Signal Processing Based Quantitative Schemes for Fault Detection/Diagnosis in Dynamic Systems

Abstract Two signal processing based techniques are presented in this paper, one for fault detection, the other for fault diagnosis. In the first scheme, a recursive least squares based adaptive lattice filter is used to monitor the performance of the system and to detect the occurrence of faults. The fault detection is carried out by performing statistical analysts on adaptive filter residuals. The advantage of this scheme is that neither a priori knowledge of the model, nor the order of the system is required for fault detection. The second part of the paper introduces a new approach for fault diagnosis which combines the linear predictive coding (both forward and backward) algorithms with the root loci representation of the variation of the physical system parameters. However, the detailed model of the fault-free system is required for this part. The distinctive feature of this approach is that the fault can be diagnosed as variations of the physical system parameters, therefore, the post-fault model of the system can be obtained easily. Such information may be of importance for fault compensation, such as during the controller reconfiguration process.

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

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

[3]  Jie Zhang,et al.  On-line process fault diagnosis using neural network techniques , 1992 .

[4]  Alan S. Willsky,et al.  F-8 DFBW sensor failure identification using analytic redundancy , 1977 .

[5]  H. V. Panossian,et al.  Real-time fault detection of the Space Shuttle Main Engine , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[6]  Paul M. Frank.  Residual evaluation for fault diagnosis based on adaptive fuzzy thresholds , 1995 .

[7]  Jin Jiang,et al.  Design of reconfigurable control systems using eigenstructure assignments , 1994 .

[8]  R. Kumaresan,et al.  Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise , 1982 .

[9]  Jin Jiang,et al.  A robust fault diagnosis scheme based on signal modal estimation , 1995 .

[10]  S. M. Batill,et al.  Structural Identification Using Order Overspecified Time-Series Models , 1992 .

[11]  F. Jabbari,et al.  Vector-channel lattice filters and identification of flexible structures , 1988 .

[12]  R. Doraiswami,et al.  Performance monitoring in expert control systems , 1989, Autom..

[13]  Ron J. Patton,et al.  Robustness in Model-Based Fault Diagnosis: The 1995 Situation , 1995 .

[14]  R. Doraiswami,et al.  Performance monitoring and fault prediction using a linear predictive coding algorithm , 1993, Autom..

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

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