Novel Method for Inverter Faults Detection and Diagnosis in PMSM Drives of HEVs based on Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    AKTAS, M.

    2012-11-01

    Full Text Available The paper proposes a novel method, based on wavelet decomposition, for detection and diagnosis of faults (switch short-circuits and switch open-circuits in the driving systems with Field Oriented Controlled Permanent Magnet Synchro?nous Motors (PMSM of Hybrid Electric Vehicles. The fault behaviour of the analyzed system was simulated by Matlab/SIMULINK R2010a. The stator currents during transients were analysed up to the sixth level detail wavelet decomposition by Symlet2 wavelet. The results prove that the proposed fault diagnosis system have very good capabilities.

  3. Diagnosis of Short-Circuit Fault in Large-Scale Permanent-Magnet Wind Power Generator Based on CMAC

    Directory of Open Access Journals (Sweden)

    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. Dynamic system identification and model-based fault diagnosis of an industrial gas turbine prototype

    Energy Technology Data Exchange (ETDEWEB)

    Simani, S. [Universita di Ferrara (Italy). Dipartimento di Ingegneria; Fantuzzi, C. [Universita di Modena e Reggio Emilia (Italy). Dipartimento di Scienze e Metodi per l' Ingegneria

    2006-07-15

    In this paper, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine process is presented. The main point of the present work consists of exploiting system identification schemes in connection with observer and filter design procedures for diagnostic purpose. Linear model identification (black-box modelling) and output estimation (dynamic observers and Kalman filters) integrated approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance. This scheme is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. A model of the process under investigation is obtained by identification procedures, whilst the residual generation task is achieved by means of output observers and Kalman filters designed in both noise-free and noisy assumptions. The proposed tools have been tested on a single-shaft industrial gas turbine prototype model and they have been evaluated using non-linear simulations, based on the gas turbine data. (author)

  5. Fault detection and diagnosis for gas turbines based on a kernelized information entropy model.

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  7. Navigation System Fault Diagnosis for Underwater Vehicle

    DEFF Research Database (Denmark)

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

    2014-01-01

    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 b...

  8. Fault Diagnosis of a Turbo-unit Based on Wavelet Packet Theory

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In this paper we studied the fault feature of the generator set and the characteristics of wavelet packet theory for signal de-noising. The vibration signal of the generator set in diffrent states is analyzed by using the signal re-construction technique of the wavelet packet theory. The time domain method is given for the generator set fault diagnosis. The experiment results show that the wavelet packet theory can be used to directly identify the state of the generator set and provide a credible new idea for complex machinery fault diagnosis.

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

    Directory of Open Access Journals (Sweden)

    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.

  10. A Method for Incipient Fault Diagnosis of Roller Bearings Based on the Wavelet Transform Correlation Filter and Hilbert Transform

    Institute of Scientific and Technical Information of China (English)

    ZENG Qing-hu; QIU Jing; LIU Guan-jun

    2007-01-01

    Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation Filter and Hilbert Transform was proposed. First, the weak fault information features are picked up from the roller bearings fault vibration signals by use of a de-noising characteristic of the Wavelet Transform Correlation Filter as the preprocessing of the Hilbert Envelope Analysis. Then, in order to get fault features frequency, de-noised wavelet coefficients of high scales which represent high frequency signal were analyzed by Hilbert Envelope Spectrum Analysis. The simulation signals and diagnosing examples analysis results reveal that the proposed method is more effective than the method of direct wavelet coefficients-Hilbert Transform in de-noising and clarifying roller bearing incipient fault.

  11. Fault Diagnosis for Compensating Capacitors of Jointless Track Circuit Based on Dynamic Time Warping

    Directory of Open Access Journals (Sweden)

    Wei Dong

    2014-01-01

    Full Text Available Aiming at the problem of online fault diagnosis for compensating capacitors of jointless track circuit, a dynamic time warping (DTW based diagnosis method is proposed in this paper. Different from the existing related works, this method only uses the ground indoor monitoring signals of track circuit to locate the faulty compensating capacitor, not depending on the shunt current of inspection train, which is an indispensable condition for existing methods. So, it can be used for online diagnosis of compensating capacitor, which has not yet been realized by existing methods. To overcome the key problem that track circuit cannot obtain the precise position of the train, the DTW method is used for the first time in this situation to recover the function relationship between receiver’s peak voltage and shunt position. The necessity, thinking, and procedure of the method are described in detail. Besides the classical DTW based method, two improved methods for improving classification quality and reducing computation complexity are proposed. Finally, the diagnosis experiments based on the simulation model of track circuit show the effectiveness of the proposed methods.

  12. Diagnosis of soft faults in analog integrated circuits based on fractional correlation

    Institute of Scientific and Technical Information of China (English)

    Deng Yong; Shi Yibing; Zhang Wei

    2012-01-01

    Aiming at the problem of diagnosing soft faults in analog integrated circuits,an approach based on fractional correlation is proposed.First,the Volterra series of the circuit under test (CUT) decomposed by the fractional wavelet packet are used to calculate the fractional correlation functions.Then,the calculated fractional correlation functions are used to form the fault signatures of the CUT.By comparing the fault signatures,the different soft faulty conditions of the CUT are identified and the faults are located.Simulations of benchmark circuits illustrate the proposed method and validate its effectiveness in diagnosing soft faults in analog integrated circuits.

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

    Science.gov (United States)

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

    2017-03-01

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

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

    Directory of Open Access Journals (Sweden)

    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.

  15. A Dynamic Integrated Fault Diagnosis Method for Power Transformers

    Directory of Open Access Journals (Sweden)

    Wensheng Gao

    2015-01-01

    Full Text Available 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.

  16. Rotating machinery fault diagnosis based on multiple fault manifolds%基于多故障流形的旋转机械故障诊断

    Institute of Scientific and Technical Information of China (English)

    苏祖强; 汤宝平; 赵明航; 秦毅

    2015-01-01

    针对旋转机械不同故障可能分布于不同故障流形,提出了基于多故障流形的旋转机械故障诊断方法。该方法分别提取每一类故障对应的故障流形,并在多故障流形上进行新增样本的故障识别。针对所需解决的低维流形提取、流形内蕴维数选取和多故障流形上的故障识别问题,分别采用线性局部切空间排列算法和免疫遗传算法来进行低维故障流形提取和流形内蕴维数选取,并通过故障样本重构误差这一新的判别准则进行故障识别。齿轮箱故障模拟实验的结果验证了此方法的有效性。%The existing fault diagnosis methods based on manifold learning assume that all the faults distribute on a single mani-fold,however the faults may distribute on different manifolds in practical applications.Aiming at this problem,rotating ma-chinery fault diagnosis method based on multiple fault manifolds is proposed.Firstly,mixed-domain features are extracted from the vibration signals to characterize the property of the faults,and the vibration signals are also preprocessed by empirical model decomposition before feature extraction.Then,the corresponding fault manifold of each fault is extracted from the high-dimensional fault samples.In the method,linear local tangent space alignment is applied to solve the problem of low-dimen-sional manifold extraction,and immune genetic algorithm is used to select the intrinsic dimensionality of fault manifold.At last,the test samples are respectively projected to all the fault manifolds,and the projection errors are used as the criterion to determine the fault types of the test samples.In order to verify the effectiveness of the proposed fault diagnosis method,the method is applied to diagnose the faults of the gear box.The experimental results indicate that feature compression can remove the redundant information between features,and moreover fault diagnosis method based on multiple fault

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

    Institute of Scientific and Technical Information of China (English)

    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.

  18. GENERATOR VIBRATION FAULT DIAGNOSIS METHOD BASED ON ROTOR VIBRATION AND STATOR WINDING PARALLEL BRANCHES CIRCULATING CURRENT CHARACTERISTICS

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Science.gov (United States)

    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.

  20. A Vague Decision Method for Analog Circuit Fault Diagnosis Based on Description Sphere

    Institute of Scientific and Technical Information of China (English)

    LUO Hui; WANG Youren; CUI Jiang

    2011-01-01

    This paper proposes a vague decision method for analog circuit fault diagnosis based on description sphere.Firstly,the proposed method uses the wavelet transform as the preprocessor to extract fault features from the output voltages of the circuit under test (CUT).And then,each class sample is trained to produce a minimum description sphere.Finally,the test samples are detected by a defined vague decision rule,which is based on the vague weight distance between the test data and the center of description sphere.The defined decision rule fuses the truth and false membership degrees of the test sample and the weight of the description sphere,and it can effectively deal with the uncertain information.The reliability of the defined decision rule is proved theoretically.This new diagnostic method is first applied to testing two actual circuits,and then it is compared with other two diagnostic methods.The experimental results show that the proposed technique can achieve good performance and reduce the diagnostic time.

  1. A Novel Association Rule Mining with IEC Ratio Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers

    Directory of Open Access Journals (Sweden)

    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.

  2. Cooperative Human-Machine Fault Diagnosis

    Science.gov (United States)

    Remington, Roger; Palmer, Everett

    1987-02-01

    Current expert system technology does not permit complete automatic fault diagnosis; significant levels of human intervention are still required. This requirement dictates a need for a division of labor that recognizes the strengths and weaknesses of both human and machine diagnostic skills. Relevant findings from the literature on human cognition are combined with the results of reviews of aircrew performance with highly automated systems to suggest how the interface of a fault diagnostic expert system can be designed to assist human operators in verifying machine diagnoses and guiding interactive fault diagnosis. It is argued that the needs of the human operator should play an important role in the design of the knowledge base.

  3. Diagnosis of Gearbox Typical Fault in Rolling Mills Based on the Wavelet Packets Technology

    Institute of Scientific and Technical Information of China (English)

    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.

  4. Solving fault diagnosis problems linear synthesis techniques

    CERN Document Server

    Varga, Andreas

    2017-01-01

    This book addresses fault detection and isolation topics from a computational perspective. Unlike most existing literature, it bridges the gap between the existing well-developed theoretical results and the realm of reliable computational synthesis procedures. The model-based approach to fault detection and diagnosis has been the subject of ongoing research for the past few decades. While the theoretical aspects of fault diagnosis on the basis of linear models are well understood, most of the computational methods proposed for the synthesis of fault detection and isolation filters are not satisfactory from a numerical standpoint. Several features make this book unique in the fault detection literature: Solution of standard synthesis problems in the most general setting, for both continuous- and discrete-time systems, regardless of whether they are proper or not; consequently, the proposed synthesis procedures can solve a specific problem whenever a solution exists Emphasis on the best numerical algorithms to ...

  5. UiLog:Improving Log-Based Fault Diagnosis by Log Analysis

    Institute of Scientific and Technical Information of China (English)

    De-Qing Zou; Hao Qin; Hai Jin

    2016-01-01

    In modern computer systems, system event logs have always been the primary source for checking system status. As computer systems become more and more complex, the interaction between software and hardware increases frequently. The components will generate enormous log information, including running reports and fault information. The sheer quantity of data is a great challenge for analysis relying on the manual method. In this paper, we implement a management and analysis system of log information, which can assist system administrators to understand the real-time status of the entire system, classify logs into different fault types, and determine the root cause of the faults. In addition, we improve the existing fault correlation analysis method based on the results of system log classification. We apply the system in a cloud computing environment for evaluation. The results show that our system can classify fault logs automatically and effectively. With the proposed system, administrators can easily detect the root cause of faults.

  6. Discrete wavelet transform-based fault diagnosis for driving system of pipeline detection robot arm

    Institute of Scientific and Technical Information of China (English)

    Deng Huiyu; Wang Xinli; Ma Peisun

    2005-01-01

    A real-time wavelet multi-resolution analysis (MRA)-based fault detection algorithm is proposed. The first stage detailed MRA signals extracted from the original signals were used as the criteria for fault detection. By measuring sharp variations in the detailed MRA signals, faults in the motor driving system of pipeline detection robot arm could be detected. The fault type was then identified by comparison of the three-phase MRA sharp variations. The effects of the faults were examined. The simulation results show that this algorithm is effective and robust, it is promising for fault detection in a robot's joint driving system. The method is simple, rapid and it can operate in real time.

  7. Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising

    Directory of Open Access Journals (Sweden)

    Wentao He

    2016-01-01

    Full Text Available Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose.

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

    Science.gov (United States)

    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.

  9. A Signal Based Triangular Structuring Element for Mathematical Morphological Analysis and Its Application in Rolling Element Bearing Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Zhaowen Chen

    2014-01-01

    Full Text Available Mathematical morphology (MM is an efficient nonlinear signal processing tool. It can be adopted to extract fault information from bearing signal according to a structuring element (SE. Since the bearing signal features differ for every unique cause of failure, the SEs should be well tailored to extract the fault feature from a particular signal. In the following, a signal based triangular SE according to the statistics of the magnitude of a vibration signal is proposed, together with associated methodology, which processes the bearing signal by MM analysis based on proposed SE to get the morphology spectrum of a signal. A correlation analysis on morphology spectrum is then employed to obtain the final classification of bearing faults. The classification performance of the proposed method is evaluated by a set of bearing vibration signals with inner race, ball, and outer race faults, respectively. Results show that all faults can be detected clearly and correctly. Compared with a commonly used flat SE, the correlation analysis on morphology spectrum with proposed SE gives better performance at fault diagnosis of bearing, especially the identification of the location of outer race fault and the level of fault severity.

  10. Sparse Representation of Transients Based on Wavelet Basis and Majorization-Minimization Algorithm for Machinery Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Wei Fan

    2014-01-01

    Full Text Available Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detection of transients a basis pursuit denoising (BPD problem, which is hard to be solved in explicit form. With sparse representation theory, this paper proposes a novel method for machinery fault diagnosis by combining the wavelet basis and majorization-minimization (MM algorithm. This method converts transients hidden in the noisy signal into sparse coefficients; thus the transients can be detected sparsely. Simulated study concerning cyclic transient signals with different signal-to-noise ratio (SNR shows that the effectiveness of this method. The comparison in the simulated study shows that the proposed method outperforms the method based on split augmented Lagrangian shrinkage algorithm (SALSA in convergence and detection effect. Application in defective gearbox fault diagnosis shows the fault feature of gearbox can be sparsely and effectively detected. A further comparison between this method and the method based on SALSA shows the superiority of the proposed method in machinery fault diagnosis.

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

    Directory of Open Access Journals (Sweden)

    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.

  12. A Novel Approach To Diagnosis Of Analog Circuit Incipient Faults Based On KECA And OAO LSSVM

    Directory of Open Access Journals (Sweden)

    Zhang Chaolong

    2015-06-01

    Full Text Available Correct incipient identification of an analog circuit fault is conducive to the health of the analog circuit, yet very difficult. In this paper, a novel approach to analog circuit incipient fault identification is presented. Time responses are acquired by sampling outputs of the circuits under test, and then the responses are decomposed by the wavelet transform in order to generate energy features. Afterwards, lower-dimensional features are produced through the kernel entropy component analysis as samples for training and testing a one-against-one least squares support vector machine. Simulations of the incipient fault diagnosis for a Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass filter demonstrate the diagnosing procedure of the proposed approach, and also reveal that the proposed approach has higher diagnosis accuracy than the referenced methods.

  13. Design and implementation of virtual fault diagnosis system for photoelectric tracking devices based on OpenGL

    Science.gov (United States)

    Hou, MingLiang; Li, Cunhua; Zhang, Yong; Su, Liyun

    2009-10-01

    In view of the crucial deficiency of the traditional diagnosis approaches for photoelectric tracking devices and the output of more sufficient diagnosis information, in this paper, an virtual fault diagnosis system based on open graphic library(OpenGL) is proposed. Firstly, some interrelated key principles and technology of virtual reality, visualization and intelligent fault diagnosis technology are put forward. Then, the demand analysis and architecture of the system are elaborated. Next, details of interrelated essential implementation issues are also discussed, including the the 3D modeling of the related diagnosis equipments, key development process and design via OpenGL. Practical applications and experiments illuminate that the proposed approach is feasible and effective.

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

    Science.gov (United States)

    Zheng, Jinde; Pan, Haiyang; Cheng, Junsheng

    2017-02-01

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

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

    Directory of Open Access Journals (Sweden)

    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.

  16. Model based fault diagnosis in a centrifugal pump application using structural analysis

    DEFF Research Database (Denmark)

    Kallesøe, C. S.; Izadi-Zamanabadi, Roozbeh; Rasmussen, Henrik;

    2004-01-01

    A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are use...... it to an industrial benchmark. The benchmark tests have shown that the algorithm is capable of detection and isolation of five different faults in the mechanical and hydraulic parts of the pump.......A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are used...

  17. Model Based Fault Diagnosis in a Centrifugal Pump Application using Structural Analysis

    DEFF Research Database (Denmark)

    Kallesøe, C. S.; Izadi-Zamanabadi, Roozbeh; Rasmussen, Henrik;

    2004-01-01

    A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are use...... it to an industrial benchmark. The benchmark tests have shown that the algorithm is capable of detection and isolation of five different faults in the mechanical and hydraulic parts of the pump.......A model based approach for fault detection and isolation in a centrifugal pump is proposed in this paper. The fault detection algorithm is derived using a combination of structural analysis, Analytical Redundant Relations (ARR) and observer designs. Structural considerations on the system are used...

  18. Wayside Bearing Fault Diagnosis Based on a Data-Driven Doppler Effect Eliminator and Transient Model Analysis

    Directory of Open Access Journals (Sweden)

    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.

  19. Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis.

    Science.gov (United States)

    Liu, Fang; Shen, Changqing; He, Qingbo; Zhang, Ao; Liu, Yongbin; Kong, Fanrang

    2014-05-05

    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.

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

    Science.gov (United States)

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

    2017-01-01

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

  1. Incipient Gearbox Fault Diagnosis Based on the Reverse State Transformation of the Chaotic Duffing Oscillator and Sampling Integral Technology

    Directory of Open Access Journals (Sweden)

    Li Jie

    2015-01-01

    Full Text Available Incipient fault for a gearbox diagnosis is difficult because the signals with low signal-to-noise ratio (SNR are corrupted with background noise. A method based on chaos theory and sampling integral technology will be presented to detect the incipient fault of gearbox according to the characters of the gearbox vibration signals. Sampling integral technology was used to improve the tracking ability of fault signals with lower SNR. The small changes in the sidebands of meshing frequency can be detected by the transformation of chaotic phase diagram and its Hu moment invariants, and on this basis the incipient faults can be diagnosed. The results based on gearboxes experiment justify the effectiveness of the method.

  2. Completing fault models for abductive diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Knill, E. [Los Alamos National Lab., NM (United States); Cox, P.T.; Pietrzykowski, T. [Technical Univ., NS (Canada)

    1992-11-05

    In logic-based diagnosis, the consistency-based method is used to determine the possible sets of faulty devices. If the fault models of the devices are incomplete or nondeterministic, then this method does not necessarily yield abductive explanations of system behavior. Such explanations give additional information about faulty behavior and can be used for prediction. Unfortunately, system descriptions for the consistency-based method are often not suitable for abductive diagnosis. Methods for completing the fault models for abductive diagnosis have been suggested informally by Poole and by Cox et al. Here we formalize these methods by introducing a standard form for system descriptions. The properties of these methods are determined in relation to consistency-based diagnosis and compared to other ideas for integrating consistency-based and abductive diagnosis.

  3. MultiBoost with ENN-based ensemble fault diagnosis method and its application in complicated chemical process

    Institute of Scientific and Technical Information of China (English)

    夏崇坤; 苏成利; 曹江涛; 李平

    2016-01-01

    Fault diagnosis plays an important role in complicated industrial process. It is a challenging task to detect, identify and locate faults quickly and accurately for large-scale process system. To solve the problem, a novel MultiBoost-based integrated ENN (extension neural network) fault diagnosis method is proposed. Fault data of complicated chemical process have some difficult-to-handle characteristics, such as high-dimension, non-linear and non-Gaussian distribution, so we use margin discriminant projection(MDP) algorithm to reduce dimensions and extract main features. Then, the affinity propagation (AP) clustering method is used to select core data and boundary data as training samples to reduce memory consumption and shorten learning time. Afterwards, an integrated ENN classifier based on MultiBoost strategy is constructed to identify fault types. The artificial data sets are tested to verify the effectiveness of the proposed method and make a detailed sensitivity analysis for the key parameters. Finally, a real industrial system—Tennessee Eastman (TE) process is employed to evaluate the performance of the proposed method. And the results show that the proposed method is efficient and capable to diagnose various types of faults in complicated chemical process.

  4. Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation

    Directory of Open Access Journals (Sweden)

    Huanguo Chen

    2017-01-01

    Full Text Available The rapid expansion of wind farms has accelerated research into improving the reliability of wind turbines to reduce operational and maintenance costs. A critical component in wind turbine drive-trains is the gearbox, which is prone to different types of failures due to long-term operation under tough environments, variable speeds and alternating loads. To detect gearbox fault early, a method is proposed for an effective fault diagnosis by using improved ensemble empirical mode decomposition (EEMD and Hilbert square demodulation (HSD. The method was verified numerically by implementing the scheme on the vibration signals measured from bearing and gear test rigs. In the implementation process, the following steps were identified as being important: (1 in order to increase the accuracy of EEMD, a criterion of selecting the proper resampling frequency for raw vibration signals was developed; (2 to select the fault related intrinsic mode function (IMF that had the biggest kurtosis index value, the resampled signal was decomposed into a series of IMFs; (3 the selected IMF was demodulated by means of HSD, and fault feature information could finally be obtained. The experimental results demonstrate the merit of the proposed method in gearbox fault diagnosis.

  5. An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

    Directory of Open Access Journals (Sweden)

    Meijiao Li

    2014-11-01

    Full Text Available In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.

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

    Directory of Open Access Journals (Sweden)

    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.

  7. A Diagnosis Method for Rotation Machinery Faults Based on Dimensionless Indexes Combined with K-Nearest Neighbor Algorithm

    Directory of Open Access Journals (Sweden)

    Jianbin Xiong

    2015-01-01

    Full Text Available It is difficult to well distinguish the dimensionless indexes between normal petrochemical rotating machinery equipment and those with complex faults. When the conflict of evidence is too big, it will result in uncertainty of diagnosis. This paper presents a diagnosis method for rotation machinery fault based on dimensionless indexes combined with K-nearest neighbor (KNN algorithm. This method uses a KNN algorithm and an evidence fusion theoretical formula to process fuzzy data, incomplete data, and accurate data. This method can transfer the signals from the petrochemical rotating machinery sensors to the reliability manners using dimensionless indexes and KNN algorithm. The input information is further integrated by an evidence synthesis formula to get the final data. The type of fault will be decided based on these data. The experimental results show that the proposed method can integrate data to provide a more reliable and reasonable result, thereby reducing the decision risk.

  8. FAULT DIAGNOSIS BASED ON INTE- GRATION OF CLUSTER ANALYSIS,ROUGH SET METHOD AND FUZZY NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Science.gov (United States)

    Wang, Tianyang; Chu, Fulei; Han, Qinkai

    2017-03-01

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

  10. Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy

    Directory of Open Access Journals (Sweden)

    Lei Zhang

    2016-01-01

    Full Text Available In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT, sample entropy (SampEn, and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier.

  11. Research on Fault Diagnosis System of a Diesel Engine Based on Wavelet Analysis and LabVIEW Software

    Directory of Open Access Journals (Sweden)

    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.

  12. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network.

    Science.gov (United States)

    Jiang, Peng; Hu, Zhixin; Liu, Jun; Yu, Shanen; Wu, Feng

    2016-10-13

    Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB) and a Lowest False Positive criterion (LFP), for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

  13. Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Peng Jiang

    2016-10-01

    Full Text Available Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE and work through a layer-by-layer successive learning process. The features are added to the top Softmax regression layer to construct the discriminative fault characteristics for diagnosis in a supervised manner. Considering the expensive and time consuming labeling of sensor data in chemical applications, in contrast to the available methods, we employ a novel active learning criterion for the particularity of chemical processes, which is a combination of Best vs. Second Best criterion (BvSB and a Lowest False Positive criterion (LFP, for further fine-tuning of diagnosis model in an active manner rather than passive manner. That is, we allow models to rank the most informative sensor data to be labeled for updating the DNN parameters during the interaction phase. The effectiveness of the proposed method is validated in two well-known industrial datasets. Results indicate that the proposed method can obtain superior diagnosis accuracy and provide significant performance improvement in accuracy and false positive rate with less labeled chemical sensor data by further active learning compared with existing methods.

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

    DEFF Research Database (Denmark)

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

    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...

  15. On-line Dynamic Model Correction Based Fault Diagnosis in Chemical Processes

    Institute of Scientific and Technical Information of China (English)

    田文德; 孙素莉

    2007-01-01

    A novel fault detection and diagnosis method was proposed,using dynamic simulation to monitor chemical process and identify faults when large tracking deviations occur.It aims at parameter failures,and the parameters are updated via on-line correction.As it can predict the trend of process and determine the existence of malfunctions simultaneously,this method does not need to design problem-specific observer to estimate unmeasured state variables.Application of the proposed method is presented on one water tank and one aromatization reactor,and the results are compared with those from the traditional method.

  16. A fault diagnosis approach for diesel engine valve train based on improved ITD and SDAG-RVM

    Science.gov (United States)

    Yu, Liu; Junhong, Zhang; Fengrong, Bi; Jiewei, Lin; Wenpeng, Ma

    2015-02-01

    Targeting the non-stationary characteristics of the vibration signals of a diesel engine valve train, and the limitation of the autoregressive (AR) model, a novel approach based on the improved intrinsic time-scale decomposition (ITD) and relevance vector machine (RVM) is proposed in this paper for the identification of diesel engine valve train faults. The approach mainly consists of three stages: First, prior to the feature extraction, non-uniform B-spline interpolation is introduced to the ITD method for the fitting of baseline signal, then the improved ITD is used to decompose the non-stationary signals into a set of stationary proper rotation components (PRCs). Second, the AR model is established for each PRC, and the first several AR coefficients together with the remnant variance of all PRCs are regarded as the fault feature vectors. Finally, a new separability based directed acyclic graph (SDAG) method is proposed to determine the structure of multi-class RVM, and the fault feature vectors are classified using the SDAG-RVM classifier to recognize the fault of the diesel engine valve train. The experimental results demonstrate that the proposed fault diagnosis approach can effectively extract the fault features and accurately identify the fault patterns.

  17. Fault diagnosis technology based on transistor behavior analysis for physical analysis

    OpenAIRE

    Sanada, M; Yoshizawa, Y.

    2008-01-01

    The novel method has been developed to detect accuracy faultelements in transistor level circuit, analyzing the characteristics of circuitoperation influenced on leakage fault and being combined with diagnosissoftware, based on switching level simulation. This method is based on behaviorof CMOS transistor to which applied unstable voltage produced by leakage fault.Unsettled logic brings the transistor’s operation point to saturation area withmulti-impedance value and forms penetration current...

  18. Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    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.

  19. Reconstruction based approach to sensor fault diagnosis using auto-associative neural networks

    Institute of Scientific and Technical Information of China (English)

    Mousavi Hamidreza; Shahbazian Mehdi; Jazayeri-Rad Hooshang; Nekounam Aliakbar

    2014-01-01

    Fault diagnostics is an important research area including different techniques. Principal component analysis (PCA) is a linear technique which has been widely used. For nonlinear processes, however, the nonlinear principal component analysis (NLPCA) should be applied. In this work, NLPCA based on auto-associative neural network (AANN) was applied to model a chemical process using historical data. First, the residuals generated by the AANN were used for fault detection and then a reconstruction based approach called enhanced AANN (E-AANN) was presented to isolate and reconstruct the faulty sensor simultaneously. The proposed method was implemented on a continuous stirred tank heater (CSTH) and used to detect and isolate two types of faults (drift and offset) for a sensor. The results show that the proposed method can detect, isolate and reconstruct the occurred fault properly.

  20. EXPERIMENT BASED FAULT DIAGNOSIS ON BOTTLE FILLING PLANT WITH LVQ ARTIFICIAL NEURAL NETWORK ALGORITHM

    Directory of Open Access Journals (Sweden)

    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.

  1. Synthetic Intelligent Fault Diagnosis Technology for Complex Process

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A fault diagnosis method of knowledge-based fuzzy neural network is proposed for complex process, which is hard to develop practical mathematical model. Fault detection is performed through a knowledge-based system, where fault detection heuristic rules have been generated from deep and shallow knowledge of the process. The fuzzy neural network performs the fault diagnosis task. This method does not need practical mathematical models of objects, so it is a strong implement for complex process.

  2. Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

    Directory of Open Access Journals (Sweden)

    Mariela Cerrada

    2015-09-01

    Full Text Available There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The Sensors 2015, 15 23904 approach is tested on a dataset from a real test bed with several fault classes under different running conditions of load and velocity. The model performance for diagnosis is over 98%.

  3. Efficient RT-Level Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    Ozgur Sinanoglu; Alex Orailoglu

    2005-01-01

    Increasing IC densities necessitate diagnosis methodologies with enhanced defect locating capabilities. Yet the computational effort expended in extracting diagnostic information and the stringent storage requirements constitute major concerns due to the tremendous number of faults in typical ICs. In this paper, we propose an RT-level diagnosis methodology capable of responding to these challenges. In the proposed scheme, diagnostic information is computed on a grouped fault effect basis, enhancing both the storage and the computational aspects. The fault effect grouping criteria are identified based on a module structure analysis, improving the propagation ability of the diagnostic information through RT modules. Experimental results show that the proposed methodology provides superior speed-ups and significant diagnostic information compression at no sacrifice in diagnostic resolution, compared to the existing gate-level diagnosis approaches.

  4. A Novel Association Rule Mining with IEC Ratio Based Dissolved Gas Analysis for Fault Diagnosis of Power Transformers

    Directory of Open Access Journals (Sweden)

    Kanika Shrivastava

    2012-06-01

    Full Text Available Dissolved gas Analysis (DGA is the most important component of finding fault in large oil filled transformers. Early detection of incipient faults in transformers reduces costly unplanned outages. The most sensitive and reliable technique for evaluating the core of transformer is dissolved gas analysis. In this paper we evaluate different transformer condition on different cases. This paper uses dissolved gas analysis to study the history of different transformers in service, from which dissolved combustible gases (DCG in oil are used as a diagnostic tool for evaluating the condition of the transformer. Oil quality and dissolved gasses tests are comparatively used for this purpose. In this paper we present a novel approach which is based on association rule mining and IEC ratio method. By using data mining concept we can categorize faults based on single and multiple associations and also map the percentage of fault. This is an efficient approach for fault diagnosis of power transformers where we can find the fault in all obvious conditions. We use java for programming and comparative study.

  5. A Desk-top tutorial Demonstration of Model-based Fault Detection and Diagnosis

    OpenAIRE

    Shi, John Z.; Elshanti, Ali; Gu, Fengshou; Ball, Andrew

    2007-01-01

    In this paper, a demonstration on the model-based approach for fault detection has been presented. The aim of this demo is to provide students a desk-top tool to start learning model-based approach. The demo works on a traditional three-tank system. After a short review of the model-based approach, this paper emphasizes on two difficulties often asked by students when they start learning model-based approach: how to develop a system model and how to generate residual for fault detection. The ...

  6. Study on fault diagnosis method for nuclear power plant based on hadamard error-correcting output code

    Science.gov (United States)

    Mu, Y.; Sheng, G. M.; Sun, P. N.

    2017-05-01

    The technology of real-time fault diagnosis for nuclear power plants(NPP) has great significance to improve the safety and economy of reactor. The failure samples of nuclear power plants are difficult to obtain, and support vector machine is an effective algorithm for small sample problem. NPP is a very complex system, so in fact the type of NPP failure may occur very much. ECOC is constructed by the Hadamard error correction code, and the decoding method is Hamming distance method. The base models are established by lib-SVM algorithm. The result shows that this method can diagnose the faults of the NPP effectively.

  7. Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Songrong Luo

    2015-01-01

    Full Text Available When a local defect occurs in gearbox, the vibration signals present as the form of multicomponent amplitude modulation and frequency modulation (AM-FM. Demodulation analysis is an effective way for this kind of signal. A self-adaptive wavelet ridge demodulation method based on LCD is proposed in this paper. Firstly, multicomponent AM-FM signal is decomposed into series of intrinsic scale components (ISCs and the special intrinsic scale component is selected in order to decrease the lower frequency background noise. Secondly, the genetic algorithm is employed to optimize wavelet parameters according to the inherent characteristics of signal; thirdly, self-adaptive wavelet ridge demodulation wavelet for the selected ISC component is performed to get instantaneous amplitude (IA or instantaneous frequency (IF. Lastly, the characteristics frequency can be obtained to identify the working state or failure information from its spectrum. By two simulation signals, the proposed method was compared with various existing demodulation methods; the simulation results show that it has higher accuracy and higher noise tolerant performance than others. Furthermore, the proposed method was applied to incipient fault diagnosis for gearbox and the results show that it is simple and effective.

  8. Clustering diagnosis of rolling element bearing fault based on integrated Autoregressive/Autoregressive Conditional Heteroscedasticity model

    Science.gov (United States)

    Wang, Guofeng; Liu, Chang; Cui, Yinhu

    2012-09-01

    Feature extraction plays an important role in the clustering analysis. In this paper an integrated Autoregressive (AR)/Autoregressive Conditional Heteroscedasticity (ARCH) model is proposed to characterize the vibration signal and the model coefficients are adopted as feature vectors to realize clustering diagnosis of rolling element bearings. The main characteristic is that the AR item and ARCH item are interrelated with each other so that it can depict the excess kurtosis and volatility clustering information in the vibration signal more accurately in comparison with two-stage AR/ARCH model. To testify the correctness, four kinds of bearing signals are adopted for parametric modeling by using the integrated and two-stage AR/ARCH model. The variance analysis of the model coefficients shows that the integrated AR/ARCH model can get more concentrated distribution. Taking these coefficients as feature vectors, K means based clustering is utilized to realize the automatic classification of bearing fault status. The results show that the proposed method can get more accurate results in comparison with two-stage model and discrete wavelet decomposition.

  9. A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

    Directory of Open Access Journals (Sweden)

    Yu Yang

    2008-05-01

    Full Text Available An accurate autoregressive (AR model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD, which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs, is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

  10. Weak Fault Diagnosis of Wind Turbine Gearboxes Based on MED-LMD

    Directory of Open Access Journals (Sweden)

    Zhijian Wang

    2017-06-01

    Full Text Available In view of the problem that the fault signal of the rolling bearing is weak and the fault feature is difficult to extract in the strong noise environment, a method based on minimum entropy deconvolution (MED and local mean deconvolution (LMD is proposed to extract the weak fault features of the rolling bearing. Through the analysis of the simulation signal, we find that LMD has many limitations for the feature extraction of weak signals under strong background noise. In order to eliminate the noise interference and extract the characteristics of the weak fault, MED is employed as the pre-filter to remove noise. This method is applied to the weak fault feature extraction of rolling bearings; that is, using MED to reduce the noise of the wind turbine gearbox test bench under strong background noise, and then using the LMD method to decompose the denoised signals into several product functions (PFs, and finally analyzing the PF components that have strong correlation by a cyclic autocorrelation function. The finding is that the failure of the wind power gearbox is generated from the micro-bending of the high-speed shaft and the pitting of the #10 bearing outer race at the output end of the high-speed shaft. This method is compared with LMD, which shows the effectiveness of this method. This paper provides a new method for the extraction of multiple faults and weak features in strong background noise.

  11. Robust fault diagnosis for a class of nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    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.

  12. Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

    Institute of Scientific and Technical Information of China (English)

    孙玉山; 李岳明; 张国成; 张英浩; 吴海波

    2016-01-01

    Autonomous underwater vehicles (AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model (estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.

  13. Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II

    Directory of Open Access Journals (Sweden)

    Peng-yuan Liu

    2016-01-01

    Full Text Available A novel feature extraction and selection scheme is presented for intelligent engine fault diagnosis by utilizing two-dimensional nonnegative matrix factorization (2DNMF, mutual information, and nondominated sorting genetic algorithms II (NSGA-II. Experiments are conducted on an engine test rig, in which eight different engine operating conditions including one normal condition and seven fault conditions are simulated, to evaluate the presented feature extraction and selection scheme. In the phase of feature extraction, the S transform technique is firstly utilized to convert the engine vibration signals to time-frequency domain, which can provide richer information on engine operating conditions. Then a novel feature extraction technique, named two-dimensional nonnegative matrix factorization, is employed for characterizing the time-frequency representations. In the feature selection phase, a hybrid filter and wrapper scheme based on mutual information and NSGA-II is utilized to acquire a compact feature subset for engine fault diagnosis. Experimental results by adopted three different classifiers have demonstrated that the proposed feature extraction and selection scheme can achieve a very satisfying classification performance with fewer features for engine fault diagnosis.

  14. Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems.

    Science.gov (United States)

    Huang, Gang; Luo, Yi-Ping; Zhang, Chang-Fan; Huang, Yi-Shan; Zhao, Kai-Hui

    2015-05-11

    This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in αβ coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method.

  15. Current Sensor Fault Diagnosis Based on a Sliding Mode Observer for PMSM Driven Systems

    Directory of Open Access Journals (Sweden)

    Gang Huang

    2015-05-01

    Full Text Available This paper proposes a current sensor fault detection method based on a sliding mode observer for the torque closed-loop control system of interior permanent magnet synchronous motors. First, a sliding mode observer based on the extended flux linkage is built to simplify the motor model, which effectively eliminates the phenomenon of salient poles and the dependence on the direct axis inductance parameter, and can also be used for real-time calculation of feedback torque. Then a sliding mode current observer is constructed in αβ coordinates to generate the fault residuals of the phase current sensors. The method can accurately identify abrupt gain faults and slow-variation offset faults in real time in faulty sensors, and the generated residuals of the designed fault detection system are not affected by the unknown input, the structure of the observer, and the theoretical derivation and the stability proof process are concise and simple. The RT-LAB real-time simulation is used to build a simulation model of the hardware in the loop. The simulation and experimental results demonstrate the feasibility and effectiveness of the proposed method.

  16. Fault diagnosis for power system transmission line based on PCA and SVMs

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yuanjun; Li, Kang; Liu, Xueqin [Queen' s Univ., Belfast (United Kingdom). School of Electronics, Electrical Engineering and Computer Science

    2013-07-01

    This paper presents the application of a fault detection method based on the principal component analysis (PCA) and support vector machine (SVM) for the detection and classification of faults in power system transmission lines. Consider that the data may be huge with a number of strongly correlated variables, method which incorporates both the principal component analysis (PCA) and support vector machine (SVM) is proposed. This algorithm has two stages. The first stage involves the use of the PCA to reduce the dimensionality as well as to find violating point of the signals according to the confidential limit. The features of each fault extracted from the data are used in the second stage to construct SVM networks. The second stage is to use pattern recognition method to distinguish the phase of the faulty situation. The proposed scheme is able to solve the problems encountered in traditional magnitude and frequency based methods. The benefits of this improvement are demonstrated.

  17. Research on the fault diagnosis of bearing based on wavelet and demodulation

    Science.gov (United States)

    Li, Jiapeng; Yuan, Yu

    2017-05-01

    As a most commonly-used machine part, antifriction bearing is extensively used in mechanical equipment. Vibration signal analysis is one of the methods to monitor and diagnose the running status of antifriction bearings. Therefore, using wavelet analysis for demising is of great importance in the engineering practice. This paper firstly presented the basic theory of wavelet analysis to study the transformation, decomposition and reconstruction of wavelet. In addition, edition software LabVIEW was adopted to conduct wavelet and demodulation upon the vibration signal of antifriction bearing collected. With the combination of Hilbert envelop demodulation analysis, the fault character frequencies of the demised signal were extracted to conduct fault diagnosis analysis, which serves as a reference for the wavelet and demodulation of the vibration signal in engineering practice.

  18. A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy

    Science.gov (United States)

    Li, Yongbo; Xu, Minqiang; Wang, Rixin; Huang, Wenhu

    2016-01-01

    This paper presents a new rolling bearing fault diagnosis method based on local mean decomposition (LMD), improved multiscale fuzzy entropy (IMFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT). When the fault occurs in rolling bearings, the measured vibration signal is a multi-component amplitude-modulated and frequency-modulated (AM-FM) signal. LMD, a new self-adaptive time-frequency analysis method can decompose any complicated signal into a series of product functions (PFs), each of which is exactly a mono-component AM-FM signal. Hence, LMD is introduced to preprocess the vibration signal. Furthermore, IMFE that is designed to avoid the inaccurate estimation of fuzzy entropy can be utilized to quantify the complexity and self-similarity of time series for a range of scales based on fuzzy entropy. Besides, the LS approach is introduced to refine the fault features by sorting the scale factors. Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault pattern identifications. The experimental results validate the effectiveness of the methodology and demonstrate that proposed algorithm can be applied to recognize the different categories and severities of rolling bearings.

  19. Mechanical Fault Diagnosis Using Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    LI Ling-jun; ZHANG Zhou-suo; HE Zheng-jia

    2003-01-01

    The Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory ( SLT) , which can get good classification effects even with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents a SVM-based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearings is conducted. The vibration signals acquired from the bearings are used directly in the calculating without the preprocessing of extracting its features. Compared with the methods based on Artificial Neural Network (ANN), the SVM-based meth-od has desirable advantages. It is applicable for on-line diagnosis of mechanical systems.

  20. High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process☆

    Institute of Scientific and Technical Information of China (English)

    Ying Tian; Wenli Du; Feng Qian

    2015-01-01

    Purified terephthalic acid (PTA) is an important chemical raw material. P-xylene (PX) is transformed to terephthalic acid (TA) through oxidation process and TA is refined to produce PTA. The PX oxidation reaction is a complex process involving three-phase reaction of gas, liquid and solid. To monitor the process and to im-prove the product quality, as wel as to visualize the fault type clearly, a fault diagnosis method based on self-organizing map (SOM) and high dimensional feature extraction method, local tangent space alignment (LTSA), is proposed. In this method, LTSA can reduce the dimension and keep the topology information simultaneously, and SOM distinguishes various states on the output map. Monitoring results of PX oxidation reaction process in-dicate that the LTSA–SOM can wel detect and visualize the fault type.

  1. Hierarchical Fault Diagnosis for a Hybrid System Based on a Multidomain Model

    Directory of Open Access Journals (Sweden)

    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.

  2. Active fault diagnosis by temporary destabilization

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Stoustrup, Jakob

    2006-01-01

    An active fault diagnosis method for parametric or multiplicative faults is proposed. The method periodically adds a term to the controller that for a short period of time renders the system unstable if a fault has occurred, which facilitates rapid fault detection. An illustrative example is given....

  3. Intelligent Fault Diagnosis in a Power Distribution Network

    Directory of Open Access Journals (Sweden)

    Oluleke O. Babayomi

    2016-01-01

    Full Text Available This paper presents a novel method of fault diagnosis by the use of fuzzy logic and neural network-based techniques for electric power fault detection, classification, and location in a power distribution network. A real network was used as a case study. The ten different types of line faults including single line-to-ground, line-to-line, double line-to-ground, and three-phase faults were investigated. The designed system has 89% accuracy for fault type identification. It also has 93% accuracy for fault location. The results indicate that the proposed technique is effective in detecting, classifying, and locating low impedance faults.

  4. Power transformer fault diagnosis model based on rough set theory with fuzzy representation

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Inthis paper,rough-fuzzy hybridizationis usedfor synthetic fault diagnosis of power transfor mers.Each diagnostic method s result is represented by itsfuzzy membership with respect to three credible de-gree sets as:Low,Middle,orHigh,thereby gen-erating a fuzzy granulation of the feature space thatcontains granules with otherwiseill-defined bounda-ries.Discernibility of the granulated objects inter ms of attributes is thencomputedinthe for mof adiscernibility matrix.Using rough set theory,anumber of decision...

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

    DEFF Research Database (Denmark)

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

    2015-01-01

    . 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...... tolerant control of wind turbines using a benchmark model. In this paper, the fault diagnosis scheme is improved and integrated with a fault accommodation scheme which enables and disables the individual pitch algorithm based on the fault detection. In this way, the blade and tower loads are not increased...

  6. Actuator Fault Detection and Diagnosis for Quadrotors

    NARCIS (Netherlands)

    Lu, P.; Van Kampen, E.-J.; Yu, B.

    2014-01-01

    This paper presents a method for fault detection and diagnosis of actuator loss of effectiveness for a quadrotor helicopter. This paper not only considers the detection of the actuator loss of effectiveness faults, but also addresses the diagnosis of the faults. The detection and estimation of the f

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

    Science.gov (United States)

    Lai, Zhi-hui; Leng, Yong-gang

    2016-12-01

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

  8. Fault Diagnosis of Plunger Pump in Truck Crane Based on Relevance Vector Machine with Particle Swarm Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Wenliao Du

    2013-01-01

    Full Text Available Promptly and accurately dealing with the equipment breakdown is very important in terms of enhancing reliability and decreasing downtime. A novel fault diagnosis method PSO-RVM based on relevance vector machines (RVM with particle swarm optimization (PSO algorithm for plunger pump in truck crane is proposed. The particle swarm optimization algorithm is utilized to determine the kernel width parameter of the kernel function in RVM, and the five two-class RVMs with binary tree architecture are trained to recognize the condition of mechanism. The proposed method is employed in the diagnosis of plunger pump in truck crane. The six states, including normal state, bearing inner race fault, bearing roller fault, plunger wear fault, thrust plate wear fault, and swash plate wear fault, are used to test the classification performance of the proposed PSO-RVM model, which compared with the classical models, such as back-propagation artificial neural network (BP-ANN, ant colony optimization artificial neural network (ANT-ANN, RVM, and support vectors, machines with particle swarm optimization (PSO-SVM, respectively. The experimental results show that the PSO-RVM is superior to the first three classical models, and has a comparative performance to the PSO-SVM, the corresponding diagnostic accuracy achieving as high as 99.17% and 99.58%, respectively. But the number of relevance vectors is far fewer than that of support vector, and the former is about 1/12–1/3 of the latter, which indicates that the proposed PSO-RVM model is more suitable for applications that require low complexity and real-time monitoring.

  9. Machinery Fault Diagnosis Using Two-Channel Analysis Method Based on Fictitious System Frequency Response Function

    Directory of Open Access Journals (Sweden)

    Kihong Shin

    2015-01-01

    Full Text Available Most existing techniques for machinery health monitoring that utilize measured vibration signals usually require measurement points to be as close as possible to the expected fault components of interest. This is particularly important for implementing condition-based maintenance since the incipient fault signal power may be too small to be detected if a sensor is located further away from the fault source. However, a measurement sensor is often not attached to the ideal point due to geometric or environmental restrictions. In such a case, many of the conventional diagnostic techniques may not be successfully applicable. In this paper, a two-channel analysis method is proposed to overcome such difficulty. It uses two vibration signals simultaneously measured at arbitrary points in a machine. The proposed method is described theoretically by introducing a fictitious system frequency response function. It is then verified experimentally for bearing fault detection. The results show that the suggested method may be a good alternative when ideal points for measurement sensors are not readily available.

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

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2015-12-01

    Full Text Available 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 based on wavelet time-frequency entropy (WTFE and one-class support vector machine (OCSVM is proposed. In this method; the S-transform (ST is proposed to analyze the energy time-frequency distribution of HVCBs’ vibration signals. Then; WTFE is selected as the feature vector that reflects the information characteristics of vibration signals in the time and frequency domains. OCSVM is used for judging whether a mechanical fault of HVCBs has occurred or not. In order to improve the fault detection accuracy; a particle swarm optimization (PSO algorithm is employed to optimize the parameters of OCSVM; including the window width of the kernel function and error limit. If the mechanical fault is confirmed; a support vector machine (SVM-based classifier will be used to recognize the fault type. The experiments carried on a real SF6 HVCB demonstrated the improved effectiveness of the new approach.

  11. Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Wen-An Yang

    2016-01-01

    Full Text Available The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings.

  12. Realization of Fault Diagnosis for ATS Based on Fault Tree Analysis%ATS故障树法故障诊断功能的实现

    Institute of Scientific and Technical Information of China (English)

    任献彬; 姜志森

    2013-01-01

    In absence of the transcendental experience of fault diagnosis,fault tree is an effective method which can be easily realized in engineering.With analyzing and inducing the association relationship between test items and SRUs,the expressing method of fault diagnosis information in fault tree database is obtained.The structure of fault tree,the data format of database,and fault diagnosis reasoning procedure are proposed,and the fault diagnosis system for ATS is designed.In this method,fault tree database can be designed easily,fault diagnosis procedure can be expressed definitely.This method has been applied in two types of ATS,both fault isolation rate and false alarm rate all meet the system needs.%当缺乏故障诊断先验知识时,故障树法是工程上易于实现的一种有效的故障诊断方法.通过分析、归纳测试项目与SRU的关联关系,得出了故障树模型中故障诊断知识的表达方式.以Access数据库为基础,提出了故障树的结构、数据组织形式及故障诊断的推理方法,并设计了适用于自动测试系统的故障诊断系统.该方法具有故障诊断推理过程表达明确、树模型易于建立等优点,已应用到两型机载电子设备的故障诊断中,故障隔离率和虚警率都达到了设计要求.

  13. Rolling Bearing Fault Diagnosis Based on ELCD Permutation Entropy and RVM

    Directory of Open Access Journals (Sweden)

    Jiang Xingmeng

    2016-01-01

    Full Text Available Aiming at the nonstationary characteristic of a gear fault vibration signal, a recognition method based on permutation entropy of ensemble local characteristic-scale decomposition (ELCD and relevance vector machine (RVM is proposed. First, the vibration signal was decomposed by ELCD; then a series of intrinsic scale components (ISCs were obtained. Second, according to the kurtosis of ISCs, principal ISCs were selected and then the permutation entropy of principal ISCs was calculated and they were combined into a feature vector. Finally, the feature vectors were input in RVM classifier to train and test and identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnose four kinds of working condition, and the effect is better than local characteristic-scale decomposition (LCD method.

  14. Fault Diagnosis in HVAC Chillers

    Science.gov (United States)

    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.

  15. Fault Diagnosis in HVAC Chillers

    Science.gov (United States)

    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.

  16. Power Transformer Partial Discharge Fault Diagnosis Based on Multidimensional Feature Region

    Directory of Open Access Journals (Sweden)

    Rong Jia

    2016-01-01

    Full Text Available Effectively extracting power transformer partial discharge (PD signals feature is of great significance for monitoring power transformer insulation condition. However, there has been lack of practical and effective extraction methods. For this reason, this paper suggests a novel method for the PD signal feature extraction based on multidimensional feature region. Firstly, in order to better describe differences in each frequency band of fault signals, empirical mode decomposition (EMD and Hilbert-Huang transform (HHT band-pass filter wave for raw signal is carried out. And the component of raw signals on each frequency band can be obtained. Secondly, the sample entropy value and the energy value of each frequency band component are calculated. Using the difference of each frequency band energy and complexity, signals feature region is established by the multidimensional energy parameters and the multidimensional sample entropy parameters to describe PD signals multidimensional feature information. Finally, partial discharge faults are classified by sphere-structured support vector machines algorithm. The result indicates that this method is able to identify and classify different partial discharge faults.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  18. Gearbox fault diagnosis using adaptive zero phase time-varying filter based on multi-scale chirplet sparse signal decomposition

    Science.gov (United States)

    Wu, Chunyan; Liu, Jian; Peng, Fuqiang; Yu, Dejie; Li, Rong

    2013-07-01

    When used for separating multi-component non-stationary signals, the adaptive time-varying filter(ATF) based on multi-scale chirplet sparse signal decomposition(MCSSD) generates phase shift and signal distortion. To overcome this drawback, the zero phase filter is introduced to the mentioned filter, and a fault diagnosis method for speed-changing gearbox is proposed. Firstly, the gear meshing frequency of each gearbox is estimated by chirplet path pursuit. Then, according to the estimated gear meshing frequencies, an adaptive zero phase time-varying filter(AZPTF) is designed to filter the original signal. Finally, the basis for fault diagnosis is acquired by the envelope order analysis to the filtered signal. The signal consisting of two time-varying amplitude modulation and frequency modulation(AM-FM) signals is respectively analyzed by ATF and AZPTF based on MCSSD. The simulation results show the variances between the original signals and the filtered signals yielded by AZPTF based on MCSSD are 13.67 and 41.14, which are far less than variances (323.45 and 482.86) between the original signals and the filtered signals obtained by ATF based on MCSSD. The experiment results on the vibration signals of gearboxes indicate that the vibration signals of the two speed-changing gearboxes installed on one foundation bed can be separated by AZPTF effectively. Based on the demodulation information of the vibration signal of each gearbox, the fault diagnosis can be implemented. Both simulation and experiment examples prove that the proposed filter can extract a mono-component time-varying AM-FM signal from the multi-component time-varying AM-FM signal without distortion.

  19. A combined method for triplex pump fault diagnosis based on wavelet transform, fuzzy logic and neuro-networks

    Science.gov (United States)

    Kong, Fansen; Chen, Ruheng

    2004-01-01

    A new combined method based on wavelet transformation, fuzzy logic and neuro-networks is proposed for fault diagnosis of a triplex. The failure characteristics of the fluid- and dynamic-end can be divided into wavelet transform in different scales at the same time (in: Jun Zhu et al. (Eds.), Proceedings of an International Conference on Condition Monitoring. National Defense Industry Press, Beijing, 1997, pp. 271-275). Therefore, the characteristic variables can be constructed making use of the coefficients of Edgeworth asymptotic spectrum expansion formula and fuzzified to train the neuro-network to identify the faults of fluid- and dynamic-end of triplex pump in fuzzy domain. Tests indicate that the information of wavelet transformation in scale 2 is related to the meshing state of the gear and the information in scales 4 and 5 is related to the running state of fluid-end. Good agreement between analytical and experimental results has been obtained.

  20. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships.

    Science.gov (United States)

    Silva, Andre A; Gupta, Shalabh; Bazzi, Ali M; Ulatowski, Arthur

    2017-09-21

    Electric machines and drives have enjoyed extensive applications in the field of electric vehicles (e.g., electric ships, boats, cars, and underwater vessels) due to their ease of scalability and wide range of operating conditions. This stems from their ability to generate the desired torque and power levels for propulsion under various external load conditions. However, as with the most electrical systems, the electric drives are prone to component failures that can degrade their performance, reduce the efficiency, and require expensive maintenance. Therefore, for safe and reliable operation of electric vehicles, there is a need for automated early diagnostics of critical failures such as broken rotor bars and electrical phase failures. In this regard, this paper presents a fault diagnosis methodology for electric drives in electric ships. This methodology utilizes the two-dimensional, i.e. scale-shift, wavelet transform of the sensor data to filter optimal information-rich regions which can enhance the diagnosis accuracy as well as reduce the computational complexity of the classifier. The methodology was tested on sensor data generated from an experimentally validated simulation model of electric drives under various cruising speed conditions. The results in comparison with other existing techniques show a high correct classification rate with low false alarm and miss detection rates. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  1. Application of extension method to fault diagnosis of transformer

    Institute of Scientific and Technical Information of China (English)

    DENG Hong-gui; CAO Jian; LUO An; XIA Xiang-yang

    2007-01-01

    A novel extension diagnosis method was proposed for enhancing the diagnosis ability of the conventional dissolved gas analysis. Based on the extension theory a matter-element model was established for qualitatively and quantitatively describing the fault diagnosis problem of power transformers. The degree of relation based on the dependent functions WaS employed to determine then ature and the grade of the faults in a transformer system.And the proposed method was verified with the experimental data.The results show that accuracy rate of the diagnosis method exceeds 90% and two kinds of faults call be detected at the same time.

  2. Research on Gear-box Fault Diagnosis Method Based on Adjusting-learning-rate PSO Neural Network

    Institute of Scientific and Technical Information of China (English)

    PAN Hong-xia; MA Qing-feng

    2006-01-01

    Based on the research of Particle Swarm Optimization (PSO) learning rate, two learning rates are changed linearly with velocity-formula evolving in order to adjust the proportion of social part and cognitional part; then the methods are applied to BP neural network training, the convergence rate is heavily accelerated and locally optional solution is avoided. According to actual data of two levels compound-box in vibration lab, signals are analyzed and their characteristic values are abstracted. By applying the trained BP neural networks to compound-box fault diagnosis, it is indicated that the methods are sound effective.

  3. Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution

    Science.gov (United States)

    Li, Jimeng; Li, Ming; Zhang, Jinfeng

    2017-08-01

    Rolling bearings are the key components in the modern machinery, and tough operation environments often make them prone to failure. However, due to the influence of the transmission path and background noise, the useful feature information relevant to the bearing fault contained in the vibration signals is weak, which makes it difficult to identify the fault symptom of rolling bearings in time. Therefore, the paper proposes a novel weak signal detection method based on time-delayed feedback monostable stochastic resonance (TFMSR) system and adaptive minimum entropy deconvolution (MED) to realize the fault diagnosis of rolling bearings. The MED method is employed to preprocess the vibration signals, which can deconvolve the effect of transmission path and clarify the defect-induced impulses. And a modified power spectrum kurtosis (MPSK) index is constructed to realize the adaptive selection of filter length in the MED algorithm. By introducing the time-delayed feedback item in to an over-damped monostable system, the TFMSR method can effectively utilize the historical information of input signal to enhance the periodicity of SR output, which is beneficial to the detection of periodic signal. Furthermore, the influence of time delay and feedback intensity on the SR phenomenon is analyzed, and by selecting appropriate time delay, feedback intensity and re-scaling ratio with genetic algorithm, the SR can be produced to realize the resonance detection of weak signal. The combination of the adaptive MED (AMED) method and TFMSR method is conducive to extracting the feature information from strong background noise and realizing the fault diagnosis of rolling bearings. Finally, some experiments and engineering application are performed to evaluate the effectiveness of the proposed AMED-TFMSR method in comparison with a traditional bistable SR method.

  4. Fault Diagnosis for Hydraulic Oil Pump Vehicle Based on Fuzzy Theory%基于模糊理论的液压油泵车故障诊断

    Institute of Scientific and Technical Information of China (English)

    张来丰; 朱张青

    2013-01-01

      针对YYBC-2型液压油泵车的故障诊断需求和现有诊断方法存在的问题,本文基于模糊理论,设计了对油泵车液压系统的故障诊断系统,给出了具体实现方法。最后,通过诊断实例证明了本文方法的有效性。%According to the requirement of fault diagnosis for YYBC-2 hydraulic oil pump vehicle and the existing problems, fault diagnosis for hydraulic system of hydraulic oil pump vehicle is presented based on fuzzy theory, and the method to accomplish fault detection is discussed. Final y hydraulic system of YYBC-2 hydraulic oil pump vehicle is studied to explain that the fault diagnosis based on fuzzy theory is effective.

  5. Rough set and radial basis function neural network based insulation data mining fault diagnosis for power transformer

    Institute of Scientific and Technical Information of China (English)

    DONG Li-xin; XIAO Deng-ming Xiao; LIU Yi-lu

    2007-01-01

    Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose "confidence" and "support" is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose "confidence and support" is lower than requirement,are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e. , as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.

  6. Layered clustering multi-fault diagnosis for hydraulic piston pump

    Science.gov (United States)

    Du, Jun; Wang, Shaoping; Zhang, Haiyan

    2013-04-01

    Efficient diagnosis is very important for improving reliability and performance of aircraft hydraulic piston pump, and it is one of the key technologies in prognostic and health management system. In practice, due to harsh working environment and heavy working loads, multiple faults of an aircraft hydraulic pump may occur simultaneously after long time operations. However, most existing diagnosis methods can only distinguish pump faults that occur individually. Therefore, new method needs to be developed to realize effective diagnosis of simultaneous multiple faults on aircraft hydraulic pump. In this paper, a new method based on the layered clustering algorithm is proposed to diagnose multiple faults of an aircraft hydraulic pump that occur simultaneously. The intensive failure mechanism analyses of the five main types of faults are carried out, and based on these analyses the optimal combination and layout of diagnostic sensors is attained. The three layered diagnosis reasoning engine is designed according to the faults' risk priority number and the characteristics of different fault feature extraction methods. The most serious failures are first distinguished with the individual signal processing. To the desultory faults, i.e., swash plate eccentricity and incremental clearance increases between piston and slipper, the clustering diagnosis algorithm based on the statistical average relative power difference (ARPD) is proposed. By effectively enhancing the fault features of these two faults, the ARPDs calculated from vibration signals are employed to complete the hypothesis testing. The ARPDs of the different faults follow different probability distributions. Compared with the classical fast Fourier transform-based spectrum diagnosis method, the experimental results demonstrate that the proposed algorithm can diagnose the multiple faults, which occur synchronously, with higher precision and reliability.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  8. The Fault Diagnosis of Bora Engine CH Emissions based on Neural network

    Directory of Open Access Journals (Sweden)

    Tie Wang

    2012-12-01

    Full Text Available Along with an increase of the automobile possession quantity, the air pollution caused by the pollutant of the automobile emissions is serious day by day. The emission diagnosis become the important technology for guaranteeing the human sustainable development. The article introduces the reasons of CH excessive emissions in vehicle discharge of pollutants, the impact which CH excessive emissions have on our environment, expounds the advantages of SOM neural network and BP neural network, briefly describes why these two tools are applied to the project. In the article, diagnostic procedures are written by MATLAB software, parameters are analyzed which influence CH emission of a particular model engine. In the article, Volkswagen Bora acts as experimental models, the data stream is extracted, then the data are classified, trained and operated, the diagnostic results and diagnostic accuracy are finally obtained. Through SOM, the accuracy rate of fault sample data diagnostic is 73.3% and BP is 65.1%. The results of sample show that: SOM neural network can quickly and accurately diagnose the reasons of the CH excessive emissions in vehicle discharge of pollutants.

  9. Fault Diagnosis and Reliability Analysis Using Fuzzy Logic Method

    Institute of Scientific and Technical Information of China (English)

    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.

  10. Research and application of hierarchical model for multiple fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    An Ruoming; Jiang Xingwei; Song Zhengji

    2005-01-01

    Computational complexity of complex system multiple fault diagnosis is a puzzle at all times. Based on the well-known Mozetic's approach, a novel hierarchical model-based diagnosis methodology is put forward for improving efficiency of multi-fault recognition and localization. Structural abstraction and weighted fault propagation graphs are combined to build diagnosis model. The graphs have weighted arcs with fault propagation probabilities and propagation strength. For solving the problem of coupled faults, two diagnosis strategies are used: one is the Lagrangian relaxation and the primal heuristic algorithms; another is the method of propagation strength. Finally, an applied example shows the applicability of the approach and experimental results are given to show the superiority of the presented technique.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  12. Fault Diagnosis of Valve Clearance in Diesel Engine Based on BP Neural Network and Support Vector Machine

    Institute of Scientific and Technical Information of China (English)

    毕凤荣; 刘以萍

    2016-01-01

    Based on wavelet packet transformation(WPT), genetic algorithm(GA), back propagation neural net-work(BPNN)and support vector machine(SVM), a fault diagnosis method of diesel engine valve clearance is pre-sented. With power spectral density analysis, the characteristic frequency related to the engine running conditions can be extracted from vibration signals. The biggest singular values(BSV)of wavelet coefficients and root mean square(RMS)values of vibration in characteristic frequency sub-bands are extracted at the end of third level de-composition of vibration signals, and they are used as input vectors of BPNN or SVM. To avoid being trapped in local minima, GA is adopted. The normal and fault vibration signals measured in different valve clearance condi-tions are analyzed. BPNN, GA back propagation neural network (GA-BPNN), SVM and GA-SVM are applied to the training and testing for the extraction of different features, and the classification accuracies and training time are compared to determine the optimum fault classifier and feature selection. Experimental results demonstrate that the proposed features and classification algorithms give classification accuracy of 100%.

  13. Fault diagnosis and fault-tolerant control of photovoltaic micro-inverter

    Institute of Scientific and Technical Information of China (English)

    李舟; 彭涛; 张鹏飞; 韩华; 杨建

    2016-01-01

    An observer-based fault diagnosis method and a fault tolerant control for open-switch fault and current sensor fault are proposed for interleaved flyback converters of a micro-inverter system. First, based on the topology of a grid-connected micro-inverter, a mathematical model of the flyback converters is established. Second, a state observer is applied to estimate the currents online and generate corresponding residuals. The fault is diagnosed by comparing the residuals with the thresholds. Finally, a fault-tolerant control that consists of a fault-tolerant topology for the faulty switch and a simple software redundancy control for the faulty current sensor, is proposed to achieve a fault-tolerant operation. The feasibility and effectiveness of the proposed method has been verified by simulation and experimental results.

  14. Control and fault diagnosis based sliding mode observer of a multicellular converter: Hybrid approach

    KAUST Repository

    Benzineb, Omar

    2013-01-01

    In this article, the diagnosis of a three cell converter is developed. The hybrid nature of the system represented by the presence of continuous and discrete dynamics is taken into account in the control design. The idea is based on using a hybrid control and an observer-type sliding mode to generate residuals from the observation errors of the system. The simulation results are presented at the end to illustrate the performance of the proposed approach. © 2013 FEI STU.

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

    Science.gov (United States)

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

    2012-01-01

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

  16. Novel Fault Diagnosis Scheme for HVDC System via ESO

    Institute of Scientific and Technical Information of China (English)

    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.

  17. Application of General fractal Dimension to Coupling Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Directory of Open Access Journals (Sweden)

    Rainer Nordmann

    2004-01-01

    compared to state-of-the-art diagnostic tools which are only based on the measurement of the systems outputs, i.e., displacements. In this article, the different steps of the model-based diagnosis, which are modeling, generation of significant features, respectively symptoms, fault detection, and the diagnosis procedure itself are presented and in particular, it is shown how an exemplary fault is detected and identified.

  19. Fault diagnosis of active magnetic bearings based on Gaussian GLRT detector

    DEFF Research Database (Denmark)

    Nagel, Leon; Galeazzi, Roberto; Voigt, Andreas Jauernik

    2016-01-01

    Active magnetic bearings are progressively replacing conventional bearings in many industrial applications, particularly in the energy sector. Magnetic bearings have many advantages such as contactless support and clean operation; however their use also poses some challenges connected...... to their inherent open loop instability. Occurrence of faults in one or more components of an active magnetic bearing may lead to loss of control of the rotor. Timely detection and isolation of faults in an active magnetic bearing could prevent hazardous system behaviour by enabling proper reconfiguration...... of the control system. A structural model of the bearing-rotor system is presented and used to perform a detectability and isolability analysis of faults in the magnetic actuator. Structural detectability and group-wise isolability is concluded for single and multiple faults in the actuators. A Gaussian...

  20. A robust algorithm based on a failure-sensitive matrix for fault diagnosis of power systems: an application on power transformers

    OpenAIRE

    2015-01-01

    In this paper, a robust algorithm for fault diagnosis of power system equipment based on a failure-sensitive matrix (FSM) is presented. The FSM is a dynamic matrix structure updated by multiple measurements (online) and test results (offline) on the systems. The algorithm uses many different artificial intelligence and expert system methods for adaptively detecting the location of faults, emerging failures, and causes of failures. In this algorithm, all data obtained from the power transforme...

  1. Design of Online Monitoring and Fault Diagnosis System for Belt Conveyors Based on Wavelet Packet Decomposition and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Wei Li

    2013-01-01

    Full Text Available Belt conveyors are the equipment widely used in coal mines and other manufacturing factories, whose main components are a number of idlers. The faults of belt conveyors can directly influence the daily production. In this paper, a fault diagnosis method combining wavelet packet decomposition (WPD and support vector machine (SVM is proposed for monitoring belt conveyors with the focus on the detection of idler faults. Since the number of the idlers could be large, one acceleration sensor is applied to gather the vibration signals of several idlers in order to reduce the number of sensors. The vibration signals are decomposed with WPD, and the energy of each frequency band is extracted as the feature. Then, the features are employed to train an SVM to realize the detection of idler faults. The proposed fault diagnosis method is firstly tested on a testbed, and then an online monitoring and fault diagnosis system is designed for belt conveyors. An experiment is also carried out on a belt conveyor in service, and it is verified that the proposed system can locate the position of the faulty idlers with a limited number of sensors, which is important for operating belt conveyors in practices.

  2. Robust observer-based fault diagnosis for nonlinear systems using Matlab

    CERN Document Server

    Zhang, Jian; Nguang, Sing Kiong

    2016-01-01

    This book introduces several observer-based methods, including: • the sliding-mode observer • the adaptive observer • the unknown-input observer and • the descriptor observer method for the problem of fault detection, isolation and estimation, allowing readers to compare and contrast the different approaches. The authors present basic material on Lyapunov stability theory, H¥ control theory, sliding-mode control theory and linear matrix inequality problems in a self-contained and step-by-step manner. Detailed and rigorous mathematical proofs are provided for all the results developed in the text so that readers can quickly gain a good understanding of the material. MATLAB® and Simulink® codes for all the examples, which can be downloaded from http://extras.springer.com, enable students to follow the methods and illustrative examples easily. The systems used in the examples make the book highly relevant to real-world problems in industrial control engineering and include a seventh-order aircraft mod...

  3. Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP.

    Science.gov (United States)

    Ruiz, Magda; Sin, Gürkan; Berjaga, Xavier; Colprim, Jesús; Puig, Sebastià; Colomer, Joan

    2011-01-01

    The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation.

  4. A fuzzy-based approach for open-transistor fault diagnosis in voltage-source inverter induction motor drives

    Science.gov (United States)

    Zhang, Jianghan; Luo, Hui; Zhao, Jin; Wu, Feng

    2015-02-01

    This paper develops a novel method for the detection and isolation of open-transistor faults in voltage-source inverters feeding induction motors. Based on analyzing the load currents trajectories after Concordia transformation, six diagnostic signals each of which indicates a certain switch are extracted and a fuzzy rule base is designed to perform fuzzy reasoning in order to detect and isolate 21 fault modes including single- and double-transistor faults. In addition, the fuzzy rules are rearranged and each of them is set to a reasonable value representing the fault modes. The simulation and experiment are carried out to demonstrate the effectiveness of the proposed fuzzy approach.

  5. Intelligent fault isolation and diagnosis for communication satellite systems

    Science.gov (United States)

    Tallo, Donald P.; Durkin, John; Petrik, Edward J.

    1992-01-01

    Discussed here is a prototype diagnosis expert system to provide the Advanced Communication Technology Satellite (ACTS) System with autonomous diagnosis capability. The system, the Fault Isolation and Diagnosis EXpert (FIDEX) system, is a frame-based system that uses hierarchical structures to represent such items as the satellite's subsystems, components, sensors, and fault states. This overall frame architecture integrates the hierarchical structures into a lattice that provides a flexible representation scheme and facilitates system maintenance. FIDEX uses an inexact reasoning technique based on the incrementally acquired evidence approach developed by Shortliffe. The system is designed with a primitive learning ability through which it maintains a record of past diagnosis studies.

  6. Kalman filter based fault diagnosis of networked control system with white noise

    Institute of Scientific and Technical Information of China (English)

    Yanwei WANG; Ying ZHENG

    2005-01-01

    The networked control system NCS is regarded as a sampled control system with output time-variant delay.White noise is considered in the model construction of NCS.By using the Kalman filter theory to compute the filter parameters,a Kalman filter is constructed for this NCS.By comparing the output of the filter and the practical system,a residual is generated to diagnose the sensor faults and the actuator faults.Finally,an example is given to show the feasibility of the approach.

  7. 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

    Institute of Scientific and Technical Information of China (English)

    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。

  8. A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery.

    Science.gov (United States)

    Liu, Zhiwen; He, Zhengjia; Guo, Wei; Tang, Zhangchun

    2016-03-01

    In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.

  9. Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

    Directory of Open Access Journals (Sweden)

    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.

  10. 基于条件规则与故障树法的燃气轮机故障诊断%Gas turbine engine fault diagnosis based on conditions of rule-based and fault tree

    Institute of Scientific and Technical Information of China (English)

    尚文; 王维民; 齐鹏逸; 崔津; 曾咏奎

    2013-01-01

    Aiming at all kinds of the gas turbine fault diagnosis problems ,a comprehensive technology of the rule-based and fault tree method was investigated in the gas turbine fault diagnosis research. On the base of the established fault tree of gas turbine, the typical fault cases and maintenance experience were summarized, the based on condition the rules of logic reasoning model was established, the fault analysis principle of gas turbine based on signal processing was utilized, the certain conditions rules were increased in the middle of the fault tree events and bottom events, the physical and logical judge were judged to determine the fault tree on each branch diagnosis choice, thus every step of the fault diagnosis analysis were clear ,the accurate fault causes and failure parts were concluded in the end. For the gas turbine generating unit rotor vibration fault example of an offshore oil operation area, the rapid and accurate the root causes were analyzed by the based on rules conditiongs of fault tree method on the foundation of online monitoring ,The results indicate that the method is convenient in maintenance and technical staff to grasp, it can be widely used in gas turbine generating unit reliability maintenance field.%针对燃气轮机各类故障的诊断问题,将条件规则与故障树法相综合的诊断技术应用到燃气轮机的故障诊断研究中.在建立燃气轮机失效故障树的基础上,通过归纳总结典型的故障案例和维修经验,构建了基于条件规则的逻辑推理模型,利用基于信号处理技术的燃气轮机故障分析原理,在故障树的中间事件和底端事件上,增加了具体故障分析的条件规则,并进行了物理和逻辑判断,以确定故障树每个分支的诊断选择,从而明确了每一步的故障诊断分析,最终得出了精确的故障原因和故障部位.结合某海上石油作业区燃气轮机发电机组的转子振动故障实例,在进行燃气轮

  11. Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

    Science.gov (United States)

    Mrugalski, Marcin; Luzar, Marcel; Pazera, Marcin; Witczak, Marcin; Aubrun, Christophe

    2016-03-01

    The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks.

  12. Bevel Gearbox Fault Diagnosis using Vibration Measurements

    Directory of Open Access Journals (Sweden)

    Hartono Dennis

    2016-01-01

    Full Text Available The use of vibration measurementanalysis has been proven to be effective for gearbox fault diagnosis. However, the complexity of vibration signals observed from a gearbox makes it difficult to accurately detectfaults in the gearbox. This work is based on a comparative studyof several time-frequency signal processing methods that can be used to extract information from transient vibration signals containing useful diagnostic information. Experiments were performed on a bevel gearbox test rig using vibration measurements obtained from accelerometers. Initially, thediscrete wavelet transform was implementedfor vibration signal analysis to extract the frequency content of signal from the relevant frequency region. Several time-frequency signal processing methods werethen incorporated to extract the fault features of vibration signals and their diagnostic performances were compared. It was shown thatthe Short Time Fourier Transform (STFT could not offer a good time resolution to detect the periodicity of the faulty gear tooth due the difficulty in choosing an appropriate window length to capture the impulse signal. The Continuous Wavelet Transform (CWT, on the other hand, was suitable to detection of vibration transients generated by localized fault from a gearbox due to its multi-scale property. However, both methods still require a thorough visual inspection. In contrast, it was shown from the experiments that the diagnostic method using the Cepstrumanalysis could provide a direct indication of the faulty tooth without the need of a thorough visual inspection as required by CWT and STFT.

  13. Transformer fault diagnosis using continuous sparse autoencoder.

    Science.gov (United States)

    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.

  14. A Neural Network Appraoch to Fault Diagnosis in Analog Circuits

    Institute of Scientific and Technical Information of China (English)

    尉乃红; 杨士元; 等

    1996-01-01

    Thia paper presents a neural network based fault diagnosis approach for analog circuits,taking the tolerances of circuit elements into account.Specifically,a normalization rule of input information,a pseudo-fault domain border(PFDB)pattern selection method and a new output error function are proposed for training the backpropagation(BP) network to be a fault diagnoser.Experimental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy,and provides at least an order-of-magnitude improvement in post-fault diagnostic speed.

  15. A Fault Diagnosis Approach for Broken Rotor Bars Based on EMD and Envelope Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jian-wen; ZHU Ning-hui; YANG Li; YAO Qi; LU Qing

    2007-01-01

    Empirical Mode Decomposition (EMD) used to deal with non-linear and non-stable signals, is a time-frequency analytical method that has been developed recently. In this paper the EMD method is used to filter the noise from the stator current signal that arises when rotor bars break. Then a Hilbert Transform is used to extract the envelope from the filtered signal. With the EMD method again, the frequency band containing the fault characteristic-frequency components, 2sf, can be extracted from the signal's envelope. The last step is to use a Fast Fourier Transform (FFT) method to extract the fault characteristic frequency. This frequency can be detected in actual data from a faulty motor, as shown by example. Compared to the Extend Park Vector method this method is proved to be more sensitive under light motor load.

  16. Knowledge-driven board-level functional fault diagnosis

    CERN Document Server

    Ye, Fangming; Chakrabarty, Krishnendu; Gu, Xinli

    2017-01-01

    This book provides a comprehensive set of characterization, prediction, optimization, evaluation, and evolution techniques for a diagnosis system for fault isolation in large electronic systems. Readers with a background in electronics design or system engineering can use this book as a reference to derive insightful knowledge from data analysis and use this knowledge as guidance for designing reasoning-based diagnosis systems. Moreover, readers with a background in statistics or data analytics can use this book as a practical case study for adapting data mining and machine learning techniques to electronic system design and diagnosis. This book identifies the key challenges in reasoning-based, board-level diagnosis system design and presents the solutions and corresponding results that have emerged from leading-edge research in this domain. It covers topics ranging from highly accurate fault isolation, adaptive fault isolation, diagnosis-system robustness assessment, to system performance analysis and evalua...

  17. Fault Diagnosis of an Intelligent Building Facility Using Bayesian Networks

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qi-ding; XU Jin-yu; BAI Er-lei

    2008-01-01

    There is great significance to diagnose the fault of an intelligent building facility for fault controlling, repairing, eliminating and preventing. As an example, this paper established a Bayesian networks model for fault diagnosis of the refrigeration system of an intelligent building facility, gave the networks parameters, and analyzed the reasoning mechanism. Based on the model, some data was analyzed and diagnosed by adopting Bayesian networks reasoning platform GeNIe. The result shows that the diagnosis effect is more comprehensive and reasonable than the other method.

  18. A data structure and algorithm for fault diagnosis

    Science.gov (United States)

    Bosworth, Edward L., Jr.

    1987-01-01

    Results of preliminary research on the design of a knowledge based fault diagnosis system for use with on-orbit spacecraft such as the Hubble Space Telescope are presented. A candidate data structure and associated search algorithm from which the knowledge based system can evolve is discussed. This algorithmic approach will then be examined in view of its inability to diagnose certain common faults. From that critique, a design for the corresponding knowledge based system will be given.

  19. Least Squares Support Vector Machine Based Real-Time Fault Diagnosis Model for Gas Path Parameters of Aero Engines

    Institute of Scientific and Technical Information of China (English)

    WANG Xu-hui; HUANG Sheng-guo; WANG Ye; LIU Yong-jian; SHU Ping

    2009-01-01

    Least squares support vector machine (LS-SVM) is applied in gas path fault diagnosis for aero engines.Firstly,the deviation data of engine cruise are analyzed.Then,model selection is conducted using pattern search method.Finally,by decoding aircraft communication addressing and reporting system (ACARS) report,a real-time cruise data set is acquired,and the diagnosis model is adopted to process data.In contrast to the radial basis function (RBF) neutral network,LS-SVM is more suitable for real-time diagnosis of gas turbine engine.

  20. Fault Diagnosis and Fault Handling for Autonomous Aircraft

    DEFF Research Database (Denmark)

    Hansen, Søren

    Unmanned Aerial vehicles (UAVs) or drones are used increasingly for missions where piloted aircraft are unsuitable. The unmanned aircraft has a number of advantages with respect to size, weight and manoeuvrability that makes it possible for them to solve tasks that an aircraft previously has been...... that the fault is discovered in time such that appropriate actions can be taken. That could either be the aircraft controlling computer taking the fault into account or a human operator that intervenes. Detection of faults that occur during flight is exactly the subject of this thesis. Safety towards faults...... to another type of aircraft with different parameters. Amongst the main findings of this research project is a method to handle faults on the UAV’s pitot tube, which measures the aircraft speed. A set of software redundancies based on GPS velocity information and engine thrust are used to detect abnormal...

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

    DEFF Research Database (Denmark)

    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...

  2. Gear Fault Diagnosis Based on Narrowband Demodulation with Frequency Shift and Spectrum Edit

    Directory of Open Access Journals (Sweden)

    Yu Guo

    2016-09-01

    Full Text Available To address the difficulties on the vibration feature extraction of gear localized faults for rotating machinery under varying speed conditions, an improved narrowband demodulation method with spectrum edit and frequency shift is proposed in the paper. The vibration signal is acquired and resampled at constant angle increments at first, by which the non-stationary signal is converted into a quasi-stationary signal in the angular domain to reduce the distortions caused by the speed fluctuations. Subsequently, the signal in the angular domain is processed by a synchronous average algorithm, where the noises can be eliminated effectively and the order components corresponding to the gear faults become prominent. Finally, the narrowband demodulation scheme with the spectrum edit and frequency shift is applied on the averaged signal. By using the spectrum edit, most of unconcerned components can be filtered out effectively. Moreover, the frequency shift property of the Fourier transform is employed in the proposed demodulation scheme to obtain a better phase demodulation result. Simulations and experiments support the proposed scheme positively.

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

    Institute of Scientific and Technical Information of China (English)

    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 lift was discussed, and the expert system developed was realized on the VPN virtual network. The system was applied to the Gaobaozhou ship lift project, and it ran successfully.

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

    Institute of Scientific and Technical Information of China (English)

    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...

  5. Node Grouping in System-Level Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Dafang; XIE Gaogang; MIN Yinghua

    2001-01-01

    With the popularization of network applications and multiprocessor systems,dependability of systems has drawn considerable attention. This paper presents a new technique of node grouping for system-level fault diagnosis to simplify the complexity of large system diagnosis. The technique transforms a complicated system to a group network, where each group may consist of many nodes that are either fault-free or faulty. It is proven that the transformation leads to a unique group network to ease system diagnosis. Then it studies systematically one-step t-faults diagnosis problem based on node grouping by means of the concept of independent point sets and gives a simple sufficient and necessary condition. The paper presents a diagnosis procedure for t-diagnosable systems. Furthermore, an efficient probabilistic diagnosis algorithm for practical applications is proposed based on the belief that most of the nodes in a system are fault-free. The result of software simulation shows that the probabilistic diagnosis provides high probability of correct diagnosis and low diagnosis cost, and is suitable for systems of any kind of topology.

  6. Fault Diagnosis of Autonomous Underwater Vehicles

    Directory of Open Access Journals (Sweden)

    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.

  7. Sub-module Short Circuit Fault Diagnosis in Modular Multilevel Converter Based on Wavelet Transform and Adaptive Neuro Fuzzy Inference System

    DEFF Research Database (Denmark)

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

    2015-01-01

    for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage...

  8. 基于SDG的压缩机故障诊断方法研究%Research on the Fault Diagnosis of Compressor Based on the SDG Method

    Institute of Scientific and Technical Information of China (English)

    聂银燕; 林晓焕

    2013-01-01

    SDG(Signed Directed Graph)是一种重要的定性建模技术.基于符号有向图(SDG)的故障诊断是故障诊断领域中的一个重要研究方向,具有很大的应用和发展前景.本文针对往复式压缩机,提出了一个综合性SDG故障诊断架构.该综合性故障诊断架构采用基于经验知识的建模方法,引入分级SDG建模的思想和基于假设-验证的双向推理方法.研究表明,该方法具有较强的故障诊断能力.%SDG is an important technology of building qualitative model. The signed directed graph (SDG) is an important research branch of the fault diagnosis field, with a lot of application and development prospect. For the reciprocating compressor, this paper puts forward a comprehensive SDG fault diagnosis structure. The comprehensive fault diagnosis structure takes the modeling method based on the experience knowledge, introduces the idea of hierarchical modeling and the bidirectional inference method based on assumptions — verification. The research shows that this method has strong ability of fault diagnosis.

  9. A Novel Fault Diagnosis Model for Bearing of Railway Vehicles Using Vibration Signals Based on Symmetric Alpha-Stable Distribution Feature Extraction

    Directory of Open Access Journals (Sweden)

    Yongjian Li

    2016-01-01

    Full Text Available Axle box bearings are the most critical mechanical components of railway vehicles. Condition monitoring is of great benefit to ensure the healthy status of bearings in the railway train. In this paper, a novel fault diagnosis model for axle box bearing based on symmetric alpha-stable distribution feature extraction and least squares support vector machines (LS-SVM using vibration signals is proposed which is conducted in three main steps. Firstly, fast nonlocal means is used for denoising and ensemble empirical mode decomposition is applied to extract fault feature information. Then a new statistical method of feature extraction, symmetric alpha-stable distribution, is employed to obtain representative features from intrinsic mode functions. Additionally, the hybrid fault feature sets are input into LS-SVM to identify the fault type. To enhance the performance of LS-SVM in the case of small-scale samples, Morlet wavelet kernel function is combined with LS-SVM for the classification of fault type and fault severity and the particle swarm optimization is used for the optimization of LS-WSVM parameters. Finally, the experimental results demonstrate that the proposed approach performs more effectively and robustly than the other methods in small-scale samples for fault detection and classification of railway vehicle bearings.

  10. A New Method for Rolling Element Bearing Fault Diagnosis Based on Cyclostationary Theory

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The theory of cyclostationary and its application are very important for the analysis and processing of a non-stationary signal. The paper introduces second-order cyclostationary statistics, with emphass on discussion of cyclic periodogram arithmetic. Com-paring the time smoothed cyclic periodogram with the frequency smoothed cyclic perio- dogram, we found that the former is more useful to extract the feature of cyclostationary signals. The method has been applied to analyze the vibration signal of a rolling element bearing measured on a test bench, and proved to be effective. Meanwhile, we have com pared it with traditional power spectral density analysis, and the results prove that the time smoothed cyclic periodogram is more available to diagnose the fault of a rolling ele ment bearing.

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

    Directory of Open Access Journals (Sweden)

    Xiao Yu

    2015-11-01

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

  12. IC Engine Valve Fault Diagnosis Method Based on RP and NMF%基于 RP与 NMF的内燃机气阀故障诊断方法

    Institute of Scientific and Technical Information of China (English)

    岳应娟; 孙钢; 蔡艳平; 陈茹

    2016-01-01

    针对传统的内燃机故障振动诊断方法,难以有效提取故障特征,诊断精度较低的缺点,提出一种基于递归图( recurrence plots,RP)与非负矩阵分解( non-negative matrix factorization ,NMF)的内燃机故障诊断新方法。该方法是利用图像的方法来进行故障诊断:首先通过递归图将采集到的内燃机缸盖表面振动信号生成图像,然后用非负矩阵对得到递归图进行特征参数提取,最后用分类器进行分类识别完成故障诊断。将该方法应用于气阀机构8种工况下振动信号诊断实例中,结果表明:该方法克服了传统的振动诊断方法从时域或频域进行分析时参数选取和故障特征提取的难题,直接将信号生成图像,对图像进行自适应特征参数提取、分类识别,能有效诊断出内燃机气阀机构故障,故障识别精度高,为内燃机振动诊断探索了一条新途径。%In view of the traditional fault diagnosis method of internal combustion engine is difficult to extract the fault feature effectively and the fault diagnosis accuracy is low .A new fault diagnosis method based on recur-rence plots ( RP) and non-negative matrix factorization ( NMF) is put forward .The method is using image method to fault diagnosis:first use RP method to the collected cylinder head of internal combustion engine surface vibration signals, the images will be generated;and then use NMF method to the above obtained images for feature parameter extraction;finally the classifieris used for identification and classification to complete fault diagnosis .8 typical faults of diesel engine ’ s valve train were simulated .The vibration acceleration signals , which were acquired from the cylinder head , were analyzed with the proposed method .The results showed that IC Engine valve fault diagnosis method based on RP and NMF overcomes parameters selection and fault feature extraction problems of the tradition

  13. Fault detection and diagnosis of permanent-magnetic DC motors based on current analysis and BP neural networks

    Institute of Scientific and Technical Information of China (English)

    LIU Man-lan; ZHU Chun-bo; WANG Tie-cheng

    2005-01-01

    In order to guarantee quality during mass serial production of motors, a convenient approach on how to detect and diagnose the faults of a permanent-magnetic DC motor based on armature current analysis and BP neural networks was presented in this paper. The fault feature vector was directly established by analyzing the armature current. Fault features were extracted from the current using various signal processing methods including Fourier analysis, wavelet analysis and statistical methods. Then an advanced BP neural network was used to finish decision-making and separate fault patterns. Finally, the accuracy of the method in this paper was verified by analyzing the mechanism of faults theoretically. The consistency between the experimental results and the theoretical analysis shows that four kinds of representative faults of low power permanent-magnetic DC motors can be diagnosed conveniently by this method. These four faults are brush fray, open circuit of components, open weld of components and short circuit between armature coils. This method needs fewer hardware instruments than the conventional method and whole procedures can be accomplished by several software packages developed in this paper.

  14. Fault diagnosis and condition monitoring of wind turbines

    DEFF Research Database (Denmark)

    Niemann, Hans Henrik; Poulsen, Niels Kjølstad; Mirzaei, Mahmood

    2017-01-01

    standard sensors on modern wind turbines, including moment sensors and rotor angle sensors. This approach will allow the method to be applied to existing wind turbines without any modifications. The method is based on the detection of asymmetries in the rotor system caused by changes or faults in the rotor......This paper describes a model-free method for the fault diagnosis and condition monitoring of rotor systems in wind turbines. Both fault diagnosis and monitoring can be achieved without using a model for the wind turbine, applied controller, or wind profiles. The method is based on measurements from...... and phase information of the modulation signals. It is possible to detect and isolate which blade is faulty or has been changed based on these signatures. Furthermore, the faulty component can be isolated, ie, the actuator, sensor or blade, and the type of fault can be determined. The method can be used...

  15. A methodology for distributed fault diagnosis

    Science.gov (United States)

    Gupta, V.; Puig, V.; Blesa, J.

    2017-01-01

    In this paper, a methodology for distributed fault diagnosis is proposed. The algorithm places the sensors in a system in such a manner that the partition of a system into various subsystems becomes easier facilitating the implementation of a distributed fault diagnosis system. This algorithm also reduces or minimized the number of sensors to be used or install thus reducing overall cost. Binary integer linear programming is used for optimization in this algorithm. Real case study of Barcelona water network has been used to demonstrate and validate the proposed algorithm.

  16. Induction Motor Fault Diagnosis Using a Hilbert-Park Lissajou's Curve Analysis and Neural Network-Based Decision

    OpenAIRE

    Bensalem, Samira; Bacha, Khmais; Benbouzid, Mohamed; Chaari, Abdelkader

    2013-01-01

    International audience; In this work we propose an original fault signature based on the Hilbert-Park Lissajou's curve analysis. The performances of the proposed signature were compared to those of the Park Lissajou's curve which is the signature most recently used. The proposed fault signature does not require a long temporal recording, and their processing is simple. This analysis offers an easy interpretation to conclude on the induction motor condition and its voltage supply state. The pr...

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

    Institute of Scientific and Technical Information of China (English)

    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.

  18. Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

    Directory of Open Access Journals (Sweden)

    Tian Han

    2006-01-01

    Full Text Available This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT, feature extraction, genetic algorithm (GA, and neural network (ANN techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

  19. Development of a variable structure-based fault detection and diagnosis strategy applied to an electromechanical system

    Science.gov (United States)

    Gadsden, S. Andrew; Kirubarajan, T.

    2017-05-01

    Signal processing techniques are prevalent in a wide range of fields: control, target tracking, telecommunications, robotics, fault detection and diagnosis, and even stock market analysis, to name a few. Although first introduced in the 1950s, the most popular method used for signal processing and state estimation remains the Kalman filter (KF). The KF offers an optimal solution to the estimation problem under strict assumptions. Since this time, a number of other estimation strategies and filters were introduced to overcome robustness issues, such as the smooth variable structure filter (SVSF). In this paper, properties of the SVSF are explored in an effort to detect and diagnosis faults in an electromechanical system. The results are compared with the KF method, and future work is discussed.

  20. INVESTIGATION ON THE DIAGNOSIS OF SIMPLE AND COMBINES MECHANICAL FAULTS IN ASYNCHRONOUS MOTOR BASED ELECTRIC DRIVES

    Directory of Open Access Journals (Sweden)

    Bouras A. Karim

    2014-01-01

    Full Text Available In this study, the problems of mechanical unbalance, parallel and angular misalignments and their combinations are analyzed experimentally. Such frequent defects in the drives mainly in the major powers are also responsible for the bearings degradation. However, they have not raised the attention of researchers, given the complexity of their modeling. The combination of the phasic current signal analysis and the neutral current by the FFT supplemented by visual interpretation of patterns models these defects resulting from the 3D representation. The results obtained by using the proposed method show the efficiency to provide an accurate diagnosis of the state of the electric drive undergoing to isolated and combined mechanical defaults to a maintenance staff not necessarily expert of mechanical failure. The innovative approach validated experimentally on a 5.37 KW motor, offers an efficiency to provide an accurate diagnosis to a maintenance staff not necessarily composed of experts in this field.

  1. Wayside Bearing Fault Diagnosis Based on Envelope Analysis Paved with Time-Domain Interpolation Resampling and Weighted-Correlation-Coefficient-Guided Stochastic Resonance

    Directory of Open Access Journals (Sweden)

    Yongbin Liu

    2017-01-01

    Full Text Available Envelope spectrum analysis is a simple, effective, and classic method for bearing fault identification. However, in the wayside acoustic health monitoring system, owing to the high relative moving speed between the railway vehicle and the wayside mounted microphone, the recorded signal is embedded with Doppler effect, which brings in shift and expansion of the bearing fault characteristic frequency (FCF. What is more, the background noise is relatively heavy, which makes it difficult to identify the FCF. To solve the two problems, this study introduces solutions for the wayside acoustic fault diagnosis of train bearing based on Doppler effect reduction using the improved time-domain interpolation resampling (TIR method and diagnosis-relevant information enhancement using Weighted-Correlation-Coefficient-Guided Stochastic Resonance (WCCSR method. First, the traditional TIR method is improved by incorporating the original method with kinematic parameter estimation based on time-frequency analysis and curve fitting. Based on the estimated parameters, the Doppler effect is removed using the TIR easily. Second, WCCSR is employed to enhance the diagnosis-relevant period signal component in the obtained Doppler-free signal. Finally, paved with the above two procedures, the local fault is identified using envelope spectrum analysis. Simulated and experimental cases have verified the effectiveness of the proposed method.

  2. A quantum annealing approach for fault detection and diagnosis of graph-based systems

    Science.gov (United States)

    Perdomo-Ortiz, A.; Fluegemann, J.; Narasimhan, S.; Biswas, R.; Smelyanskiy, V. N.

    2015-02-01

    Diagnosing the minimal set of faults capable of explaining a set of given observations, e.g., from sensor readouts, is a hard combinatorial optimization problem usually tackled with artificial intelligence techniques. We present the mapping of this combinatorial problem to quadratic unconstrained binary optimization (QUBO), and the experimental results of instances embedded onto a quantum annealing device with 509 quantum bits. Besides being the first time a quantum approach has been proposed for problems in the advanced diagnostics community, to the best of our knowledge this work is also the first research utilizing the route Problem → QUBO → Direct embedding into quantum hardware, where we are able to implement and tackle problem instances with sizes that go beyond previously reported toy-model proof-of-principle quantum annealing implementations; this is a significant leap in the solution of problems via direct-embedding adiabatic quantum optimization. We discuss some of the programmability challenges in the current generation of the quantum device as well as a few possible ways to extend this work to more complex arbitrary network graphs.

  3. Entropy Based Test Point Evaluation and Selection Method for Analog Circuit Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Yuan Gao

    2014-01-01

    Full Text Available By simplifying tolerance problem and treating faulty voltages on different test points as independent variables, integer-coded table technique is proposed to simplify the test point selection process. Usually, simplifying tolerance problem may induce a wrong solution while the independence assumption will result in over conservative result. To address these problems, the tolerance problem is thoroughly considered in this paper, and dependency relationship between different test points is considered at the same time. A heuristic graph search method is proposed to facilitate the test point selection process. First, the information theoretic concept of entropy is used to evaluate the optimality of test point. The entropy is calculated by using the ambiguous sets and faulty voltage distribution, determined by component tolerance. Second, the selected optimal test point is used to expand current graph node by using dependence relationship between the test point and graph node. Simulated results indicate that the proposed method more accurately finds the optimal set of test points than other methods; therefore, it is a good solution to minimize the size of the test point set. To simplify and clarify the proposed method, only catastrophic and some specific parametric faults are discussed in this paper.

  4. Faults and Diagnosis Systems in Power Converters

    DEFF Research Database (Denmark)

    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......A power converter is needed in almost all kinds of renewable energy systems and drive systems. It is used both for controlling the renewable source and for interfacing with the load, which can be grid-connected or working in standalone mode. Further, it drives the motors efficiently. Increasing...... frequently. Therefore, it is important to monitor the power device and capacitor fault to increase the reliability of power electronics. In this chapter, the diagnosis methods for power device fault will be discussed by dividing into open- and short-circuit faults. Then, the condition monitoring methods...

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

    Energy Technology Data Exchange (ETDEWEB)

    Tarifa, Enrique E.; Scenna, Nicolas J

    1995-07-01

    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.

  6. Transformer Fault Diagnosis Based on Support Vector Machines%基于支持向量机的变压器故障诊断

    Institute of Scientific and Technical Information of China (English)

    刘义艳; 陈晨; 亢旭红; 巨永锋

    2011-01-01

    Due to lack of typical damage samples in the transformer fault diagnosis, a new fault diagnosis method based on support vector machines (SVMs) is presented. According to the method, the five characteristic gases dissolved in transformer oil are extracted by the K-means clustering (KMC) method as feature vectors, which are input into multi-classified SVMs for training, and then the SVMs diagnosis model is established to implement fault samples classification. The results of experiment and analysis show that with KMC algorithm, the diagnosis information are concentrated and the great time consumption in parameter determination is remitted effectively. The presented method can detect the faults in transformer with a high correct judgment rate and can reach the purpose of automation diagnosis for transformer faults under the condition of few samples.%针对变压器故障诊断中缺少实际典型故障样本的问题,提出了支持向量机(SVMs)变压器故障诊断方法.该方法采用K均值聚类(KMC)对变压器油中5种特征气体样本进行预选取作为特征向量,输入到多分类支持向量机中进行训练,建立SVMs诊断模型,实现对故障样本的诊断分类.实例分析表明,KMC算法浓缩了故障信息,有效地解决了确定模型参数时耗时巨大的问题.该方法在有限样本情况下,能够达到较高的故障正判率,满足变压器故障自动诊断的目的.

  7. Fault Diagnosis and Fault-tolerant Control of Modular Multi-level Converter High-voltage DC System: A Review

    DEFF Research Database (Denmark)

    Liu, Hui; Ma, Ke; Wang, Chao;

    2016-01-01

    Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) configuration is a promising solution for the efficient grid integration and bulky power transmission over long distance. However, the large number of series connected identical modules in MMC may increase the probability...... strategies of MMC-HVDC systems for the most common faults happened in MMC-HVDC systems covering MMC faults, DC side faults as well as AC side faults. An important part of this paper is devoted to a discussion of the vulnerable spots as well as failure mechanism of the MMC-HVDC system covering switching...... device fault, DC line faults as well as AC grid faults. Special attention is given to the comparison of the corresponding fault diagnosis and fault-tolerant control approaches. Further, focus is dedicated to control/protection strategies and topologies with fault ride-though capability for MMC...

  8. Sensor fault diagnosis with a probabilistic decision process

    Science.gov (United States)

    Sharifi, Reza; Langari, Reza

    2013-01-01

    In this paper a probabilistic approach to sensor fault diagnosis is presented. The proposed method is applicable to systems whose dynamic can be approximated with only few active states, especially in process control where we usually have a relatively slow dynamics. Unlike most existing probabilistic approaches to fault diagnosis, which are based on Bayesian Belief Networks, in this approach the probabilistic model is directly extracted from a parity equation. The relevant parity equation can be found using a model of the system or through principal component analysis of data measured from the system. In addition, a sensor detectability index is introduced that specifies the level of detectability of sensor faults in a set of analytically redundant sensors. This index depends only on the internal relationships of the variables of the system and noise level. The method is tested on a model of the Tennessee Eastman process and the result shows a fast and reliable prediction of fault in the detectable sensors.

  9. Multiscale singular value manifold for rotating machinery fault diagnosis

    Energy Technology Data Exchange (ETDEWEB)

    Feng, Yi; Lu, BaoChun; Zhang, Deng Feng [School of Mechanical Engineering, Nanjing University of Science and Technology,Nanjing (United States)

    2017-01-15

    Time-frequency distribution of vibration signal can be considered as an image that contains more information than signal in time domain. Manifold learning is a novel theory for image recognition that can be also applied to rotating machinery fault pattern recognition based on time-frequency distributions. However, the vibration signal of rotating machinery in fault condition contains cyclical transient impulses with different phrases which are detrimental to image recognition for time-frequency distribution. To eliminate the effects of phase differences and extract the inherent features of time-frequency distributions, a multiscale singular value manifold method is proposed. The obtained low-dimensional multiscale singular value manifold features can reveal the differences of different fault patterns and they are applicable to classification and diagnosis. Experimental verification proves that the performance of the proposed method is superior in rotating machinery fault diagnosis.

  10. Active Fault Diagnosis in Sampled-data Systems

    DEFF Research Database (Denmark)

    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...... transformation) structure in the connection between the parametric faults and the matrix transfer function (also known as the fault signature matrix) applied for AFD is not directly preserved for sampled-data system. As a consequence of this, the AFD methods cannot directly be applied for sampled-data systems....... Two methods are considered in this paper to handle the fault signature matrix for sampled-data systems such that standard AFD methods can be applied. The first method is based on a discretization of the system such that the LFT structure is preserved resulting in the same LFT structure in the fault...

  11. Fault Diagnosis in Dynamic Systems Using Fuzzy Interacting Observers

    Directory of Open Access Journals (Sweden)

    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.

  12. A sensor network based virtual beam-like structure method for fault diagnosis and monitoring of complex structures with Improved Bacterial Optimization

    Science.gov (United States)

    Wang, H.; Jing, X. J.

    2017-02-01

    This paper proposes a novel method for the fault diagnosis of complex structures based on an optimized virtual beam-like structure approach. A complex structure can be regarded as a combination of numerous virtual beam-like structures considering the vibration transmission path from vibration sources to each sensor. The structural 'virtual beam' consists of a sensor chain automatically obtained by an Improved Bacterial Optimization Algorithm (IBOA). The biologically inspired optimization method (i.e. IBOA) is proposed for solving the discrete optimization problem associated with the selection of the optimal virtual beam for fault diagnosis. This novel virtual beam-like-structure approach needs less or little prior knowledge. Neither does it require stationary response data, nor is it confined to a specific structure design. It is easy to implement within a sensor network attached to the monitored structure. The proposed fault diagnosis method has been tested on the detection of loosening screws located at varying positions in a real satellite-like model. Compared with empirical methods, the proposed virtual beam-like structure method has proved to be very effective and more reliable for fault localization.

  13. Distributed adaptive diagnosis of sensor faults using structural response data

    Science.gov (United States)

    Dragos, Kosmas; Smarsly, Kay

    2016-10-01

    The reliability and consistency of wireless structural health monitoring (SHM) systems can be compromised by sensor faults, leading to miscalibrations, corrupted data, or even data loss. Several research approaches towards fault diagnosis, referred to as ‘analytical redundancy’, have been proposed that analyze the correlations between different sensor outputs. In wireless SHM, most analytical redundancy approaches require centralized data storage on a server for data analysis, while other approaches exploit the on-board computing capabilities of wireless sensor nodes, analyzing the raw sensor data directly on board. However, using raw sensor data poses an operational constraint due to the limited power resources of wireless sensor nodes. In this paper, a new distributed autonomous approach towards sensor fault diagnosis based on processed structural response data is presented. The inherent correlations among Fourier amplitudes of acceleration response data, at peaks corresponding to the eigenfrequencies of the structure, are used for diagnosis of abnormal sensor outputs at a given structural condition. Representing an entirely data-driven analytical redundancy approach that does not require any a priori knowledge of the monitored structure or of the SHM system, artificial neural networks (ANN) are embedded into the sensor nodes enabling cooperative fault diagnosis in a fully decentralized manner. The distributed analytical redundancy approach is implemented into a wireless SHM system and validated in laboratory experiments, demonstrating the ability of wireless sensor nodes to self-diagnose sensor faults accurately and efficiently with minimal data traffic. Besides enabling distributed autonomous fault diagnosis, the embedded ANNs are able to adapt to the actual condition of the structure, thus ensuring accurate and efficient fault diagnosis even in case of structural changes.

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

    Directory of Open Access Journals (Sweden)

    Changqing Shen

    2013-11-01

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

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

    Institute of Scientific and Technical Information of China (English)

    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.

  16. Deep Fault Recognizer: An Integrated Model to Denoise and Extract Features for Fault Diagnosis in Rotating Machinery

    Directory of Open Access Journals (Sweden)

    Xiaojie Guo

    2016-12-01

    Full Text Available Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN and deep convolution neural network (DCNN, have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.

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

    DEFF Research Database (Denmark)

    Peng, Tao; Dan, Hanbing; Yang, Jian

    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...

  18. Robust Model-Based Fault Diagnosis for DC Zonal Electrical Distribution System

    Science.gov (United States)

    2007-06-01

    control schemes. Through sponsorships from the National Science Foundation (NSF) and the Office of Naval Research (ONR), the MATLAB Simulink based...Chaos : With Applications to Physics, Biology, Chemistry , and Engineering. Cambridge, Mass: Perseus Pub., 1994, pp. 498. [327] S. D. Sudhoff, S. H

  19. Model-based fault diagnosis techniques design schemes, algorithms, and tools

    CERN Document Server

    Ding, Steven

    2008-01-01

    The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms, and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers. This is a textbook with extensive examples and references. Most methods are given in the form of an algorithm that enables a direct implementation in a programme. Comparisons among different methods are included when possible.

  20. Inter Processor Communication for Fault Diagnosis in Multiprocessor Systems

    Directory of Open Access Journals (Sweden)

    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.

  1. A Study on SVM Based on the Weighted Elitist Teaching-Learning-Based Optimization and Application in the Fault Diagnosis of Chemical Process

    Directory of Open Access Journals (Sweden)

    Cao Junxiang

    2015-01-01

    Full Text Available Teaching-Learning-Based Optimization (TLBO is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.

  2. Sequential fault diagnosis for mechatronics system using diagnostic hybrid bond graph and composite harmony search

    Directory of Open Access Journals (Sweden)

    Ming Yu

    2015-12-01

    Full Text Available This article proposes a sequential fault diagnosis method to handle asynchronous distinct faults using diagnostic hybrid bond graph and composite harmony search. The faults under consideration include fault mode, abrupt fault, and intermittent fault. The faults can occur in different time instances, which add to the difficulty of decision making for fault diagnosis. This is because the earlier occurred fault can exhibit fault symptom which masks the fault symptom of latter occurred fault. In order to solve this problem, a sequential identification algorithm is developed in which the identification task is reactivated based on two conditions. The first condition is that the latter occurred fault has at least one inconsistent coherence vector element which is consistent in coherence vector of the earlier occurred fault, and the second condition is that the existing fault coherence vector has the ability to hide other faults and the second-level residual exceeds the threshold. A new composite harmony search which is capable of handling continuous variables and binary variables simultaneously is proposed for identification purpose. Experiments on a mobile robot system are conducted to assess the proposed sequential fault diagnosis algorithm.

  3. Diagnosis of Elevator Faults with LS-SVM Based on Optimization by K-CV

    Directory of Open Access Journals (Sweden)

    Zhou Wan

    2015-01-01

    Full Text Available Several common elevator malfunctions were diagnosed with a least square support vector machine (LS-SVM. After acquiring vibration signals of various elevator functions, their energy characteristics and time domain indicators were extracted by theoretically analyzing the optimal wavelet packet, in order to construct a feature vector of malfunctions for identifying causes of the malfunctions as input of LS-SVM. Meanwhile, parameters about LS-SVM were optimized by K-fold cross validation (K-CV. After diagnosing deviated elevator guide rail, deviated shape of guide shoe, abnormal running of tractor, erroneous rope groove of traction sheave, deviated guide wheel, and tension of wire rope, the results suggested that the LS-SVM based on K-CV optimization was one of effective methods for diagnosing elevator malfunctions.

  4. 采用模糊理论的接地网故障诊断方法%Method of Grounding Grid Fault Diagnosis Based on Fuzzy Theory

    Institute of Scientific and Technical Information of China (English)

    张蓬鹤; 何俊佳; 尹小根

    2011-01-01

    In order to settle the problem of the exact fault placement on grounding grid, a new fault diagnosis method was brought forward based on the fuzzy theory. The two important principles of fuzzy theory, degree of nearness and furthest selection, were improved according to this specific problem of grounding grid fault diagnosis. Making use of the complex image method and Prony method, adopting the MATLAB program, the distribution of earth surface potential of the 10xl0 intact and fault grounding grid were calculated, as the sample data-base. Adopting VC++ program, calculating the relation between the intact grounding grid sample and the fault grounding grid sample based on the improved fuzzy degree of nearness, the fault sample data-base were diagnosed. Subsequently, calculating the difference between the fault samples, the fault placement of grounding grid was diagnosed based on the principle of furthest selection. The result indicated that the accuracy is high.%为解决接地网故障准确定位的问题,笔者提出了采用模糊函数的接地网故障诊断方法.根据接地网故障诊断这一具体问题,改进了模糊理论中的两个重要原则--贴近度和择近原则.利用复镜像法和Prony 法,采用MATLAB进行编程,计算出10×10完整状态接地网和故障状态接地网的地表电位分布,作为诊断算法样本数据库.采用VC++编程,用改进的贴近度公式计算完整状态接地网样本与故障状态接地网样本之间的关系,初步诊断出故障样本库.最后,计算故障样本内各点之间的差异值,利用择远原则最终诊断出接地网的故障位置.运算结果表明模糊理论算法准确率高.

  5. Fault Diagnosis Expert System of Diesel Engine Based on Fault Tree%基于层次分析法的发动机故障诊断专家系统

    Institute of Scientific and Technical Information of China (English)

    崔中清; 薛金红; 杨小强

    2012-01-01

    运用故障树分析法进行康明斯发动机的故障分析,建立了发动机常见故障的层次树模型,并将其转化成二叉故障树以构建专家系统的知识库;研究了系统的推理诊断流程:采用层次分析法设计了故障诊断专家系统.基于Windows平台和Delphi7.0语言开发了故障诊断专家系统.提供了一套简单、实用的故障诊断工具,给装备的故障诊断带来了极大的方便.%The fault analysis of Cummins engine is demonstrated with the fault tree analytical method. The common fault's analytic hierarchy tree is constructed and turned into binary fault trees. Meanwhile,the knowledge base is set up with the method of production rule and frame experss. Then Cummins engine fault diagnosis system is designed on the thought of analytical hierarchy process. The software application is developed using Delphi 7.0 language. This software offers a simple and practical tool for users and brings great convenience to engineering corps.

  6. Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input

    Directory of Open Access Journals (Sweden)

    Zhang Wei

    2017-01-01

    Full Text Available Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.

  7. Fault Diagnosis and Fault-Tolerant Control of Uncertain Robot Manipulators Using High-Order Sliding Mode

    Directory of Open Access Journals (Sweden)

    Mien Van

    2016-01-01

    Full Text Available A robust fault diagnosis and fault-tolerant control (FTC system for uncertain robot manipulators without joint velocity measurement is presented. The actuator faults and robot manipulator component faults are considered. The proposed scheme is designed via an active fault-tolerant control strategy by combining a fault diagnosis scheme based on a super-twisting third-order sliding mode (STW-TOSM observer with a robust super-twisting second-order sliding mode (STW-SOSM controller. Compared to the existing FTC methods, the proposed FTC method can accommodate not only faults but also uncertainties, and it does not require a velocity measurement. In addition, because the proposed scheme is designed based on the high-order sliding mode (HOSM observer/controller strategy, it exhibits fast convergence, high accuracy, and less chattering. Finally, computer simulation results for a PUMA560 robot are obtained to verify the effectiveness of the proposed strategy.

  8. 基于SOM网的风电变流器故障诊断%Fault Diagnosis of Wind Turbine's Converter Based on SOM Neural Net

    Institute of Scientific and Technical Information of China (English)

    王占霞; 张晓波

    2011-01-01

    我国新疆、甘肃、宁夏、内蒙、浙江、黑龙江、江苏、广东等都在大规模建设风电场,这些风电场建成后,其故障维护就有了很大市场.以新疆风电场为基础,尝试开发用于风力机故障智能诊断的系统.首先介绍了风力机及其变频器系统的结构,分析了变频器的故障机理.使用SOM神经网络对风机变流器进行了诊断,用数据验证了诊断结果.把传统的电力电子设备故障诊断技术与新疆风力机变频器的故障诊断相结合,为风电大面积推广应用产生了积极作用.%Large-scaledwindpowerfarms are underconstruction at present in Xinjiang Gansu Ningxia Inner Magnolia,Heilongjiag Jiangsu Guangdong and other provinces and autonomous repons in China The completion and operation of these wind farms will create a huge market of wind farm maintenance service. This paper introduced a fault diagnosis system for converters of wind turbines based on wind farms in Xinjiang This paper firstly introduced the structure of the wind turbine and its converter and analyzed causes of converters' faults The SOM neural net was used to diagnose the faults and the data generated verified its effect The study innovatively combines the traditional fault diagnosis technology for electncal and electronic devices with the fault diagnosis technology for wind turbines'converters in Xinpang wind farms, and will play a positive role in popularization of the technique in more wind farms.

  9. Optimal input design for fault detection and diagnosis

    DEFF Research Database (Denmark)

    Sadegh, Payman; Madsen, Henrik; Holst, J.

    1995-01-01

    In the paper, the design of optimal input signals for detection and diagnosis in a stochastic dynamical system is investigated. The design is based on maximization of Kullback measure between the model under fault and the model under normal operation conditions. It is established that the optimal...

  10. Fault Diagnosis of Rotating Machinery Based on Multisensor Information Fusion Using SVM and Time-Domain Features

    Directory of Open Access Journals (Sweden)

    Ling-li Jiang

    2014-01-01

    Full Text Available Multisensor information fusion, when applied to fault diagnosis, the time-space scope, and the quantity of information are expanded compared to what could be acquired by a single sensor, so the diagnostic object can be described more comprehensively. This paper presents a methodology of fault diagnosis in rotating machinery using multisensor information fusion that all the features are calculated using vibration data in time domain to constitute fusional vector and the support vector machine (SVM is used for classification. The effectiveness of the presented methodology is tested by three case studies: diagnostic of faulty gear, rolling bearing, and identification of rotor crack. For each case study, the sensibilities of the features are analyzed. The results indicate that the peak factor is the most sensitive feature in the twelve time-domain features for identifying gear defect, and the mean, amplitude square, root mean square, root amplitude, and standard deviation are all sensitive for identifying gear, rolling bearing, and rotor crack defect comparatively.

  11. Bispectrum Analysis in Fault Diagnosis of Gears

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The application of bispectrum analysis in fault diagnosis of gears is studied in this paper. Bispectrum analysis is capable of removing Gaussian or symmetric non-Gaussian noise and providing more information than power spectrum analysis. The results of the research show that normal gear signals, cracked gear signals and broken gear signals can be easily distinguished by using bispectrum as the signal features. The bispectrum diagonal slice Bx(ω1,ω2) can be used to identify the gear condition automatically.

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

    CERN Document Server

    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...

  13. A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhi-tao Wang

    2015-01-01

    Full Text Available As an important gas path performance parameter of gas turbine, exhaust gas temperature (EGT can represent the thermal health condition of gas turbine. In order to monitor and diagnose the EGT effectively, a fusion approach based on fuzzy C-means (FCM clustering algorithm and support vector machine (SVM classification model is proposed in this paper. Considering the distribution characteristics of gas turbine EGT, FCM clustering algorithm is used to realize clustering analysis and obtain the state pattern, on the basis of which the preclassification of EGT is completed. Then, SVM multiclassification model is designed to carry out the state pattern recognition and fault diagnosis. As an example, the historical monitoring data of EGT from an industrial gas turbine is analyzed and used to verify the performance of the fusion fault diagnosis approach presented in this paper. The results show that this approach can make full use of the unsupervised feature extraction ability of FCM clustering algorithm and the sample classification generalization properties of SVM multiclassification model, which offers an effective way to realize the online condition recognition and fault diagnosis of gas turbine EGT.

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

    Science.gov (United States)

    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.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  16. MODIFIED LAPLACIAN EIGENMAP METHOD FOR FAULT DIAGNOSIS

    Institute of Scientific and Technical Information of China (English)

    JIANG Quansheng; JIA Minping; HU Jianzhong; XU Feiyun

    2008-01-01

    A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.

  17. 基于PNN的舵机故障诊断方法研究%Method for Fault Diagnosis of Steering Gear Based on Probabilistic Neural Networks

    Institute of Scientific and Technical Information of China (English)

    周晶; 宋辉; 刘喜作

    2011-01-01

    Steering gear is one of the important parts of ship's control system, and has a crucial effect on safety. With the development of science and technology, while steering gear is constantly upgrading its performance, its complexity is vastly increased. Since its fault phenomenon and fault itself are not simple mapping relation, the equipment fault diagnosis is the typical complex nonlinear classification problem in essence. A method of steering gear fault diagnosis based on probabilistic neural network (PNN) is proposed in this paper. The feature values extracted from steering gear vibration signals are taken as input parameters for PNN. Simulation results show that it works stably and quickly, and can perform high-accuracy classifica-tion of steering gear faults.%随着科技的发展,舵机在不断提升性能的同时,其复杂性也大幅度提高,故障现象与故障本身并非简单的映射关系,设备故障诊断实质上是典型的复杂非线性分类问题.提出一种利用概率神经网络对舵机故障分类的方法,将从舵机振动信号中的提取特征值作为PNN的输入参数,构造出基于概率神经网络的舵机故障诊断方法.仿真结果显示,该网络工作稳定,运算速度快,对舵机故障分类准确率较高.

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

    Directory of Open Access Journals (Sweden)

    Meng-Hui Wang

    2012-01-01

    Full Text Available 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 paper uses a wind power simulator to produce fault conditions and features from the monitoring sensors. Then an extension neural network type-1- (ENN-1- based method is proposed to develop the core of the fault diagnosis system. The proposed system will benefit the development of real fault diagnosis systems with testing models that demonstrate satisfactory results.

  19. Process fault detection and diagnosis based on ICA-PCA and Lasso%基于 ICA-PCA 和 Lasso 的过程故障诊断

    Institute of Scientific and Technical Information of China (English)

    衷路生; 吴秀江; 谭畅; 龚锦红

    2016-01-01

    为了解决复杂工业过程中变量多,难以判断引起故障的主要异常变量的问题,提出一种基于IC A‐PC A (独立成分分析和主成分分析)算法和Lasso (最小绝对收缩和选择算子)回归算法的过程故障检测与诊断的集成模型。首先,建立IC A‐PC A模型提取数据的高斯信号和非高斯信号,构造相关统计量实现在线故障检测;然后,基于ICA‐PCA模型获得的过程状态及故障信息,进一步构造基于Lasso回归算法的故障诊断模型,实现故障发生时的主要异常变量的定位和选择;最后,利用Matlab进行了TE(田纳西‐伊斯曼)过程的数值仿真实验,并与已有故障诊断方法分布式PC A贡献图法进行比较,结果表明所提出的方法是有效的。%In order to solve the complex industrial process variables ,it is difficult to judge caused by failure of the main abnormal variables ,based on ICA‐PCA (independent component analysis and prin‐cipal component analysis ) algorithm and Lasso (least absolute shrinkage and selection operator ) re‐gression algorithm of fault detection and diagnosis of integrated model was proposed .First ,ICA‐PCA model was established to extract the data of the Gaussian signal and the non Gaussian signal ,struc‐ture related statistics for online fault detection ;then ,based on ICA‐PCA model the process state and fault information were obtained ,further structure based on Lasso regression algorithm was estab‐lished for fault diagnosis model and the orientation and choice of the fault occurs were realized when the main abnormal variables .Finally ,the numerical simulation experiment of TE (Eastman Tennes‐see) process was carried out by using the simulation software Matlab ,and the results were compared with that of the existing fault diagnosis method for distributed PCA with diagram method .The results show that the proposed method is effective .

  20. Adaptive Time-Frequency Distribution Based on Time-Varying Autoregressive and Its Application to Machine Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The time-varying autoregressive (TVAR) modeling of a non-stationary signal is studied. In the proposed method, time-varying parametric identification of a non-stationary signal can be translated into a linear time-invariant problem by introducing a set of basic functions. Then, the parameters are estimated by using a recursive least square algorithm with a forgetting factor and an adaptive time-frequency distribution is achieved. The simulation results show that the proposed approach is superior to the short-time Fourier transform and Wigner distribution. And finally, the proposed method is applied to the fault diagnosis of a bearing, and the experiment result shows that the proposed method is effective in feature extraction.

  1. Fault diagnosis method based on MBKPCA and SDG%基于多块KPCA和SDG的故障诊断方法

    Institute of Scientific and Technical Information of China (English)

    王雅琳; 何巍; 桂卫华; 阳春华

    2013-01-01

    针对大规模复杂工业过程,提出一种基于多块核主元分析(MBKPCA)和符号有向图(SDG)的故障诊断方法。首先,提出基于SDG和优先级的分块策略,以强连接元SCC为最高优先级、多入/出度节点群为次高优先级、节点链为最低优先级对过程进行分块;在此基础上,采用MBKPCA进行过程监控,对于检测到的故障,先确定故障发生在哪一个数据块,再触发SDG在故障块内完成故障定位。所提出方法克服了多块KPCA故障隔离不完全和SDG推理过程中组合爆炸的缺点,可以提高复杂工业过程故障诊断的准确度和速度。基于Tennessee Eastman过程的仿真研究表明了所提出故障诊断方法的有效性。%Aiming at the large-scale complex industrial process, a fault diagnosis method based on multiblock kernel principal component analysis(MBKPCA) and signed directed graph(SDG) is proposed. Firstly, by proposing a partition strategy based on SDG and priority, the process is divided into multiple blocks according to the strong connected component as the highest priority, the multiple input or output degree node group as the second priority and the node chain as the lowest priority. On that basis, MBKPCA is used for the process monitoring. If the fault is detected, MBKPCA will determine which block the fault occurs in, then SDG is triggered to complete the fault location in the fault block. The proposed method can improve the accuracy and rapid of the fault diagnosis for the complex industrial process by overcoming the disadvantage of the incomplete fault isolation of MBKPCA and combination explosion in SDG reasoning process. The simulation research on Tennessee Eastman process is performed to show the effectiveness of the proposed method.

  2. Fault Diagnosis of Simulation Circuit Based on Negative Selection Algorithm%基于否定选择算法的模拟电路故障诊断

    Institute of Scientific and Technical Information of China (English)

    王玉珏; 漆德宁

    2015-01-01

    针对传统智能诊断技术受限于先验知识、模拟电路故障多样性等不足,对基于否定选择算法的模拟电路故障诊断进行研究。分析人工免疫系统中的否定选择算法原理及应用,介绍实值否定选择算法的产生机制,提出与自体耐受和Monte Carlo相结合的优化算法,通过Fish’s Iris数据仿真显示,并将优化算法运用于电阻电路的8种软故障诊断。结果表明:优化算法的总体检测率达90%,能降低成熟检测器冗余,节省计算空间。%Research the fault diagnosis of simulation circuit based on negative selection algorithm to deal with traditional intelligent diagnosis technology shortages such as prior knowledge limit and simulation circuit fault variety and so on. Analyze principle and application of negative selection algorithm in artificial immune system. Introduce the mechanism of the real-valued negative selection algorithm, proposes optimized algorithm which combines Monte Carlo with self tolerance. By means of Fish’s Iris simulation result, use optimized algorithm in the redundancy of detections and saves space of computer. Use optimized algorithm in the fault diagnosis of resistance circuit which has eight soft fault kinds. The results show that the total detection rate of optimized algorithm reaches 90%, it can reduce redundancy of mature detector, and save computation space.

  3. On-Line Broken-Bar Fault Diagnosis System of Induction Motor

    Institute of Scientific and Technical Information of China (English)

    ZHANG Rong; WANG Xiuhe

    2008-01-01

    Induction motor faults including mechanical and electrical faults are reviewed. The fault diagnosis methods are summarized. To analyze the influence of stator current, torque, speed and rotor current on faulted bars, a time-stepping transient finite element (FE) model of induction motor with bars faulted is created in this paper. With wavelet package analysis method and FFT method, the simulation result of finite element is analyzed. Based on the simulation analysis, the on-line fault diagnosis system of induction motor with bars faulted is developed. With the speed of broken bars motor changed from 1 478 r/min to 1 445 r/min, the FFT power spectra and the wavelet package decoupling factors are given. The comparison result shows that the on-line diagnosis system can detect broken-bar fault efficiently.

  4. 基于递归图分析的压缩机故障诊断方法研究%Compressor Fault Diagnosis Based on Recurrence Plot Analysis

    Institute of Scientific and Technical Information of China (English)

    杨俊; 周丙寅; 张毅; 周勇

    2013-01-01

    Recurrence plot of the vibration signal is researched by studying the internal regularity and characteristic based on recurrence quantification analysis effectively for extracting the nonlinear feature of vibration system of reciprocating natural gas compressor under the fault working condition.The classification and recognition method based on the extracted feature is applied in diagnosis of compressor fault successfully.Simulations and experiments were worked based on some samples of 3 types of typical faults.Results show that the feature parameter which is extracted by this method has nice classification performance,and shows nice performance in the application of fault diagnosis.%通过有效提取往复式天然气压缩机在故障工作状态下机械振动系统的非线性特征量,对振动信号的递归图进行定量分析,研究递归图内部规律性和特征,对提取特征量的分类识别成功应用于对某型压缩机的故障诊断.仿真实验以三类典型故障类别一定样本数量的振动数据为研究对象进行,仿真结果表明定量递归分析提取的特征量具有很好的聚类性,能很好地把三类故障区分,达到对故障诊断的目的.

  5. 基于T-PLS贡献图方法的故障诊断技术%Total PLS Based Contribution Plots for Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    李钢; 秦泗钊; 吉吟东; 周东华

    2009-01-01

    Multivariate statistical process monitoring (MSPM) is an efficient data-driven fault detection and diagnosis approach for complex industrial processes. Partial least squares or projection to latent structures (PLS) is one of the latent projection structures used in MSPM, which uses process data X and quality data Y together. In this paper, we discuss a new fault diagnosis approach based on total projection to latent structures (T-PLS). Four kinds of monitoring statistics are used in T-PLS, and a new definition of variable contributions to T2 of PLS is proposed. Then, definitions of variable contributions to all statistics are derived to identify the faults. Control limits for contribution plots are calculated to identify whether a variable is in abnormal situation or not. Further, the proposed method separates the identified variables into faulty variables related to Y and unrelated to Y more clearly than conventional method. A case study on Tennessee Eastman process (TEP) indicates the efficiency of the proposed approach.

  6. Fault diagnosis method and application based on fuzzy signed directed graph%模糊SDG故障诊断方法及其应用

    Institute of Scientific and Technical Information of China (English)

    马昕; 张贝克

    2011-01-01

    If the thresholds are set unreasonably,the fault diagnosis method based on signed directed graph may cause the fault is predicted incorrectly or ignored. In this paper, the model of fuzzy signed directed graph is presented, in which the three-stage SDG model is substituted by the five-stage SDG model, the parameter node is given the fuzzy membership, and the corresponding fuzzy reasoning algorithm is presented. Fuzzy SDG model and fuzzy SDG reasoning are introduced into a fault diagnosis example of atmospheric and vacuum distillation unit,which verifies the method is effective and feasible.%基于SDG的故障诊断方法在使用过程中,由于阈值设定不合理会导致故障的误报或漏报.针对该问题展开研究,提出模糊SDG模型,建立五级SDG模型并引入参数模糊隶属度,提出相应的模糊推理算法.通过将模糊SDG模型及其推理方法应用于某常减压蒸馏装置进行故障诊断实例分析,验证了方法的有效性和可行性.

  7. Fault Diagnosis for Loop Reactor Based on SDG%基于SDG方法的环管反应器故障诊断

    Institute of Scientific and Technical Information of China (English)

    闫志国; 郑明; 赵世平; 宣爱国; 吴元欣

    2011-01-01

    以某化工厂丙烯聚合关键设备—环管反应器为例,通过熟悉工艺流程,选取合适的关键变量,确定变量之间的影响关系,建立用于故障诊断的SDG模型.在与工厂工艺操作手册及仿真模型模拟分析校对该模型后,对该模型进行了故障诊断分析.实验结果表明,基于SDG模型的故障诊断能够准确地揭示故障传播路径.%Taking loop reactor in the propylene polymerization process as an example, the proper variables were selected and the relationship among them were confirmed to build a SDG model for fault diagnosis, then having it checked with operation manual and simulation model. Experimental result shows that the SDG-based fault diagnosis can reveal the fault path clearly and accurately.

  8. Intelligent Fault Diagnosis in Lead-zinc Smelting Process

    Institute of Scientific and Technical Information of China (English)

    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.

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

    Science.gov (United States)

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

    2015-03-01

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

  10. 基于LMD和SVDD的滚动轴承故障诊断方法%Roller Bearing Fault Diagnosis Method Based on LMD And SVDD

    Institute of Scientific and Technical Information of China (English)

    刘震坤

    2012-01-01

    Aiming at the absence of fault samples in the engineering applications, a roller bearing fault diagnosis method based on local mean decomposition (LMD) and Support Vector Data Description (SVDD) is proposed. First, the rolling bearing fault vibration signals collected from the inner-race and outer-race were decomposed into a number of PFs by LMD; then, the ratios of amplitudes in the characteristic frequencies were defined as the characteristic amplitude ratios after the envelope spectra of some of PFs includ- ing the main fault information were obtained; finally, the characteristic amplitude ratios were served as the fault characteristic vectors, which were be trained and tested through SVDD. The result shows, the method based on LMD and SVDD has higher detection rate and better classification results.%针对在实际工程应用中缺乏故障样本的问题,论文提出一种基于局部均值分解(Localmeandecomposition,LMD)和支持向量数据描述(supponVectorDataDescription,SVDD)的故障诊断方法。该方法首先将采集到的滚动轴承内外圈故障振动信号进行LMD分解后为若干个PF分量,然后求出包含主要故障信息的若干个PF分量的包络谱,将包络谱中故障特征频率处的幅值作为故障特征向量,通过SVDD进行训练和测试。结果显示,基于LMD和SVDD的滚动轴承故障诊断方法的检测率较高、分类效果较好。

  11. Noise reduction method for nonlinear signal based on maximum variance unfolding and its application to fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    A new noise reduction method for nonlinear signal based on maximum variance unfolding(MVU)is proposed.The noisy sig- nal is firstly embedded into a high-dimensional phase space based on phase space reconstruction theory,and then the manifold learning algorithm MVU is used to perform nonlinear dimensionality reduction on the data of phase space in order to separate low-dimensional manifold representing the attractor from noise subspace.Finally,the noise-reduced signal is obtained through reconstructing the low-dimensional manifold.The simulation results of Lorenz system show that the proposed MVU-based noise reduction method outperforms the KPCA-based method and has the advantages of simple parameter estimation and low parameter sensitivity.The proposed method is applied to fault detection of a vibration signal from rotor-stator of aero engine with slight rubbing fault.The denoised results show that the slight rubbing features overwhelmed by noise can be effectively extracted by the proposed noise reduction method.

  12. Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT for Aquaculture

    Directory of Open Access Journals (Sweden)

    Yingyi Chen

    2017-01-01

    Full Text Available In the Internet of Things (IoT equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

  13. Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture.

    Science.gov (United States)

    Chen, Yingyi; Zhen, Zhumi; Yu, Huihui; Xu, Jing

    2017-01-14

    In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT.

  14. Fault Diagnosis of Power Systems Using Intelligent Systems

    Science.gov (United States)

    Momoh, James A.; Oliver, Walter E. , Jr.

    1996-01-01

    The power system operator's need for a reliable power delivery system calls for a real-time or near-real-time Al-based fault diagnosis tool. Such a tool will allow NASA ground controllers to re-establish a normal or near-normal degraded operating state of the EPS (a DC power system) for Space Station Alpha by isolating the faulted branches and loads of the system. And after isolation, re-energizing those branches and loads that have been found not to have any faults in them. A proposed solution involves using the Fault Diagnosis Intelligent System (FDIS) to perform near-real time fault diagnosis of Alpha's EPS by downloading power transient telemetry at fault-time from onboard data loggers. The FDIS uses an ANN clustering algorithm augmented with a wavelet transform feature extractor. This combination enables this system to perform pattern recognition of the power transient signatures to diagnose the fault type and its location down to the orbital replaceable unit. FDIS has been tested using a simulation of the LeRC Testbed Space Station Freedom configuration including the topology from the DDCU's to the electrical loads attached to the TPDU's. FDIS will work in conjunction with the Power Management Load Scheduler to determine what the state of the system was at the time of the fault condition. This information is used to activate the appropriate diagnostic section, and to refine if necessary the solution obtained. In the latter case, if the FDIS reports back that it is equally likely that the faulty device as 'start tracker #1' and 'time generation unit,' then based on a priori knowledge of the system's state, the refined solution would be 'star tracker #1' located in cabinet ITAS2. It is concluded from the present studies that artificial intelligence diagnostic abilities are improved with the addition of the wavelet transform, and that when such a system such as FDIS is coupled to the Power Management Load Scheduler, a faulty device can be located and isolated

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

    DEFF Research Database (Denmark)

    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 probable diagnosis. Results from the DX 09 Diagnostic Challenge shown strong detection properties, while...

  16. Multisensor Fused Fault Diagnosis for Rotation Machinery Based on Supervised Second-Order Tensor Locality Preserving Projection and Weighted k-Nearest Neighbor Classifier under Assembled Matrix Distance Metric

    Directory of Open Access Journals (Sweden)

    Fen Wei

    2016-01-01

    Full Text Available In order to sufficiently capture the useful fault-related information available in the multiple vibration sensors used in rotation machinery, while concurrently avoiding the introduction of the limitation of dimensionality, a new fault diagnosis method for rotation machinery based on supervised second-order tensor locality preserving projection (SSTLPP and weighted k-nearest neighbor classifier (WKNNC with an assembled matrix distance metric (AMDM is presented. Second-order tensor representation of multisensor fused conditional features is employed to replace the prevailing vector description of features from a single sensor. Then, an SSTLPP algorithm under AMDM (SSTLPP-AMDM is presented to realize dimensional reduction of original high-dimensional feature tensor. Compared with classical second-order tensor locality preserving projection (STLPP, the SSTLPP-AMDM algorithm not only considers both local neighbor information and class label information but also replaces the existing Frobenius distance measure with AMDM for construction of the similarity weighting matrix. Finally, the obtained low-dimensional feature tensor is input into WKNNC with AMDM to implement the fault diagnosis of the rotation machinery. A fault diagnosis experiment is performed for a gearbox which demonstrates that the second-order tensor formed multisensor fused fault data has good results for multisensor fusion fault diagnosis and the formulated fault diagnosis method can effectively improve diagnostic accuracy.

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

    Institute of Scientific and Technical Information of China (English)

    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.

  18. Fault diagnosis with the Aladdin transient classifier

    Science.gov (United States)

    Roverso, Davide

    2003-08-01

    The purpose of Aladdin is to assist plant operators in the early detection and diagnosis of faults and anomalies in the plant that either have an impact on the plant performance, or that could lead to a plant shutdown or component damage if allowed to go unnoticed. The kind of early fault detection and diagnosis performed by Aladdin is aimed at allowing more time for decision making, increasing the operator awareness, reducing component damage, and supporting improved plant availability and reliability. In this paper we describe in broad lines the Aladdin transient classifier, which combines techniques such as recurrent neural network ensembles, Wavelet On-Line Pre-processing (WOLP), and Autonomous Recursive Task Decomposition (ARTD), in an attempt to improve the practical applicability and scalability of this type of systems to real processes and machinery. The paper focuses then on describing an application of Aladdin to a Nuclear Power Plant (NPP) through the use of the HAMBO experimental simulator of the Forsmark 3 boiling water reactor NPP in Sweden. It should be pointed out that Aladdin is not necessarily restricted to applications in NPPs. Other types of power plants, or even other types of processes, can also benefit from the diagnostic capabilities of Aladdin.

  19. Fault Diagnosis of Transformer Based on D-S Evidence Theory%基于D-S证据理论的变压器故障诊断

    Institute of Scientific and Technical Information of China (English)

    王日彬; 佘彩绮; 刘新东; 周锦龙

    2012-01-01

    Because the relationship between fault symptom and failure cause of power transformer is complex and the fault of transformer is not always diagnosed accurately by using single method, a new fault diagnosis method by combining grey association entropy method and weighted K-NN algorithm is proposed based on the D-S Evidence Theory in this paper. On the basis of the dissolved gases analysis (DGA), the basic credit assignment function of evidence theory is built by grey association entropy algorithm and weighted K-NN algorithm. Then, more reliable evidence information is generated by using of evidence combination rule. In the end, the fault of transformer is diagnosed according to the maximum basic credit value. The example of fault diagnosis of transformer testifies the feasibility and effectiveness of proposed algorithm, which can accurately diagnose the transformer fault.%针对电力变压器故障征兆与原因之间错综复杂的关系,以及单一变压器故障诊断算法精度有限的问题,本文提出一种在D-S证据理论的基础上,结合灰关联熵法和加权K邻近算法的变压器故障诊断新方法.该算法以油中溶解气体分析方法(Dissolved Gases Analysis,简称DGA)为基础,通过灰关联熵法和加权K邻近算法构建证据理论的基本可信度赋值函数,然后利用证据组合规则产生更为可靠的证据信息;最后根据基本可信数最大值确定变压器故障类型.变压器故障诊断实例结果表明该算法能够准确判断出变压器的故障类型,证明了该算法的可行性和有效性.

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

    Institute of Scientific and Technical Information of China (English)

    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. Study on Electric Fault Diagnosis Model Based on MAS%基于MAS电务故障诊断模型的研究

    Institute of Scientific and Technical Information of China (English)

    李佳奇; 党建武

    2013-01-01

    铁路运输组织模式逐步向集中调度、系统综合集成、智能管理和信息共享及融合的方向转变和延伸,在此趋势下,针对高速铁路信号监测系统功能尚存在缺陷的状况,提出将分布式人工智能技术引入到信号设备故障诊断系统中.利用MAS对复杂系统问题的较强求解能力,建立基于MAS的故障诊断系统,采用面向Agent的知识表示,构造数据采集Agent、诊断Agent、事例分析Agent及管理Agent等核心模块的BDI模型,扩展混合Agent结构,将常规的不具备自学习能力的诊断系统设计成为低耦合高内聚的具有自学习能力的并行MAS诊断系统,使系统具有良好的可靠性、扩展性和鲁棒性,提高故障诊断决策和监测管理水平.%Under the new situation that the railway transportation organization pattern is gradually transforming and extending to the direction of centralized dispatching, comprehensive systems integration, intelligent management and information sharing and integrating, in view of the fact that the functions of signal monitoring systems of high-speed railways still have defects, the distributed artificial intelligence technology was introduced into the fault diagnosis system of signal devices. Utilizing the strong MAS ability to solve complicated system problems, the MAS-based fault diagnosis system was established. By indication of facing the Agent knowledge, the center BDI models were constructed, which consisted of central modules of data acquisition A-gent, diagnosis Agent, event analysis Agent and management Agent. The hybrid Agent structure was extended. The conventional diagnosis system without self-learning ability was designed into the low-coupling high-cohesion diagnosis system with self-learning ability and parallel to the MAS diagnosis system. The proposed system has good reliability, augmentabilily and robustness so that the level of fault diagnosis and monitoring management is raized.

  2. An adaptive particle filter for mobile robot fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    DUAN Zhuo-hua; FU Ming; CAI Zi-xing; YU Jin-xia

    2006-01-01

    An adaptive particle filter for fault diagnosis of dead-reckoning system was presented, which applied a general framework to integrate rule-based domain knowledge into particle filter. Domain knowledge was exploited to constrain the state space to certain subset. The state space was adjusted by setting the transition matrix. Firstly, the monitored mobile robot and its kinematics models,measurement models and fault models were given. Then, 5 kinds of planar movement states of the robot were estimated with driving speeds of left and right side. After that, the possible (or detectable) fault modes were obtained to modify the transitional probability.There are two typical advantages of this method, i.e. particles will never be drawn from hopeless area of the state space, and the particle number is reduced.

  3. Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals

    Science.gov (United States)

    Lu, Siliang; Wang, Xiaoxian; He, Qingbo; Liu, Fang; Liu, Yongbin

    2016-12-01

    Transient signal analysis (TSA) has been proven an effective tool for motor bearing fault diagnosis, but has yet to be applied in processing bearing fault signals with variable rotating speed. In this study, a new TSA-based angular resampling (TSAAR) method is proposed for fault diagnosis under speed fluctuation condition via sound signal analysis. By applying the TSAAR method, the frequency smearing phenomenon is eliminated and the fault characteristic frequency is exposed in the envelope spectrum for bearing fault recognition. The TSAAR method can accurately estimate the phase information of the fault-induced impulses using neither complicated time-frequency analysis techniques nor external speed sensors, and hence it provides a simple, flexible, and data-driven approach that realizes variable-speed motor bearing fault diagnosis. The effectiveness and efficiency of the proposed TSAAR method are verified through a series of simulated and experimental case studies.

  4. Analog circuit diagnosis with fault dictionary method based on DAGSVC*%基于DAGSVC的模拟电路故障字典法

    Institute of Scientific and Technical Information of China (English)

    姜媛媛; 韩振云; 崔江

    2011-01-01

    Focusing on the design of problem of fault diagnosis of analog circuit and classifier with support vector machines(SVMs) ,a new method of fault dictionary based on directed acyclic graph SVMs classifier (DAGSVC) is presented, and a specification for estimating the average test complexity of the support vector machine classifier (SVC) is also compared. Two actual analog filter are tested to validate the proposed method,whose performance is proven to be superior to the traditional methods, such as “1-v-r” SVC and “l-v-1” SVC. The proposed method, being proper to perform analog circuit diagnosis and faults isolation,could also achieve almost the same diagnosis rate as the clustering binary tree SVC,whose test structure is not unique.%针对模拟电路的故障诊断和支持向量机分类器的设计问题,讨论了一种基于有向无环图支持向量机分类器(DAGSVC)的故障字典新方法,并比较了几种支持向量机故障分类器的平均测试复杂度指标.通过对2个实际模拟滤波器的实际测试和验证表明:该方法性能要优于"1-v-r"SVC,"1-v-1"SVC等常规的故障分类器,并和聚类二叉树SVC的诊断性能接近,适合模拟电路的故障分类和诊断.

  5. 基于非线性主元分析和符号有向图的故障诊断方法%A fault diagnosis method based on nonlinear principal component analysis and sign directed graph

    Institute of Scientific and Technical Information of China (English)

    黄道平; 龚婷婷; 曾辉

    2009-01-01

    Nonlinear principal component analysis (NLPCA) fault detection method achieves good detection results especially in a nonlinear process. Signed directed graph (SDG) model is based on deep-going information, which excels in fault interpretation. In this work, an NLPCA-SDG fault diagnosis method was proposed. SDG model was used to interpret the residual contributions produced by NLPCA. This method could overcome the shortcomings of traditional principal component analysis (PCA) method in fault detection of a nonlinear process and the shortcomings of traditional SDG method in single variable statistics in discriminating node conditions and threshold values. The application to a distillation unit of a petrochemical plant illustrated its validity in nonlinear process fault diagnosis.%@@引言 化工过程通常具有变量多、非线性程度高、难以获得准确的数学模型、故障样本少等特点.

  6. Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP

    DEFF Research Database (Denmark)

    Ruiz, Magda; Sin, Gürkan; Berjaga, Xavier;

    2011-01-01

    problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology...

  7. 基于人工蜂群算法的电网故障诊断%Fault Diagnosis of Power Network Based on Artificial Bee Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    韦晓广; 陈奎

    2012-01-01

    In order to solve 0-1 programming problem in fault diagnosis of power network, the paper proposed optimization methods of artificial bee colony algorithm from aspects of algebra and geometry. The simulation results show that the artificial bee colony algorithm is feasible and reasonable, and the overall performance is significantly superior to traditional genetic algorithms; artificial bee colony algorithm based on geometric has better stability and search capabilities than the algorithm based on algebraic, and is more suitable for occasions with high stability and accuracy requirements. Fault diagnosis of power network, artificial bee colony algorithm, algebra method,%针对电网故障诊断中的0-1规划问题,从代数和几何角度优化了人工蜂群算法.仿真结果表明,人工蜂群算法具有可行性和合理性,并且综合性能显著优于传统的遗传算法 ;在两种人工蜂群算法中,基于几何思想的人工蜂群算法具有更好的稳定性和搜索能力,更加适用于对稳定性和精准度要求很高的场合.

  8. Power System Fault Diagnosis Method Based on Bayesian Petri Nets%一种基于贝叶斯Petri网的故障诊断方法

    Institute of Scientific and Technical Information of China (English)

    佘维; 叶阳东

    2011-01-01

    针对Petri网在分析复杂电力系统时的容错性差且难以适应网络拓扑变化的问题,提出一种贝叶斯Petri网模型(BPN),并基于该模型提出一种电网故障诊断方法.该方法通过电力系统网络拓扑分析确定停电区域,随后按照故障蔓延方向对停电区域内的元件分别建立BPN模型,应用Petri网推理和贝叶斯概率计算确定故障元件,最后采用均值方法对各方向上的分析结果进行融合,诊断分析表明,该方法在信息不完备的情况下具有较好的容错性,并且在网络拓扑结构发生变化后仍具有较好的适应性.由于BPN推理时根据基于统计的先验概率求取元件故障的发生概率,避免了直接对计算参数进行设定的主观性.%For Petri net's fault tolerance was poor and it was difficult to adapt to the topological change of network in the analysis of complex power system, this paper proposed a kind of Bayesian Petri net model, and gave a fault diagnosis method for power grid based on this model. According to the fault spread directions, this method established the BPN models for each component in power cut area separately, and determined the failed component by the application of Petri net inference and Bayesian probability calculation, finally, fused the results by averaging method. Diagnosis analysis showed that this method had better fault tolerance under the condition of incomplete information, and had better adaptability after the change of network topology structure. In the BPN inference, calculating the fault probability of component based on the prior probability from statistics, avoided the subjectivity of setting related parameters directly.

  9. A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks

    Science.gov (United States)

    Cai, Baoping; Liu, Hanlin; Xie, Min

    2016-12-01

    Bayesian network (BN) is a commonly used tool in probabilistic reasoning of uncertainty in industrial processes, but it requires modeling of large and complex systems, in situations such as fault diagnosis and reliability evaluation. Motivated by reduction of the overall complexities of BNs for fault diagnosis, and the reporting of faults that immediately occur, a real-time fault diagnosis methodology of complex systems with repetitive structures is proposed using object-oriented Bayesian networks (OOBNs). The modeling methodology consists of two main phases: an off-line OOBN construction phase and an on-line fault diagnosis phase. In the off-line phase, sensor historical data and expert knowledge are collected and processed to determine the faults and symptoms, and OOBN-based fault diagnosis models are developed subsequently. In the on-line phase, operator experience and sensor real-time data are placed in the OOBNs to perform the fault diagnosis. According to engineering experience, the judgment rules are defined to obtain the fault diagnosis results.

  10. Sensor Fault Diagnosis for a Class of Time Delay Uncertain Nonlinear Systems Using Neural Network

    Institute of Scientific and Technical Information of China (English)

    Mou Chen; Chang-Sheng Jiang; Qing-Xian Wu

    2008-01-01

    In this paper, a sliding mode observer scheme of sensor fault diagnosis is proposed for a class of time delay nonlinear systems with input uncertainty based on neural network. The sensor fault and the system input uncertainty are assumed to be unknown but bounded. The radial basis function (RBF) neural network is used to approximate the sensor fault. Based on the output of the RBF neural network, the sliding mode observer is presented. Using the Lyapunov method, a criterion for stability is given in terms of matrix inequality. Finally, an example is given for illustrating the availability of the fault diagnosis based on the proposed sliding mode observer.

  11. Improved wavelet analysis for induction motors mixed-fault diagnosis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hanlei; ZHOU Jiemin; LI Gang

    2007-01-01

    Eccentricity is one of the frequent faults of induction motors,and it may cause rub between the rotor and the stator.Early detection of significant rub from pure eccentricity can prolong the lifespan of induction motors.This paper is devoted to such mixed-fault diagnosis:eccentricity plus rub fault.The continuous wavelet transform(CWT)is employed to analyze vibration signals obtained from the motor body.An improved continuous wavelet trartsform was proposed to alleviate the frequency aliasing.Experimental results show that the proposed method can effectively distinguish two types of faults,single-fault of eccentricity and mixed-fault of eccentricity plus rub.

  12. Multisensor Data Fusion for Automotive Engine Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    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.

  13. Analog fault diagnosis by inverse problem technique

    KAUST Repository

    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.

  14. Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

    OpenAIRE

    Weigang Wen; Gao, Robert X.; Weidong Cheng

    2016-01-01

    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 w...

  15. 基于键合图的动态系统故障诊断方法综述%Summarization of Fault Diagnosis Method of Dynamic System Based on Bond Graphs

    Institute of Scientific and Technical Information of China (English)

    杨永利; 丛华; 张丽霞; 冯辅周; 王敏; 宋美球

    2015-01-01

    Bond graphs can be used to build universal models for energy conservation transitions between different energy domains,and the models built contain causality of all components.Bond graphs based fault diagnosis is a kind of fault diagnosis method based on model,consisting of qualitative and quantita-tive bond graphs based fault diagnosis.The principles,advantages and disadvantages and applications of qualitative and quantitative bond graphs based fault diagnosis are analyzed,and the applications of bond graphs on hybrid dynamic systems are stressed for introduction.Finally,the developmental trend of bond graphs based fault diagnosis is forecasted.%键合图可对系统中不同能域的守恒变换过程进行统一建模,其模型包含各部件的因果关系。基于键合图的故障诊断方法是一种基于模型的故障诊断方法,分为定,和定量2种方法。对这2种方法的原理、优缺点以及应用情况进行了分析,重点介绍了键合图在混合动态系统故障诊断中的应用,最后对其发展趋势进行了展望。

  16. Application of Ferrography to Fault Diagnosis of Hydraulic Systems

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    This paper deals with research on the successful use of ferrography as a wear measurement method for condition monitoring and fault diagnosis of hydraulic systems.The analysis program and progression is discussed, and a case study for condition monitoring and fault diagnosis of hydraulic systems by means of ferrography is also reviewed.

  17. A Complete Analytic Model for Fault Diagnosis of Power Systems

    Institute of Scientific and Technical Information of China (English)

    LIU Daobing; GU Xueping; LI Haipeng

    2011-01-01

    Interconnections of the modem bulk electric power systems, while contributing to the operating economy and reliability by means of mutual assistance between the subsystems, result in an increased complexity of fault diagnosis and a more serious consequence of misdiagnosis. The online fault diagnosis has become a more challenging problem for dispatchers to operate a power system securely,

  18. Data-Driven Adaptive Observer for Fault Diagnosis

    OpenAIRE

    Shen Yin; Xuebo Yang; Hamid Reza Karimi

    2012-01-01

    This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After...

  19. Study of Fault Diagnosis Method for Wind Turbine with Decision Classification Algorithms and Expert System

    Directory of Open Access Journals (Sweden)

    Feng Yongxin

    2012-09-01

    Full Text Available Study on the fault diagnosis method through the combination of decision classification algorithms and expert system. The method of extracting diagnosis rules with the CTree software was given, and a fault diagnosis system based on CLIPS was developed. In order to verify the feasibility of the method, at first the sample data was got through the simulations under fault of direct-drive wind turbine and gearbox, then the diagnosis rules was extracted with the CTree software, at last the fault diagnosis system proposed and the rules was used to extracted to diagnose the fault simulated. Test results showed that the misdiagnosis rate both within 5%, thus the feasibility of the method was verified.

  20. Research on Transformer Fault Based on Probabilistic Neural Network

    Directory of Open Access Journals (Sweden)

    Li Yingshun

    2015-01-01

    Full Text Available With the development of computer science and technology, and increasingly intelligent industrial production, the application of big data in industry also advances rapidly, and the development of artificial intelligence in the aspect of fault diagnosis is particularly prominent. On the basis of MATLAB platform, this paper constructs a fault diagnosis expert system of artificial intelligence machine based on the probabilistic neural network, and it also carries out a simulation of production process by the use of bionic algorithm. This paper makes a diagnosis of transformer fault by the use of an expert system developed by this paper, and verifies that the probabilistic neural network has a good convergence, fault-tolerant ability and big data handling capability in the fault diagnosis. It is suitable for industrial production, which can provide a reliable mathematical model for the construction of fault diagnosis expert system in the industrial production.

  1. Application of Petri Net to Fault Diagnosis in Satellite

    Institute of Scientific and Technical Information of China (English)

    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.

  2. Rough Set Theory Based Approach for Fault Diagnosis Rule Extraction of Distribution System%基于粗糙集理论的配电网故障诊断规则提取方法

    Institute of Scientific and Technical Information of China (English)

    周永勇; 周湶; 刘佳宾

    2008-01-01

    As the first step of service restoration of distribution system, rapid fault diagnosis is a significant task for reducing power outage time, decreasing outage loss, and subsequently improving service reliability and safety. This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task. Aiming at this reduction problem, a heuristic reduction algorithm based on attribution length and frequency is proposed. At the same time, the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction. Meanwhile, a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking. Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise. Moreover, it needs less calculation towards specific discernibility matrix, and thus avoids the corresponding NP hard problem. The whole process is realized by MATLAB programming. A simulation example shows that the method has a fast calculation speed, and the extracted rules can reflect the characteristic of fault with a concise form. The rule database, formed by different reduction of decision table, can diagnose single fault and multi-faults efficiently, and give satisfied results even when the existed information is incomplete. The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.

  3. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Actuators and Sensors

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.;

    2016-01-01

    This paper presents a novel scheme for diagnosis of faults affecting the sensors measuring the satellite attitude, body angular velocity and flywheel spin rates as well as defects related to the control torques provided by satellite reaction wheels. A nonlinear geometric design is used to avoid...... that aerodynamic disturbance torques have unwanted influence on the residuals exploited for fault detection and isolation. Radial basis function neural networks are used to obtain fault estimation filters that do not need a priori information about the fault internal models. Simulation results are based...... on a detailed nonlinear satellite model with embedded disturbance description. The results document the efficacy of the proposed diagnosis scheme....

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

    Directory of Open Access Journals (Sweden)

    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.

  5. Reliability of measured data for pH sensor arrays with fault diagnosis and data fusion based on LabVIEW.

    Science.gov (United States)

    Liao, Yi-Hung; Chou, Jung-Chuan; Lin, Chin-Yi

    2013-12-13

    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 diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2) sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

  6. Reliability of Measured Data for pH Sensor Arrays with Fault Diagnosis and Data Fusion Based on LabVIEW

    Directory of Open Access Journals (Sweden)

    Yi-Hung Liao

    2013-12-01

    Full Text Available 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 diagnosis to remove any faulty sensors in advance, and then proceed with data fusion in the sensor array. The average, self-adaptive and coefficient of variance data fusion methods are used in this study. The pH electrode is fabricated with ruthenium dioxide (RuO2 sensing membrane using a sputtering system to deposit it onto a silicon substrate, and eight RuO2 pH electrodes are fabricated to form a sensor array for this study.

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

    CERN Document Server

    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...

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

    DEFF Research Database (Denmark)

    Hansen, Søren; Blanke, Mogens

    2013-01-01

    Diagnosis of actuator faults is crucial for aircraft since loss of actuation can have catastrophic consequences. For autonomous aircraft the steps necessary to achieve fault tolerance is limited when only basic and non-redundant sensor and actuators suites are present. Through diagnosis...... that exploits analytical redundancies it is, nevertheless, possible to cheaply enhance the level of safety. This paper presents a method for diagnosing control surface faults by using basic sensors and hardware available on an autonomous aircraft. The capability of fault diagnosis is demonstrated obtaining...... 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....

  9. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine

    Science.gov (United States)

    Mao, Wentao; He, Ling; Yan, Yunju; Wang, Jinwan

    2017-01-01

    Diagnosis of bearings generally plays an important role in fault diagnosis of mechanical system, and machine learning has been a promising tool in this field. In many real applications of bearings fault diagnosis, the data tend to be online imbalanced, which means, the number of fault data is much less than the normal data while they are all collected in online sequential way. Suffering from this problem, many traditional diagnosis methods will get low accuracy of fault data which acts as the minority class in the collected bearing data. To address this problem, an online sequential prediction method for imbalanced fault diagnosis problem is proposed based on extreme learning machine. This method introduces the principal curve and granulation division to simulate the flow distribution and overall distribution characteristics of fault data, respectively. Then a confident over-sampling and under-sampling process is proposed to establish the initial offline diagnosis model. In online stage, the obtained granules and principal curves are rebuilt on the bearing data which are arrived in sequence, and after the over-sampling and under-sampling process, the balanced sample set is formed to update the diagnosis model dynamically. A theoretical analysis is provided and proves that, even existing information loss, the proposed method has lower bound of the model reliability. Simulation experiments are conducted on IMS bearing data and CWRU bearing data. The comparative results demonstrate that the proposed method can improve the fault diagnosis accuracy with better effectiveness and robustness than other algorithms.

  10. A Combined Fault Diagnosis Method for Power Transformer in Big Data Environment

    Directory of Open Access Journals (Sweden)

    Yan Wang

    2017-01-01

    Full Text Available The fault diagnosis method based on dissolved gas analysis (DGA is of great significance to detect the potential faults of the transformer and improve the security of the power system. The DGA data of transformer in smart grid have the characteristics of large quantity, multiple types, and low value density. In view of DGA big data’s characteristics, the paper first proposes a new combined fault diagnosis method for transformer, in which a variety of fault diagnosis models are used to make a preliminary diagnosis, and then the support vector machine is used to make the second diagnosis. The method adopts the intelligent complementary and blending thought, which overcomes the shortcomings of single diagnosis model in transformer fault diagnosis, and improves the diagnostic accuracy and the scope of application of the model. Then, the training and deployment strategy of the combined diagnosis model is designed based on Storm and Spark platform, which provides a solution for the transformer fault diagnosis in big data environment.

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

    Directory of Open Access Journals (Sweden)

    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. Robust On-Line Fault Diagnosis for Nonlinear Difference-Algebraic Systems Using Least Squares Estimate

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    A new robust on-line fault diagnosis method based on least squares estimate for nonlinear difference-algebraic systems (DAS) with uncertainties is proposed. Based on the known nominal model of the DAS, this method firstly constructs an auxiliary system consisting of a difference equation and an algebraic equation, then, based on the relationship between the state deviation and the faults in the difference equation and the relationship between the algebraic variable deviation and the faults in algebraic equation, it identifies the faults on-line through least squares estimate. This method can not only detect, isolate and identify faults for DAS, but also give the upper bound of the error of fault identification. The simulation results indicate that it can give satisfactory diagnostic results for both abrupt and incipient faults.

  13. Damage/fault diagnosis in an operating wind turbine under uncertainty via a vibration response Gaussian mixture random coefficient model based framework

    Science.gov (United States)

    Avendaño-Valencia, Luis David; Fassois, Spilios D.

    2017-07-01

    The study focuses on vibration response based health monitoring for an operating wind turbine, which features time-dependent dynamics under environmental and operational uncertainty. A Gaussian Mixture Model Random Coefficient (GMM-RC) model based Structural Health Monitoring framework postulated in a companion paper is adopted and assessed. The assessment is based on vibration response signals obtained from a simulated offshore 5 MW wind turbine. The non-stationarity in the vibration signals originates from the continually evolving, due to blade rotation, inertial properties, as well as the wind characteristics, while uncertainty is introduced by random variations of the wind speed within the range of 10-20 m/s. Monte Carlo simulations are performed using six distinct structural states, including the healthy state and five types of damage/fault in the tower, the blades, and the transmission, with each one of them characterized by four distinct levels. Random vibration response modeling and damage diagnosis are illustrated, along with pertinent comparisons with state-of-the-art diagnosis methods. The results demonstrate consistently good performance of the GMM-RC model based framework, offering significant performance improvements over state-of-the-art methods. Most damage types and levels are shown to be properly diagnosed using a single vibration sensor.

  14. Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

    Directory of Open Access Journals (Sweden)

    Jian-wei Yang

    2015-01-01

    Full Text Available Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs, such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1 Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2 Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.

  15. Application of MBAM Neural Network in CNC Machine Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    宋刚; 胡德金

    2004-01-01

    In order to improve the bidirectional associative memory (BAM) performance, a modified BAM model (MBAM) is used to enhance neural network(NN)'s memory capacity and error correction capability, theoretical analysis and experiment results illuminate that MBAM performs much better than the original BAM. The MBAM is used in computer numeric control(CNC) machine fault diagnosis, it not only can complete fault diagnosis correctly but also have fairly high error correction capability for disturbed Input Information sequence. Moreover MBAM model is a more convenient and effective method of solving the problem of CNC electric system fault diagnosis.

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

    CERN Document Server

    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.

  17. The Design and Implementation of a Remote Fault Reasoning Diagnosis System for Meteorological Satellites Data Acquisition

    Directory of Open Access Journals (Sweden)

    Zhu Jie

    2017-01-01

    Full Text Available Under the background of the trouble shooting requirements of FENGYUN-3 (FY-3 meteorological satellites data acquisition in domestic and oversea ground stations, a remote fault reasoning diagnosis system is developed by Java 1.6 in eclipse 3.6 platform. The general framework is analyzed, the workflow is introduced. Based on the system, it can realize the remote and centralized monitoring of equipment running status in ground stations,triggering automatic fault diagnosis and rule based fault reasoning by parsing the equipment quality logs, generating trouble tickets and importing expert experience database, providing text and graphics query methods. Through the practical verification, the system can assist knowledge engineers in remote precise and rapid fault location with friendly graphical user interface, boost the fault diagnosis efficiency, enhance the remote monitoring ability of integrity operating control system. The system has a certain practical significance to improve reliability of FY-3 meteorological satellites data acquisition.

  18. Research on Gear-broken Fault Diagnosis in a Tank Gearbox

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    A fault diagnosis method of working position gear in a tank gearbox is put forward based on simulating the fault of working position gear in an actual tank, extracting the envelope of vibration signal by Hilbert transformation amplitude demodulation method, and zooming the low-frequency band to envelope signal.

  19. Fault Diagnosis of Ultrasonic Motor Based on Correlation Dimension%关联维数的超声波电机故障诊断

    Institute of Scientific and Technical Information of China (English)

    王理停; 黄宜坚

    2011-01-01

    利用关联维数分析方法对超声波电机进行故障诊断,并针对故障诊断的实际情况,建构特殊实验平台采集故障振动信号.通过互信息法及Cao方法来确定相空间重构的2个重要参数,然后计算超声波电机在不同故障状态下的关联维数.结果表明,不同故障状态产生机制不同,其关联维数也不同,关联维数可用于故障的特征提取.通过分析不同状态下超声波电机的关联维数,可以判断超声波电机是否出现故障.%The analysis method of correlation dimension is used in the fault diagnosis of ultrasonic motor, and a special experiment platform is built to collect fault vibration signal according to the actual conditions in fault diagnosis. The correlation dimensions of the different fault of ultrasonic motor were calculated through two important parameters of reconstruction of the phase space which were determined by the mutual information and Cao method. The results show that the mechanism of different faults for ultrasonic motor are related with their different correlation dimensions, so the correlation dimension can be used to extract the feature of the faults. By the analysis of the correlation dimension of different fault vibration signals, it can be judged whether there is a fault for ultrasonic motor or not.

  20. An Embedded Condition Monitoring and Fault Diagnosis System for Rotary Machines

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    An intelligent machine is the earnest aspiration of people. From the point of view to construct an intelligent machine with self-monitoring and self-diagnosis abilities, the technology for realizing an internet oriented embedded intelligent condition monitoring and fault diagnosis system for the rotating machine with remote monitoring, diagnosis, maintenance and upgrading functions is introduced systematically. Based on the DSP ( Digital Signal Processor) and embedded microcomputer, the system can measure and store the machine work status in real time, such as the rotating speed and vibration,etc. In the system, the DSP chip is used to do the fault signal processing and feature extraction, and the embedded microcomputer with a customized Linux operation system is used to realize the internet oriented remote software upgrading and system maintenance. Embedded fault diagnosis software based on mobile agent technology is also designed in the system, which can interconnect with the remote fault diagnosis center to realize the collaborative diagnosis. The embedded condition monitoring and fault diagnosis technology proposed in this paper will effectively improve the intelligence degree of the fault diagnosis system.

  1. 基于Apriori关联规则的汽轮机振动监测与故障诊断%Steam Turbine Vibration Based on the Apriori Association Rules Monitoring and Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    刘淑艳

    2012-01-01

    In the power plant steam turbine vibration fault depends only on the vibration parameters of fault diagnosis and diagnostic time is long and the diagnosis of the cause and site specific features, is proposed based on the association rules of steam turbine vibration monitoring and fault diagnosis method. Analysis of the steam turbine vibration fault related to vibration parameters and thermal parameters, on the thermodynamic parameters and vibration parameters associated with the fault diagnosis rules, established the running status monitoring and fault diagnosis system design. Through the actual test proved that this method has very strong practical and feasible, for thermal power plant steam turbine vibration fault of equipment research and development and improvement have draw lessons from a meaning.%针对火电厂汽轮机发生振动故障时仅依靠振动参数进行故障诊断而产生诊断时间长与诊断的原因与部位不具体的问题,提出了基于关联规则的汽轮机振动监测与故障诊断方法;分析了汽轮机振动故障产生时涉及到的振动参数与热力参数,研究了将热力参数与振动参数关联结合的故障诊断规则,确立了状态运行监测与故障诊断的系统设计思路;通过实际验证证明这种方法具有很强的实用型与可行性,对火电厂汽轮机振动故障设备的研发与改进有借鉴意义.

  2. Diagnosis of airspeed measurement faults for unmanned aerial vehicles

    DEFF Research Database (Denmark)

    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...

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

    CERN Document Server

    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

  4. Genetic algorithm based on optimization of neural network structure for fault diagnosis of the clutch retainer mechanism of MF 285 tractor

    Directory of Open Access Journals (Sweden)

    S. F Mousavi

    2016-09-01

    Full Text Available Introduction The diagnosis of agricultural machinery faults must be performed at an opportune time, in order to fulfill the agricultural operations in a timely manner and to optimize the accuracy and the integrity of a system, proper monitoring and fault diagnosis of the rotating parts is required. With development of fault diagnosis methods of rotating equipment, especially bearing failure, the security, performance and availability of machines has been increasing. In general, fault detection is conducted through a specific procedure which starts with data acquisition and continues with features extraction, and subsequently failure of the machine would be detected. Several practical methods have been introduced for fault detection in rotating parts of machineries. The review of the literature shows that both Artificial Neural Networks (ANN and Support Vector Machines (SVM have been used for this purpose. However, the results show that SVM is more effective than Artificial Neural Networks in fault detection of such machineries. In some smart detection systems, incorporating an optimized method such as Genetic Algorithm in the Neural Network model, could improve the fault detection procedure. Consequently, the fault detection performance of neural networks may also be improved by combining with the Genetic Algorithm and hence will be comparable with the performance of the Support Vector Machine. In this study, the so called Genetic Algorithm (GA method was used to optimize the structure of the Artificial Neural Networks (ANN for fault detection of the clutch retainer mechanism of Massey Ferguson 285 tractor. Materials and Methods The test rig consists of some electro mechanical parts including the clutch retainer mechanism of Massey Ferguson 285 tractor, a supporting shaft, a single-phase electric motor, a loading mechanism to model the load of the tractor clutch and the corresponding power train gears. The data acquisition section consists of a

  5. Fault diagnosis of a Wind Turbine Rotor using a Multi-blade Coordinate Framework

    DEFF Research Database (Denmark)

    Henriksen, Lars Christian; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2012-01-01

    Fault diagnosis of a wind turbine rotor is considered. The faults considered are sensor faults and blades mounted with a pitch offset. A fault at a single blade will result in asymmetries in the rotor, which can be applied for fault diagnosis. The diagnosis is derived by using the multiblade coor...

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

    Data.gov (United States)

    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...

  7. Fault Detection and Diagnosis Techniques for Liquid-Propellant Rocket Propellant Engines

    Science.gov (United States)

    Wua, Jianjun; Tanb, Songlin

    2002-01-01

    Fault detection and diagnosis plays a pivotal role in the health-monitoring techniques for liquid- propellant rocket engines. This paper firstly gives a brief summary on the techniques of fault detection and diagnosis utilized in liquid-propellant rocket engines. Then, the applications of fault detection and diagnosis algorithms studied and developed to the Long March Main Engine System(LMME) are introduced. For fault detection, an analytical model-based detection algorithm, a time-series-analysis algorithm and a startup- transient detection algorithm based on nonlinear identification developed and evaluated through ground-test data of the LMME are given. For fault diagnosis, neural-network approaches, nonlinear-static-models based methods, and knowledge-based intelligent approaches are presented. Keywords: Fault detection; Fault diagnosis; Health monitoring; Neural networks; Fuzzy logic; Expert system; Long March main engines Contact author and full address: Dr. Jianjun Wu Department of Astronautical Engineering School of Aerospace and Material Engineering National University of Defense Technology Changsha, Hunan 410073 P.R.China Tel:86-731-4556611(O), 4573175(O), 2219923(H) Fax:86-731-4512301 E-mail:jjwu@nudt.edu.cn

  8. Fault Diagnosis of Analog Circuit Based on Extension Theory%基于可拓理论的模拟电路故障诊断方法

    Institute of Scientific and Technical Information of China (English)

    杜占龙; 谭业双; 甘彤

    2011-01-01

    针对模拟电路存在较多故障模式的诊断中易出现分类混叠的问题,提出一种基于可拓理论的故障诊断方法;建立定性地描述模拟电路故障诊断的物元模型,引入可拓集合中的关联函数和相关度;将响应信号进行小波分解提取其各层能量作为故障特征,并利用变尺度的混沌遗传算法优化各故障特征的权重系数,最后定量地计算各故障状态的可能程度;利用实验电路将该方法与另外两种诊断方法比较,实验结果表明,该方法故障分类正确率最高,耗时最短,从而可以证明该方法的有效性.%Aiming at overlapped recognition on analog circuit fault diagnosis with large number of fault categories, this paper presented a fault identification based on extension theory. A matter-element model for analog circuit was established and the correlation functions and correlation value in extension set was introduced. The response signal was decomposed by wavelet and the energy of each layer from wavelet was distilled. Weight parameters were optimized by chaotic genetic algorithm of mutative scale. The failure probability of any possible fault state was calculated. The proposed method and another two methods have been tested on a test circuit. Results show that compared with another two methods, classified rate of the proposed method was the highest and the time used was the least. It confirmed the proposed method effective.

  9. Robust Fault Diagnosis Algorithm for a Class of Nonlinear Systems

    Directory of Open Access Journals (Sweden)

    Hai-gang Xu

    2015-01-01

    Full Text Available A kind of robust fault diagnosis algorithm to Lipschitz nonlinear system is proposed. The novel disturbances constraint condition of the nonlinear system is derived by group algebra method, and the novel constraint condition can meet the system stability performance. Besides, the defined robust performance index of fault diagnosis observer guarantees the robust. Finally, the effectiveness of the algorithm proposed is proved in the simulations.

  10. Flotation Fault Detection and Diagnosis Method Based on Output PDF%基于输出PDF的浮选故障检测和诊断方法

    Institute of Scientific and Technical Information of China (English)

    桂卫华; 杜建江; 许灿辉; 阳春华

    2012-01-01

    计对浮选中泡沫尺寸分布的特殊性,如非高斯分布,左偏斜,高峰值等,常规分析方法无法准确描述尺寸分布的特点,因此无法准确检测和诊断浮选过程中出现的故障.提出对泡沫尺寸分布的输出概率密度函数(PDF)的统计分析,形成了一种新的浮选过程故障检测和诊断方法.通过采用自设计的核方法逼近将输出PDF转化为动态权系数,建立带有时滞的非线性不确定性权动态模型,基于线性矩阵不等式设计得到可行的故障检测和诊断算法.通过仿真验证分析,证明此算法的有效性.结合现场浮选过程,讨论了此方法的应用前景和优势.%Considering the fact that bubble size distribution in mineral flotation is found to be non-Gaussian and highly left-skewed with spike, the conventional analysis methods are unable to describe these characteristics which are significant for detecting and diagnosing process fault in flotation. It puts forward a statistical analysis on the output probability density function ( PDF) of bubble size distribution. Then a new method of fault detection and diagnosis is formed. By means of estimating the output PDF of bubble size using kernel method, the PDF is transformed into dynamic weight coefficients, based on which the uncertain nonlinear dynamic models with time delay are established. According to Linear matrix inequality (LMI) a feasible fault detection and diagnosis algorithm can be obtained. The simulation results verify the effectiveness of the proposed algorithm. With the flotation process, the prospects and advantages of this method is discussed.

  11. Intelligent Fault Diagnosis in Power Transformer with Using Dissolved Gas Analysis in different Standards by Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Rahmat Houshmand

    2007-09-01

    Full Text Available The power electric transformer fault diagnosis is based on dissolved gas-in-oil analysis (DGA. the conventional fault diagnosis methods, i.e. the ratio methods (Rogers, Dornenburg and IEC and the key gas method, have limitations such as the “no decision” problem. Various artificial intelligence techniques may help solve the problems and present a better solution. In this paper present a fuzzy systems to fault diagnosis in power electric transformer by dissolved we gas analysis.

  12. Wavelet Transform and Neural Networks in Fault Diagnosis of a Motor Rotor

    Institute of Scientific and Technical Information of China (English)

    RONG Ming-xing

    2012-01-01

    In the motor fault diagnosis technique, vibration and stator current frequency components of detection are two main means. This article will discuss the signal detection method based on vibration fault. Because the motor vibration signal is a non-stationary random signal, fault signals often contain a lot of time-varying, burst proper- ties of ingredients. The traditional Fourier signal analysis can not effectively extract the motor fault characteristics, but are also likely to be rich in failure information but a weak signal as noise. Therefore, we introduce wavelet packet transforms to extract the fault characteristics of the signal information. Obtained was the result as the neural network input signal, using the L-M neural network optimization method for training, and then used the BP net- work for fault recognition. This paper uses Matlab software to simulate and confirmed the method of motor fault di- agnosis validity and accuracy

  13. Machinery fault diagnosis using joint global and local/nonlocal discriminant analysis with selective ensemble learning

    Science.gov (United States)

    Yu, Jianbo

    2016-11-01

    The vibration signals of faulty machine are generally non-stationary and nonlinear under those complicated working conditions. Thus, it is a big challenge to extract and select the effective features from vibration signals for machinery fault diagnosis. This paper proposes a new manifold learning algorithm, joint global and local/nonlocal discriminant analysis (GLNDA), which aims to extract effective intrinsic geometrical information from the given vibration data. Comparisons with other regular methods, principal component analysis (PCA), local preserving projection (LPP), linear discriminant analysis (LDA) and local LDA (LLDA), illustrate the superiority of GLNDA in machinery fault diagnosis. Based on the extracted information by GLNDA, a GLNDA-based Fisher discriminant rule (FDR) is put forward and applied to machinery fault diagnosis without additional recognizer construction procedure. By importing Bagging into GLNDA score-based feature selection and FDR, a novel manifold ensemble method (selective GLNDA ensemble, SE-GLNDA) is investigated for machinery fault diagnosis. The motivation for developing ensemble of manifold learning components is that it can achieve higher accuracy and applicability than single component in machinery fault diagnosis. The effectiveness of the SE-GLNDA-based fault diagnosis method has been verified by experimental results from bearing full life testers.

  14. Fault diagnosis with neural networks. Part 1: Trajectory recognition

    Directory of Open Access Journals (Sweden)

    Enrique Eduardo Tarifa

    2010-04-01

    Full Text Available The present investigation was focused on formulating a method for designing a fault diagnosis system for chemical plants by using artificial neural networks. Fault diagnosis is aimed at identifying a fault which affects a given process by analysing the signs supplied by process sensors. Neuronal networks are mathematical models which try to imitate the functioning of the human brain. A neural network is defined by its structure and the learning method used. The difficulty with diagnosing faults lies in recognising the tralectories (temporal series of data followed by process variables when a fault affects the process; when tralectories are recognised, the associated fault is also identified. The theory so developed recommended an optimised structure and training method for the neural networks to use. Both the proposed structure and the training method were tested by carrying out comparative studies between traditional structures and a training method. The results showed the superiority of the neural networks designed and trained with the method proposed in this work. Except for simple processes, fault diagnosis is a more complex problem than simply identifying tralectories, because a fault may cause an infinite set of tralectories (i.e. flow. The fundaments established in this work are thus used in Part II, where the analysis is extended to recognise flows.

  15. Research on CLIPS-based Fault Diagnosis Expert System of Radar%基于CLIPS的雷达故障诊断专家系统研究

    Institute of Scientific and Technical Information of China (English)

    吴立蓉; 王仁波

    2014-01-01

    The problem about radar maintenance in army mostly relies on the factory’s support. The timeliness and ef-fectiveness cannot satisfy the need of force. The design uses the artificial intelligence technology for building the fault di-agnosis Expert System based on CLIPS, make the general maintenance personnel could use and master the diagnosis tech-nology. That has great practical significance for improving the integrated supporting capability and increasing battle effec-tiveness of radar .%基层部队雷达维护保养,大部分是依靠工厂支援,及时性和有效性都不能满足部队需要。本设计利用人工智能技术来建造基于CLIPS故障诊断专家系统,使诊断技术为一般的维护人员所掌握和使用,这对提高该雷达综合保障力,促进其战斗力的形成都具有重大的现实意义。

  16. Study on BSS Algorithm used on Fault Diagnosis of Gearbox

    Directory of Open Access Journals (Sweden)

    Yu Chen

    2013-06-01

    Full Text Available The gearbox is a complicated rotating machinery equipment, in order to realize the gearbox fault early detection and prevention, it is the key to carry out the online diagnosis. This paper used the adaptive variable step-length natural gradient blind source separation algorithm to realize the helicopter gearbox meshing vibration signal and fault vibration signal effective separation. Through the algorithm simulation, the accuracy of the algorithm gained the verification and the separation error trended to zero, which has higher separation precision. This algorithm can realize the complicated mechanical vibration signal blind source separation and fault diagnosis, which has a broad application prospect.

  17. Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

    Directory of Open Access Journals (Sweden)

    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.

  18. Fault detection and diagnosis of diesel engine valve trains

    Science.gov (United States)

    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.

  19. Research on the Diagnosis Decision-making of Key Units Based on Ontology and Fault Tree%基于本体故障树的关键机组诊断决策研究

    Institute of Scientific and Technical Information of China (English)

    于德介; 赵丹; 周安美

    2013-01-01

    In order to meet the requirements of locating fault causes of continuous work equipments or units in petrochemical enterprise quickly,an ontology based fault tree construction method was proposed.In the proposed method,the fault tree is generated from domain ontology,and then the ratio of efficiency to time for fault diagnosis is obtained through the quantitative analysis of the fault tree.At last,the optimal fault diagnosis path can be obtained according to the descending order of the ratio of efficiency to time for fault diagnosis.This method combines the advantages of ontology and fault tree,and it realizes rapid fault causes locating based on knowledge sharing and reuse.Application example shows that the efficiency of fault diagnosis can be increased and the costs of enterprise's maintenance can be reduced by using the proposed method.%为了满足石化企业连续性工作设备或机组在发生故障后对故障原因进行快速定位的要求,将本体先进的知识表示方法引入到成熟的故障树研究中,提出了基于本体的故障树构建方法,并通过对生成的故障树进行定量分析,计算出故障判明效时比,以其从大到小的顺序为依据找到故障诊断最优路径,实现了本体和故障树的优势结合.该方法在知识共享和重用的基础上,实现对故障的快速诊断定位,从而提高了故障诊断效率,减少了企业的生产维护成本.

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

    Science.gov (United States)

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

    2017-03-01

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

  1. Application of Extension Neural Network Type-1 to Fault Diagnosis of Electronic Circuits

    Directory of Open Access Journals (Sweden)

    Meng-Hui Wang

    2012-01-01

    Full Text Available The values of electronic components are always deviated, but the functions of the modern circuits are more and more precise, which makes the automatic fault diagnosis of analog circuits very complex and difficult. This paper presents an extension-neural-network-type-1-(ENN-1- based method for fault diagnosis of analog circuits. This proposed method combines the extension theory and neural networks to create a novel neural network. Using the matter-element models of fault types and a correlation function, can be calculated the correlation degree between the tested pattern and every fault type; then, the cause of the circuit malfunction can be directly diagnosed by the analysis of the correlation degree. The experimental results show that the proposed method has a high diagnostic accuracy and is more fault tolerant than the multilayer neural network (MNN and the k-means based methods.

  2. Case-Based Fuzzy Fault Diagnosis System for UAV%基于案例模糊的无人机故障诊断系统

    Institute of Scientific and Technical Information of China (English)

    季文韬; 陈汶滨; 张平

    2011-01-01

    UAV is a complex mechatronic system, in order to meet the needs of fault diagnosis, based on the theory of fuzzy mathematics, and integrate case-based reasoning into fuzzy inference machine.This paper focuses on how to determine the fuzzy relationship matrix and the principle of case-based reasoning, described in details of the case matching process, put over a new fuzzy inference mechanism, ultimately, design and implementation of system functions.It is shown from application results that this method improve the diagnostic process efficiency, accuracy and reliability.%无人机是一个结构复杂的机电一体化系统,为了满足故障诊断的需求,以模糊数学理论为基础,并将案例推理融合到模糊推理机中.重点论述如何确定模糊关系矩阵和案例式推理的原理,详细介绍了案例匹配流程,提出了一种新的模糊推理机制,并最终设计实现系统功能,提高了无人机故障诊断效率、准确度、可靠性.

  3. A Novel Data Hierarchical Fusion Method for Gas Turbine Engine Performance Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Feng Lu

    2016-10-01

    Full Text Available Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop data-based fusion methods, such as neural networks fusion, while little research has focused on fusion architecture or the fusion of different method kinds. In this paper, a data hierarchical fusion using improved weighted Dempster–Shaffer evidence theory (WDS is proposed, and the integration of data-based and model-based methods is presented for engine gas-path fault diagnosis. For the purpose of simplifying learning machine typology, a recursive reduced kernel based extreme learning machine (RR-KELM is developed to produce the fault probability, which is considered as the data-based evidence. Meanwhile, the model-based evidence is achieved using particle filter-fuzzy logic algorithm (PF-FL by engine health estimation and component fault location in feature level. The outputs of two evidences are integrated using WDS evidence theory in decision level to reach a final recognition decision of gas-path fault pattern. The characteristics and advantages of two evidences are analyzed and used as guidelines for data hierarchical fusion framework. Our goal is that the proposed methodology provides much better performance of gas-path fault diagnosis compared to solely relying on data-based or model-based method. The hierarchical fusion framework is evaluated in terms to fault diagnosis accuracy and robustness through a case study involving fault mode dataset of a turbofan engine that is generated by the general gas turbine simulation. These applications confirm the effectiveness and usefulness of the proposed approach.

  4. Fault Estimation

    DEFF Research Database (Denmark)

    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.......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...

  5. 基于概率神经网络模型的异步电机故障诊断%Based on Probabilistic Neural Network Model for Asynchronous Motor Fault Diagnosis

    Institute of Scientific and Technical Information of China (English)

    李天玉; 吴楠

    2013-01-01

    针对传统故障诊断方法的局限性,提出一种基于概率神经网络(PNN)的诊断方法.以异步电机转子断条、偏心、失电残压等故障为例进行了诊断研究,通过选取故障样本来训练PNN,将故障信息输入训练好的PNN模型后,由输出结果即可判断发生的故障种类.MATLAB仿真表明,基于PNN的电机故障诊断方法能有效识别出电机故障,故障诊断准确率高,易于工程实现.但神经网络还处于发展阶段,仍有不少问题需进一步研究.%According to the limitation of traditional fault diagnosis method, a diagnosis method based on probabilistic neural network was proposed. An example of asynchronous motor rotor with broken, eccentric, electric residual pressure fault was done. By choosing fault samples to train PNN, and then inputting the diagnosis information to the trained model of PNN, the occurred fault types could be judged from the output results. MATLAB simulation showed that diagnosis method of the motor based on probabilistic neural network could effectively identify motor fault and the fault diagnostic accuracy rate was so high that it could be easily implemented in engineering projection. But as neural network itself was undergoing developing, many problems need to be further studied.

  6. Fault diagnosis for micro-gas turbine engine sensors via wavelet entropy.

    Science.gov (United States)

    Yu, Bing; Liu, Dongdong; Zhang, Tianhong

    2011-01-01

    Sensor fault diagnosis is necessary to ensure the normal operation of a gas turbine system. However, the existing methods require too many resources and this need can't be satisfied in some occasions. Since the sensor readings are directly affected by sensor state, sensor fault diagnosis can be performed by extracting features of the measured signals. This paper proposes a novel fault diagnosis method for sensors based on wavelet entropy. Based on the wavelet theory, wavelet decomposition is utilized to decompose the signal in different scales. Then the instantaneous wavelet energy entropy (IWEE) and instantaneous wavelet singular entropy (IWSE) are defined based on the previous wavelet entropy theory. Subsequently, a fault diagnosis method for gas turbine sensors is proposed based on the results of a numerically simulated example. Then, experiments on this method are carried out on a real micro gas turbine engine. In the experiment, four types of faults with different magnitudes are presented. The experimental results show that the proposed method for sensor fault diagnosis is efficient.

  7. Fault diagnosis and prognostic of solid oxide fuel cells

    Science.gov (United States)

    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%.

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

    Energy Technology Data Exchange (ETDEWEB)

    Zhou Zhiwei, E-mail: zzw@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Zhuang Ming, E-mail: zhm@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Lu Xiaofei, E-mail: luxf1212@mail.ustc.edu.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Hu Liangbing, E-mail: huliangbing@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China); Xia Genhai, E-mail: xgh@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei, Anhui (China)

    2012-12-15

    Highlights: Black-Right-Pointing-Pointer An expert system of real-time fault diagnosis for EAST cryoplant is designed. Black-Right-Pointing-Pointer Knowledge base is built via fault tree analysis based on our fault experience. Black-Right-Pointing-Pointer It can make up the deficiency of safety monitoring in cryogenic DCS. Black-Right-Pointing-Pointer It can help operators to find the fault causes and give operation suggestion. Black-Right-Pointing-Pointer 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.

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

    CERN Document Server

    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. 基于决策融合的直驱风力发电机组轴承故障诊断%Fault Diagnosis of Spherical Roller Bearing of Direct-Drive Wind Turbine Based on Decision Fusion

    Institute of Scientific and Technical Information of China (English)

    安学利; 蒋东翔; 李少华

    2011-01-01

    Based on decision fusion of multi-source feature of vibration signal in time-domain and frequency-domain as well as its envelope spectrum, a fault diagnosis model of spherical roller bearing of direct-drive wind turbine is built. Experimental research on four states of direct-drive wind turbine including its normal operation and frequently occurred outer race fault, inner race fault and roller fault of spherical roller bearing of main shaft are performed in laboratory. In order to diagnose the faults of wind turbine bearings, some sensitive feature parameters, which possess higher fault discrimination degree and are suitable to bearing fault diagnosis of wind power generation unit, are selected. The vibration signal features of wind power unit in time-domain, frequency-domain and envelope spectrum in the frequency-domain are taken as diagnosis samples, and the preliminary diagnosis of bearing faults of the unit is carried out by gray incidence analysis; then the decision fusion of different evidences are implemented by evidence fusion theory to obtain final diagnosis result. Experimental results show that the bearing faults of wind power generation unit can be well recognized by the proposed method.%基于振动信号时域、频域和包络谱等多源特征,采用决策融合方法构建了直驱风力发电机组轴承故障诊断模型.对直驱风力发电机组主轴轴承经常发生的外圈故障、内圈故障、滚动体故障以及正常运行4种状态进行了实验研究.选取具有较高故障区分度,适合风电机组轴承故障诊断的特征参数.以风电机组振动信号的时域特征、频域特征和包络谱频域特征为诊断样本,使用灰色关联分析方法对机组轴承故障进行初步诊断,然后用证据融合理论对不同证据进行决策信息融合,从而获得最终诊断结果.实验结果表明,该方法能较好地识别风力发电机组轴承故障.

  11. Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions

    Directory of Open Access Journals (Sweden)

    Sheraz Ali Khan

    2016-01-01

    Full Text Available Traditional fault diagnosis methods of bearings detect characteristic defect frequencies in the envelope power spectrum of the vibration signal. These defect frequencies depend upon the inherently nonstationary shaft speed. Time-frequency and subband signal analysis of vibration signals has been used to deal with random variations in speed, whereas design variations require retraining a new instance of the classifier for each operating speed. This paper presents an automated approach for fault diagnosis in bearings based upon the 2D analysis of vibration acceleration signals under variable speed conditions. Images created from the vibration signals exhibit unique textures for each fault, which show minimal variation with shaft speed. Microtexture analysis of these images is used to generate distinctive fault signatures for each fault type, which can be used to detect those faults at different speeds. A k-nearest neighbor classifier trained using fault signatures generated for one operating speed is used to detect faults at all the other operating speeds. The proposed approach is tested on the bearing fault dataset of Case Western Reserve University, and the results are compared with those of a spectrum imaging-based approach.

  12. Fault Diagnosis of Broken Rotor Bars in Squirrel-Cage Induction Motor of Hoister Based on Duffing Oscillator and Multifractal Dimension

    Directory of Open Access Journals (Sweden)

    Zhike Zhao

    2014-07-01

    Full Text Available This paper is to propose a novel fault diagnosis method for broken rotor bars in squirrel-cage induction motor of hoister, which is based on duffing oscillator and multifractal dimension. Firstly, based on the analysis of the structure and performance of modified duffing oscillator, the end of transitional slope from chaotic area to large-scale cycle area is selected as the optimal critical threshold of duffing oscillator by bifurcation diagrams and Lyapunov exponent. Secondly, the phase transformation duffing oscillator from chaos to intermittent chaos is sensitive to the signals, whose frequency difference is quite weak from the reference signal. The spectrums of the largest Lyapunov exponents and bifurcation diagrams of the duffing oscillator are utilized to analyze the variance in different parameters of frequency. Finally, this paper is to analyze the characteristics of both single fractal (box-counting dimension and multifractal and make a comparison between them. Multifractal detrended fluctuation analysis is applied to detect extra frequency component of current signal. Experimental results reveal that the method is effective for early detection of broken rotor bars in squirrel-cage induction motor of hoister.

  13. 免疫检测器证据理论集成的机组复合故障诊断%Complex fault diagnosis of machine unit based on evidence theory and immune detector integrated

    Institute of Scientific and Technical Information of China (English)

    岑健; 胥布工; 张清华; 邵龙秋

    2011-01-01

    针对机组复合故障诊断准确率较低的状况,基于免疫机理的人工免疫智能方法,构建对故障比较敏感的无量纲指标免疫检测器.采用自适应调节匹配阈值和从非己空间产生的候选检测器,能有效减少黑洞和边界不清晰.通过免疫编程优化策略获得最佳识别能力的新特征指标.利用证据理论对多类免疫检测器进行集成诊断,提炼出能直接应用于复合故障诊断的优秀无量纲免疫检测器,机组实验结果表明,所得免疫检测器能快速、准确地进行复合故障诊断.%For the condition that lower accuracy exists in complex fault diagnosis of machine unit, an artificial immune intelligent method based on immune mechanism is proposed, dimensionless immune detectors are constructed and complex fault can be detect and diagnosed. Match thresholds are used and candidate detectors from nonself-space are generated, which can effectively reduce the black hole and unclear border. Immune programming is introduced into feature construct of complex fault diagnosis in order to obtain new feature parameter. A few type immune detectors are integrated and diagnosed by using evidential theory, which can directly be applied to complex fault diagnosis. These excellent immune detectors can improve the accuracy of complex fault diagnosis, and the experiment results show that complex fault can be accurately and rapidly diagnosed.

  14. Analysis of experimental result and fault diagnosis for aeroengine rotating shaft

    Science.gov (United States)

    Zhao, Baoqun; Wang, Yuanyang

    2008-10-01

    To increase the accuracy of applying traditional fault diagnosis method to aeroengine vibrant faults, a novel approach based on wavelet neural network is proposed. The effective signal features are acquired by wavelet transform with multi-resolution analysis. These feature vectors then are applied to the neural network for training and testing. The synthesized method of recursive orthogonal least squares algorithm is used to fulfill the network structure and parameter initialization. By means of choosing enough practical samples to verify the proposed network performance, the information representing the faults is inputted into the trained network. According to the output result the fault pattern can be determined. The simulation results and actual applications show that the method can effectively diagnose and analyze the vibrant fault patterns of aeroengine and the diagnosis result is correct.

  15. Solving model-based fault diagnosis with flag propagation%利用标志传播求解基于模型的故障诊断

    Institute of Scientific and Technical Information of China (English)

    欧阳丹形; 张立明; 赵剑; 白洪涛

    2011-01-01

    Model-based diagnosis is one of the active branches in artificial intelligence, which is a type of intelligent reasoning technology and it overcomes the shortcomings of traditional fault-diagnosis methods. This paper presents the concept of component output flag, then whether a component set is a diagnosis in the system or not can be driven through the propagation of the output flag. The method can compute all the minimal diagnoses directly without computing all the conflict sets and the hitting sets of the collection of the corresponding conflict sets like classical methods do. And then the combinatorial explosion caused by calling ATMS (assumption-based truth maintenance system) , known as an NP-complete problem, can be avoided as well. The computing procedure is formalized by introducing SE-Tree (set enumeration tree) to produce all the solutions gradually. Results show that the diagnosis efficiency is highly improved with this method, which satisfies real-time requirement, even for a complex system.%基于模型的诊断是一项新型的智能推理技术,是人工智能领域中一个炙手可热的研究分支.提出元件输出标志的概念,通过在系统中传播输出标志,来判断元件集合是否为系统的诊断.使用SE-Tree( set enumeration tree)形式化地描述整个计算过程,逐步生成当前系统对应的所有极小诊断.此方法不求解冲突集和碰集,直接求出所有的极小诊断,与Reiter的模型诊断方法有着本质的不同,极大地减小了诊断求解的复杂度.实验结果表明,该算法具有较好的效率,并且适用于复杂的对象的诊断问题.

  16. Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models

    Science.gov (United States)

    Sharifi, Reza; Langari, Reza

    2017-02-01

    This paper presents a methodology for sensor fault diagnosis in nonlinear systems using a Mixture of Probabilistic Principal Component Analysis (MPPCA) models. This methodology separates the measurement space into several locally linear regions, each of which is associated with a Probabilistic PCA (PPCA) model. Using the transformation associated with each PPCA model, a parity relation scheme is used to construct a residual vector. Bayesian analysis of the residuals forms the basis for detection and isolation of sensor faults across the entire range of operation of the system. The resulting method is demonstrated in its application to sensor fault diagnosis of a fully instrumented HVAC system. The results show accurate detection of sensor faults under the assumption that a single sensor is faulty.

  17. Auxiliary signal design in fault detection and diagnosis

    Science.gov (United States)

    Zhang, Xue Jun

    Fault-detection and diagnosis schemes for systems represented by linear MIMO stochastic models are developed analytically, with a focus on on the design and application of auxiliary signals. The basic principles of optimal-input design are reviewed, and consideration is given to the sequential probability ratio test (SPRT), auxiliary signals for improving SPRT fault detection, and the extension of the SPRT to multiple-hypothesis testing. Two chapters are devoted to the application of the SPRT to a model chemical plant (producing anhydrous caustic soda), including model derivation, model identification, detection of type I and type II faults, and the fault-diagnosis decision-making mechanism. Numerical results are presented in graphs and briefly characterized.

  18. Fault Diagnosis and Detection in Industrial Motor Network Environment Using Knowledge-Level Modelling Technique

    Directory of Open Access Journals (Sweden)

    Saud Altaf

    2017-01-01

    Full Text Available In this paper, broken rotor bar (BRB fault is investigated by utilizing the Motor Current Signature Analysis (MCSA method. In industrial environment, induction motor is very symmetrical, and it may have obvious electrical signal components at different fault frequencies due to their manufacturing errors, inappropriate motor installation, and other influencing factors. The misalignment experiments revealed that improper motor installation could lead to an unexpected frequency peak, which will affect the motor fault diagnosis process. Furthermore, manufacturing and operating noisy environment could also disturb the motor fault diagnosis process. This paper presents efficient supervised Artificial Neural Network (ANN learning technique that is able to identify fault type when situation of diagnosis is uncertain. Significant features are taken out from the electric current which are based on the different frequency points and associated amplitude values with fault type. The simulation results showed that the proposed technique was able to diagnose the target fault type. The ANN architecture worked well with selecting of significant number of feature data sets. It seemed that, to the results, accuracy in fault detection with features vector has been achieved through classification performance and confusion error percentage is acceptable between healthy and faulty condition of motor.

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

    Science.gov (United States)

    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.

  20. Fault Diagnosis Research for Metro Door Based on FTA%基于FTA的地铁车门故障诊断研究

    Institute of Scientific and Technical Information of China (English)

    夏军; 邢宗义; 王晓浩

    2014-01-01

    Door system is a key subsystem of metro train, so the FTA method is applied to carry out the fault diagnosis research for reducing door maintenance time and improving door reliability. On the basis of analyzing the structure and working principle of metro door system, the disabled open fault of door system is selected as a top event to establish the fault tree. The minimum cut set is calculated by the Fussell-Vesely method, then the qualitative and quantitative analysis of FTA are carried out, so , so it can be concluded that EDCU , limit switch, nut component , screw and middle unlocking component have a great damage on door disabled open fault. The obtained results can be used for fault diagnosis of the metro door system.%车门系统是地铁列车的关键子系统,应用故障树分析FTA方法对地铁车门系统进行故障诊断研究有助于缩短车门检修时间和提高车门工作可靠性。在分析地铁车门系统结构和工作原理的基础之上,选取车门无法自动开门故障作为顶事件建立故障树,采用下行法求最小割集并进行定性与定量的综合分析,从而得出了EDCU、关闭行程开关、螺母组件、丝杆和中间解锁组件对车门无法自动开门故障的影响较大的诊断结果,为地铁车门系统的故障诊断提供辅助参考决策。

  1. Gear Fault Diagnosis Based on Levenberg-Marquardt Neural Network%基于L-M神经网络的齿轮故障诊断

    Institute of Scientific and Technical Information of China (English)

    毛明明; 柳益君; 汤嘉立

    2011-01-01

    齿轮传动工况的复杂性使得其特征参量与故障形式呈非线性映射关系.提出基于Levenberg-Marquardt算法的前向多层神经网络的齿轮故障诊断方法,该方法通过利用二阶导数信息,可以提高收敛速度和增强网络的泛化性能.并以一种齿轮箱故障信号采集实验系统为例,通过MATLAB软件及其神经网络工具建模和仿真研究.结果表明,Levenberg-Marquardt神经网络对齿轮常见故障有良好的识别能力,能稳定、准确地识别各类故障,与标准BP网络相比,收敛速度快且诊断更为准确.%Because of the complexity of gear working condition, there are non-linear relationship between characteristic parameters and fault types. Proposes to apply the feed forward artificial neural network with Levenberg-Marquardt training algorithm, to the problem of gear fault diagnosis. By using second derivative information ,the network convergence speed is promoted and the generalization performance is enhanced. Taking a certain gearbox fault signal acquisition experimental system for an example, MATLAB software and its neural network toolbox are used to model and simulate. The experiment result shows that Levenberg-Marquardi neural network has good performance for the common gear fault diagnosis and it can identify various types of faults stably and accurately. Furthermore, compared with conventional BP neural network,the Levenberg-Marquardt neural network reduces training epochs and promotes prediction accuracy.

  2. 基于支持向量机的汽轮机振动故障诊断系统%Vibration Fault Diagnosis of Steam Turbine Based on SVM

    Institute of Scientific and Technical Information of China (English)

    韩中合; 刘明浩

    2013-01-01

    以MATLAB为开发平台构建了一套汽轮机振动故障诊断系统.该系统以支持向量机算法为核心,并通过建立支持向量机多分类模型对汽轮机常见故障进行了精确的诊断.%This paper develops a set of steam turbine vibration fault diagnosis system based on the development platform of MATLAB. The system uses the SVM algorithm as the core,and diagnoses the common faults of steam turbine accurately through establishing multiple classification SVM model.

  3. 基于自适应滑模观测器的航空发动机故障诊断%Fault Diagnosis of Aircraft Engines Based on Adaptive Sliding Mode Observers

    Institute of Scientific and Technical Information of China (English)

    徐清诗; 郭迎清

    2016-01-01

    Existing model-based fault-diagnosis methods of aircraft engines require high precision engine model. To deal with these shortcomings,a new approach based on adaptive sliding mode observer to de-tect,isolate,and identify faults is proposed,which has strong robustness properties and disturbance rejec-tion properties. The fault reconstruction algorithms respectively for sensor faults and actuator faults are al-so presented,whereby this two kinds of the faults can be distinguished. Moreover,design guideline of the proposed observer is analyzed,as well as the influence of critical parameters on chattering. The efficiency of the proposed fault diagnosis approach are validated with Matlab/Simulink. The simulation results indi-cate that the adaptive observers do well in both fast tracking and detection.%为解决现有航空发动机基于模型的在线故障诊断方法存在对模型精度要求高等的问题,利用滑模方法设计一种自适应滑模观测器对航空发动机进行在线故障重构、诊断与隔离。对传感器故障和执行机构故障分别设计了重构算法,针对两者重构故障的特点提出了判断逻辑,讨论了设计参数对于观测效果与抖振的影响。 Matlab/Simulink仿真结果显示,重构的故障与实际故障基本吻合,对故障的诊断、隔离、定位具有良好效果,并对环境不确定性具有优良的鲁棒性。

  4. 基于融合特权信息支持向量机的模拟电路故障诊断新方法%Novel analog circuit fault diagnosis method based on SVM of learning using privileged information

    Institute of Scientific and Technical Information of China (English)

    李涛柱; 李红波; 曾繁景; 李铁峰

    2012-01-01

    This paper proposed a novel fault diagnosis method based on SVM of learning using privileged information (LUPI-SVM),aiming at solving the problem of correctly identifying fault classes in analog circuit fault diagnosis. Firstly, the fault feature vectors were extracted by PCA (principal component analysis) feature extraction method. Then, after training the LUPI-SVM by faulty feature vectors, the LUPI-SVM model of the circuit fault diagnosis system was built. Finally, input the lest samples' feature vectors into the trained LUPI-SVM model to identify the different fault cases. The simulation results for analog and mixed-signal lest benchmark Sallen-Key filter circuits demonstrate that the proposed method improves classification ability. It correctly classifies not only the single hard fault classes with a highly average classification success rate more than 99% , but also the multiple fault classes. The method develops a new direction for the fault diagnosis of analog circuit.%针对模拟电路故障诊断复杂多样难于辨识的问题,提出了基于融合特权信息支持向量机的模拟电路故障诊断新方法.首先对采集的信号进行主成分分析( PCA)——特征提取;然后将训练集输入融合特权信息支持向量机进行训练获得故障诊断模型;最后将测试集输入训练好的支持向量机分类模型,实现对不同故障类型的识别.Sallen-Key滤波电路故障诊断仿真实验结果表明,该方法有效提高了分类的性能,不仅能够正确分类单故障而且能够有效分类多故障,其中单硬故障情况下平均故障诊断率达到了99%以上,为模拟电路故障诊断提供了新的途径.

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

    Directory of Open Access Journals (Sweden)

    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.

  6. Nuclear power plant fault-diagnosis using neural networks with error estimation

    Energy Technology Data Exchange (ETDEWEB)

    Kim, K.; Bartlett, E.B.

    1994-12-31

    The assurance of the diagnosis obtained from a nuclear power plant (NPP) fault-diagnostic advisor based on artificial neural networks (ANNs) is essential for the practical implementation of the advisor to fault detection and identification. The objectives of this study are to develop an error estimation technique (EET) for diagnosis validation and apply it to the NPP fault-diagnostic advisor. Diagnosis validation is realized by estimating error bounds on the advisor`s diagnoses. The 22 transients obtained from the Duane Arnold Energy Center (DAEC) training simulator are used for this research. The results show that the NPP fault-diagnostic advisor are effective at producing proper diagnoses on which errors are assessed for validation and verification purposes.

  7. Data-Driven Adaptive Observer for Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Shen Yin

    2012-01-01

    Full Text Available This paper presents an approach for data-driven design of fault diagnosis system. The proposed fault diagnosis scheme consists of an adaptive residual generator and a bank of isolation observers, whose parameters are directly identified from the process data without identification of complete process model. To deal with normal variations in the process, the parameters of residual generator are online updated by standard adaptive technique to achieve reliable fault detection performance. After a fault is successfully detected, the isolation scheme will be activated, in which each isolation observer serves as an indicator corresponding to occurrence of a particular type of fault in the process. The thresholds can be determined analytically or through estimating the probability density function of related variables. To illustrate the performance of proposed fault diagnosis approach, a laboratory-scale three-tank system is finally utilized. It shows that the proposed data-driven scheme is efficient to deal with applications, whose analytical process models are unavailable. Especially, for the large-scale plants, whose physical models are generally difficult to be established, the proposed approach may offer an effective alternative solution for process monitoring.

  8. 基于改进LS-SVM的异步电机转子故障诊断%Rotor Fault Diagnosis of Asynchronous Motor Based on Improved LS-SVM

    Institute of Scientific and Technical Information of China (English)

    李伟伟; 王莉; 张琳; 冯丹

    2016-01-01

    In order to improve rotor fault diagnosis accuracy of the asynchronous motor,a multi-class classification algorithm based on improved Least Square Support Vector Machine (LS-SVM)is proposed. First the fault character vectors are collected from the signal spectrum that is acquired from the signals of the motor stator current fault by FFT. Then when the feature vectors are used as the inputs of the improved algorithm for fault diagnosis,the improved algorithm confirms the weight of all faults with analytic hierarchy process,determines the order of the fault diagnosis in accordance with the weight and achieves the fault classification in turn on the basis of the former multi-class algorithm. Experimental results show that the improved algorithm saves time and improves the diagnosis accuracy when it is used for fault diagnosis and it has a bright prospect for generalization.%为了提高异步电动机转子故障的诊断精度,给出了一种基于改进最小二乘支持向量机(LS-SVM)的多故障分类算法。首先运用FFT处理电机的定子电流信号得到信号频谱图,从中提取故障特征向量;然后将特征向量送入改进算法进行故障诊断时,在原有多分类算法的基础上引入层次分析法确定故障类别的权重,根据权重值确定故障的诊断顺序,依次进行故障分类。实验表明,改进算法用于故障诊断节省了诊断时间,提高了诊断精度,具有很好的推广前景。

  9. Development of CIMS and FMS in Faults Diagnosis System

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    The research and practice of CIMS and FMS has brought about a great development to advanced manufacturing systems for decades. The experience of failure and success during the process of development is a revelation and reference for the design of a fault diagnosis system. This paper focuses on its function of directing to the design of a fault diagnosis system in terms of the flexibility of the system, the human's importance in the system, and the design of a distributed system. In view of the tendency of CIMS and FMS, the article also states the principle that the new fault diagnosis system should be improved by enhancing hardware in software, remote Internet service, and sustainable development.

  10. Hybrid fault diagnosis of nonlinear systems using neural parameter estimators.

    Science.gov (United States)

    Sobhani-Tehrani, E; Talebi, H A; Khorasani, K

    2014-02-01

    This paper presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems taking advantage of both the system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution is a bank of adaptive neural parameter estimators (NPEs) associated with a set of single-parameter fault models. The NPEs continuously estimate unknown fault parameters (FPs) that are indicators of faults in the system. Two NPE structures, series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. In contrast, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Finally, a fault tolerant observer (FTO) is designed to extend the capability of the two NPEs that originally assumes full state measurements for systems that have only partial state measurements. The proposed FTO is a neural state estimator that can estimate unmeasured states even in the presence of faults. The estimated and the measured states then comprise the inputs to the two proposed FDII schemes. Simulation results for FDII of reaction wheels of a three-axis stabilized satellite in the presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solutions under partial state measurements.

  11. Fault Diagnosis of Nonlinear Analog Circuits. Volume III. Fault Diagnosis in the Tableau Context.

    Science.gov (United States)

    1983-04-01

    of the limited fault assumption is that of Biernacki and Bandler who developed an approach to multiple fault location for linear networks. Here the...and J. W. Bandler , "Multiple-Fault Location of Analog Circuits," IEEE Trans. on Circuits and Systems, Vol. CAS-28, 361-367, May 1981. [5] R. A. DeCarlo

  12. Approach to Fire Control System Real-time Fault Diagnosis Based on Linux/QT%基于Linux/QT的火控系统实时故障诊断方法

    Institute of Scientific and Technical Information of China (English)

    刘白林; 段明晔; 肖亮; 张振华

    2011-01-01

    According to a fire control system real-time fault detection and diagnosis, we proposed a diagnosis method based on Object-Frame knowledge representation approach. In view of characteristics of fire control system, fault diagnosis knowledge framework for fire control system is established based on Object-Frame knowledge representation approach. Using the real-time detection process we got test parameters from diagnostic equipment through UDP protocol. The expert system then sends failure symptoms to the inference engine to reason and locate fault sources of equipment. A fault diagnosis expert system in the Linux/QT environment was implemented, which utilizing MySQL database for storing fault knowledge. Practical application shows that the method is effective and has good compatibility and scalability.%针对某数字化火控系统实时故障检测与诊断问题,提出了一种基于对象式框架知识表示的火控系统实时故障诊断方法.该方法采用专家系统与实时进程通信,获取故障征兆,进行实时推理定位故障源.给出了诊断系统的一般架构,介绍故障诊断系统知识表示的形式化描述,实时推理、规则匹配算法和UDP进程通信协议.实际应用证明了这种方法的有效性和可扩展性.

  13. A Novel Method of Fault Diagnosis for Rolling Bearing Based on Dual Tree Complex Wavelet Packet Transform and Improved Multiscale Permutation Entropy

    Directory of Open Access Journals (Sweden)

    Guiji Tang

    2016-01-01

    Full Text Available A novel method of fault diagnosis for rolling bearing, which combines the dual tree complex wavelet packet transform (DTCWPT, the improved multiscale permutation entropy (IMPE, and the linear local tangent space alignment (LLTSA with the extreme learning machine (ELM, is put forward in this paper. In this method, in order to effectively discover the underlying feature information, DTCWPT, which has the attractive properties as nearly shift invariance and reduced aliasing, is firstly utilized to decompose the original signal into a set of subband signals. Then, IMPE, which is designed to reduce the variability of entropy measures, is applied to characterize the properties of each obtained subband signal at different scales. Furthermore, the feature vectors are constructed by combining IMPE of each subband signal. After the feature vectors construction, LLTSA is employed to compress the high dimensional vectors of the training and the testing samples into the low dimensional vectors with better distinguishability. Finally, the ELM classifier is used to automatically accomplish the condition identification with the low dimensional feature vectors. The experimental data analysis results validate the effectiveness of the presented diagnosis method and demonstrate that this method can be applied to distinguish the different fault types and fault degrees of rolling bearings.

  14. A texture-based rolling bearing fault diagnosis scheme using adaptive optimal kernel time frequency representation and uniform local binary patterns

    Science.gov (United States)

    Chen, Haizhou; Wang, Jiaxu; Li, Junyang; Tang, Baoping

    2017-03-01

    This paper presents a new scheme for rolling bearing fault diagnosis using texture features extracted from the time-frequency representations (TFRs) of the signal. To derive the proposed texture features, firstly adaptive optimal kernel time frequency representation (AOK-TFR) is applied to extract TFRs of the signal which essentially describe the energy distribution characteristics of the signal over time and frequency domain. Since the AOK-TFR uses the signal-dependent radially Gaussian kernel that adapts over time, it can exactly track the minor variations in the signal and provide an excellent time-frequency concentration in noisy environment. Simulation experiments are furthermore performed in comparison with common time-frequency analysis methods under different noisy conditions. Secondly, the uniform local binary pattern (uLBP), which is a computationally simple and noise-resistant texture analysis method, is used to calculate the histograms from the TFRs to characterize rolling bearing fault information. Finally, the obtained histogram feature vectors are input into the multi-SVM classifier for pattern recognition. We validate the effectiveness of the proposed scheme by several experiments, and comparative results demonstrate that the new fault diagnosis technique performs better than most state-of-the-art techniques, and yet we find that the proposed algorithm possess the adaptivity and noise resistance qualities that could be very useful in real industrial applications.

  15. Fault Diagnosis Method for Rolling Bearings Based on ITD and Grey Incidence Degree%基于I TD和灰色关联度的轴承故障诊断方法

    Institute of Scientific and Technical Information of China (English)

    裴峻峰; 陈园丽; 代云聪; 黄显茹

    2014-01-01

    根据滚动轴承故障信号的非平稳特点及振动信号的强噪声背景,提出一种基于固有时间尺度分解(ITD)和灰色关联度的轴承故障诊断方法。首先利用ITD法将轴承振动信号分解为若干个固有旋转分量,进而提取故障特征参数,然后通过计算标准故障模式与待识别样本的灰色关联度对轴承故障类型进行判断。实例表明, ITD法可较好地分解轴承故障振动信号,结合灰色关联度可成功用于轴承故障在线监测与诊断,有效识别轴承工作状态。%According to the strong noise background of vibration signals and the non-stationary characteristics of fault signals for rolling bearings,a fault diagnosis method for bearings is proposed based on intrinsic time-scale decomposi-tion(ITD)and grey incidence degree.Firstly,the vibration signals for bearings is decomposed into a finite number of proper rotation components to extract fault feature parameter using the ITD method,then fault type of bearings is judged by calculating the grey incidence degree between unknown sample and standard fault pattern.The results show that ITD method can effectively decompose fault vibration signals for bearings and this method which combined with grey inci-dence degree can be successfully applied in the on-line monitoring and diagnosis of fault for bearings to identify work-ing conditions of bearings effectively.

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

    DEFF Research Database (Denmark)

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

    2013-01-01

    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...

  17. Research on the Fault Diagnosis Technology of Equipment Based on the Fuzzy Petri Net Data Fusion Organon%基于模糊Petri网融合推理的装备故障诊断技术研究

    Institute of Scientific and Technical Information of China (English)

    刘雪霞; 谭业发; 许君风

    2012-01-01

    研究了模糊Petri网应用于故障诊断中的正向推理方法,针对某型工程装备液压系统故障的特点,建立了基于模糊Petri网的故障诊断推理模型,并以液压系统压力不足故障为例,探讨了正向推理方法在系统级故障诊断中的应用.实现了故障征兆的信息融合,该推理方法适用于现场的快速故障诊断.%It introduces the forward reasoning diagnosis technology of the fault fuzzy Petri nets. Aiming at the fault symptom information fusion and fault conclusion determination, it establishes the fault diagnosis model based on forward reasoning of fuzzy Petri net, realizes the rapid and on - site diagnosis to hydraulic system of certain engineering equipment.

  18. Vibration Feature Extraction and Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey

    Directory of Open Access Journals (Sweden)

    Saleem Riaz

    2017-02-01

    Full Text Available Safety, reliability, efficiency and performance of rotating machinery in all industrial applications are the main concerns. Rotating machines are widely used in various industrial applications. Condition monitoring and fault diagnosis of rotating machinery faults are very important and often complex and labor-intensive. Feature extraction techniques play a vital role for a reliable, effective and efficient feature extraction for the diagnosis of rotating machinery. Therefore, developing effective bearing fault diagnostic method using different fault features at different steps becomes more attractive. Bearings are widely used in medical applications, food processing industries, semi-conductor industries, paper making industries and aircraft components. This paper review has demonstrated that the latest reviews applied to rotating machinery on the available a variety of vibration feature extraction. Generally literature is classified into two main groups: frequency domain, time frequency analysis. However, fault detection and diagnosis of rotating machine vibration signal processing methods to present their own limitations. In practice, most healthy ingredients faulty vibration signal from background noise and mechanical vibration signals are buried. This paper also reviews that how the advanced signal processing methods, empirical mode decomposition and interference cancellation algorithm has been investigated and developed. The condition for rotating machines based rehabilitation, prevent failures increase the availability and reduce the cost of maintenance is becoming necessary too. Rotating machine fault detection and diagnostics in developing algorithms signal processing based on a key problem is the fault feature extraction or quantification. Currently, vibration signal, fault detection and diagnosis of rotating machinery based techniques most widely used techniques. Furthermore, the researchers are widely interested to make automatic

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

    Science.gov (United States)

    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

  20. Fault diagnosis of DCT electronic control system based on neural network and evidence theory%基于神经网络和证据理论的DCT电控系统故障诊断

    Institute of Scientific and Technical Information of China (English)

    孔慧芳; 段锐; 鲍伟

    2015-01-01

    为了提高双离合器自动变速器(dual‐clutch transmission ,DCT )电控系统故障诊断精度,文章提出了一种基于神经网络和证据理论的DCT电控系统故障诊断方法。该方法首先分别用BP神经网络和RBF神经网络对DC T电控系统进行故障诊断,然后利用D‐S证据理论将两者的诊断结果进行决策融合,得出最终的诊断结果。仿真结果表明,该方法能够有效提高DC T电控系统故障诊断的精度。%In order to improve the fault diagnosis accuracy of dual‐clutch transmission(DCT ) electronic control system ,a fault diagnosis scheme based on neural network and D‐S evidence theory is devel‐oped .Both the fault diagnosis based on BP neural network and based on RBF neural network of DCT electronic control system are studied .Then the D‐S evidence theory is applied to fusing the diagnosis results of BP neural network and RBF neural network .The simulation results are presented to demon‐strate the validity and effectiveness of the fault diagnosis scheme .