A novel distributed approach to robust fault detection and identification

Abstract In this paper, we propose a novel distributed robust fault detection and identification (RFDI) scheme for a class of nonlinear systems. Firstly, a detection and identification estimator—robust fault tracking approximator (RFTA) is designed for online health monitoring. A novel feature of the RFTA is that it can simultaneously detect and accurately identify the shape and magnitude of the fault and disturbance. Moreover, it takes less online training time compared with the traditional neural network based fault diagnosis schemes. For some distributed systems, a network of distributed estimators is constructed where the RFTA is embedded into each estimator. Then we use consensus filter to filter the outputs of each estimator. One of the most important merits of the consensus filter is that its outputs can dramatically improve the accuracy of fault detection and identification. Next, the stability of the distributed RFDI scheme is rigorously investigated. Finally, two numerical examples are given to illustrate the feasibility and effectiveness of the proposed approach.

[1]  Shengli Xie,et al.  A new algorithm of iterative learning control with forgetting factors , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[2]  R. Olfati-Saber,et al.  Consensus Filters for Sensor Networks and Distributed Sensor Fusion , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[3]  Marios M. Polycarpou,et al.  Learning approach to nonlinear fault diagnosis: detectability analysis , 2000, IEEE Trans. Autom. Control..

[4]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[5]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[6]  Michèle Basseville,et al.  Fault Detection and Isolation in Nonlinear Dynamic Systems: A Combined Input-Output and Local Approach , 1998, Autom..

[7]  Marios M. Polycarpou,et al.  Learning methodology for failure detection and accommodation , 1995 .

[8]  Hong Wang,et al.  Fault detection and diagnosis for general stochastic systems using B-spline expansions and nonlinear filters , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

[9]  R. V. Patel,et al.  Neural network based fault diagnosis in an HVDC system , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[10]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[11]  Arun T. Vemuri,et al.  Sensor bias fault diagnosis in a class of nonlinear systems , 2001, IEEE Trans. Autom. Control..

[12]  Weng Zheng-xin.  A New Method for Fault Diagnosis in HVDC Systems , 2007 .

[13]  Richard M. Murray,et al.  Information flow and cooperative control of vehicle formations , 2004, IEEE Transactions on Automatic Control.

[14]  Dagmar Niebur,et al.  Artificial Neural Networks for Power Systems - A Literature Survey , 1993 .

[15]  Khashayar Khorasani,et al.  Fault identification in an AC-DC transmission system using neural networks , 1991 .

[16]  Paul M. Frank,et al.  Fault Detection and Isolation in Automatic Processes , 1991 .

[17]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[18]  W. C. Merrill.  Sensor failure detection for jet engines using analytical redundancy , 1985 .

[19]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[20]  Ron J. Patton,et al.  Robust Model-Based Fault Diagnosis: The State of the ART , 1994 .

[21]  Marios M. Polycarpou,et al.  Sensor bias fault isolation in a class of nonlinear systems , 2005, IEEE Transactions on Automatic Control.

[22]  Lv Dong-mei.  Fault Diagnosis for a Class of Nonlinear Stochastic Time-delay Systems , 2007 .

[23]  Reza Olfati-Saber,et al.  Distributed Kalman filtering and sensor fusion in sensor networks , 2006 .

[24]  Khashayar Khorasani,et al.  Application of a radial basis function (RBF) neural network for fault diagnosis in a HVDC system , 1998 .

[25]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[26]  Marios M. Polycarpou,et al.  A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems , 2002, IEEE Trans. Autom. Control..

[27]  R. Pesenti,et al.  Distributed consensus protocols for coordinating buyers , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[28]  R. Olfati-Saber,et al.  Distributed Kalman Filter with Embedded Consensus Filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[29]  P. Frank,et al.  Deterministic nonlinear observer-based approaches to fault diagnosis: A survey , 1997 .

[30]  Tamio Arai,et al.  A distributed control scheme for multiple robotic vehicles to make group formations , 2001, Robotics Auton. Syst..

[31]  A. Bahill.  A simple adaptive Smith-predictor for controlling time-delay systems: A tutorial , 1983, IEEE Control Systems Magazine.

[32]  L. L. Lai,et al.  HVDC systems fault diagnosis with neural networks , 2002 .