Hierarchical motor diagnosis utilizing structural knowledge and a self-learning neuro-fuzzy scheme

Fault diagnosis requires a classification system that can distinguish between different faults based on observed symptoms of the process under investigation. Since the fault symptom relationships are not always known beforehand, a system is needed which can be learned from experimental or simulated data. A fuzzy logic based diagnosis is advantageous. It allows an easy incorporation of a-priori known rules and also enables the user to understand the inference of the system. In this contribution, a new diagnosis scheme is presented and applied to a DC motor. The approach is based on a combination of structural a-priori knowledge and measured data in order to create a hierarchical diagnosis system that can be adapted to different motors. Advantages of the system are its high degree of transparency and an increased robustness.

[1]  J. A. Leonard,et al.  Radial basis function networks for classifying process faults , 1991, IEEE Control Systems.

[2]  Rolf Isermann,et al.  Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper , 1991, Autom..

[3]  Rolf Isermann.  FAULT DIAGNOSIS OF MACHINES VIA PARAMETER ESTIMATION AND KNOWLEDGE PROCESSING , 1992 .

[4]  Jie Chen,et al.  Review of parity space approaches to fault diagnosis for aerospace systems , 1994 .

[5]  A. W. Deshpande.  Fuzzy Fault Tree Analysis : A Case Study , 1992 .

[6]  Đani Juričić,et al.  Generation of diagnostic trees by means of simplified process models and machine learning , 1997 .

[7]  Rolf Isermann,et al.  Closed Loop Fault Diagnosis Based on a Nonlinear Process Model and Automatic Fuzzy Rule Generation , 1997 .

[8]  A. J. Morris,et al.  Process fault diagnosis using fuzzy neural networks , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[9]  Janos J. Gertler,et al.  Analytical Redundancy Methods in Fault Detection and Isolation , 1991 .

[10]  Rolf Isermann,et al.  Fault detection and diagnosis with neuro-fuzzy-systems , 1996 .

[11]  G. Mirchandani,et al.  On hidden nodes for neural nets , 1989 .

[12]  Paul M. Frank,et al.  FDI with computer-assisted human intelligence , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[13]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[14]  Thomas G. Habetler,et al.  An unsupervised, on-line system for induction motor fault detection using stator current monitoring , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[15]  Mo-Yuen Chow,et al.  A hybrid fuzzy/neural system used to extract heuristic knowledge from a fault detection problem , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[16]  Jie Chen,et al.  A REVIEW OF PARITY SPACE APPROACHES TO FAULT DIAGNOSIS , 1992 .

[17]  Timo Sorsa,et al.  APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN PROCESS FAULT DIAGNOSIS , 1992 .

[18]  Dominik Füssel.  Self-learning classification tree (SELECT) - a human-like approach to fault diagnosis , 1997 .

[19]  Mo-Yuen Chow,et al.  Set theoretic based neural-fuzzy motor fault detector , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[20]  Rolf Isermann,et al.  Adaptive Parity Equations and Advanced Parameter Estimation for Fault Detection and Diagnosis , 1996 .

[21]  Heikki N. Koivo,et al.  Application of artificial neural networks in process fault diagnosis , 1991, Autom..