A neuro-fuzzy technique for fault diagnosis and its application to rotating machinery

Malfunctions in machinery are often sources of reduced productivity and increased maintenance costs in various industrial applications. For this reason, machine condition monitoring is being pursued to recognise incipient faults. In this paper, the fault diagnostic problem is tackled within a neuro-fuzzy approach to pattern classification. Besides the primary purpose of a high rate of correct classification, the proposed neuro-fuzzy approach also aims at obtaining an easily interpretable classification model. The efficiency of the approach is verified with respect to a literature problem and then applied to a case of motor bearing fault classification.

[1]  Meng Joo Er,et al.  FAULT DIAGNOSIS IN AIR-HANDLING UNIT SYSTEM USING DYNAMIC FUZZY NEURAL NETWORK , 2004 .

[2]  Károly Sólyomvári,et al.  Vibration Testing of Ma-chines and Their Maintenance , 1990 .

[3]  Bo Li,et al.  Motor bearing fault diagnosis by a fundamental frequency amplitude based fuzzy decision system , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[4]  James L. McClelland.  Parallel Distributed Processing , 2005 .

[5]  T. Ross.  Fuzzy Logic with Engineering Applications , 1994 .

[6]  José Valente de Oliveira,et al.  Semantic constraints for membership function optimization , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[7]  Yang Xu,et al.  A KIND OF FUZZY LEAST SQUARES SUPPORT VECTOR MACHINES FOR PATTERN CLASSIFICATION , 2004 .

[8]  Eugene I. Rivin,et al.  Vibration diagnostics in precision instruments , 1989 .

[9]  Giovanna Castellano,et al.  A fuzzy clustering approach for mining diagnostic rules , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[10]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[11]  Rudolf Kruse,et al.  A neuro-fuzzy method to learn fuzzy classification rules from data , 1997, Fuzzy Sets Syst..

[12]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[13]  Mimoun Zelmat,et al.  DESIGN OF A FUZZY MODEL-BASED CONTROLLER FOR A DRUM BOILER-TURBINE SYSTEM , 2004 .

[14]  K. B. Ginn,et al.  A new technique for monitoring condenser microphones , 1996 .

[15]  Enrico Zio,et al.  Evolutionary fuzzy clustering for the Classification of transients in nuclear components , 2005 .

[16]  C. S. George Lee,et al.  Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems , 1994, IEEE Trans. Fuzzy Syst..

[17]  Enrico Zio,et al.  A fuzzy modeling approach to the identification of transients in nuclear components , 2004 .

[18]  Berndt Müller,et al.  Neural networks: an introduction , 1990 .

[19]  David I Blockley,et al.  Vulnerability of structural systems , 2003 .

[20]  Xia Hong,et al.  Adaptive Modelling, Estimation and Fusion from Data , 2002, Advanced Information Processing.

[21]  Miin-Shen Yang.  A survey of fuzzy clustering , 1993 .

[22]  H. V. Jamieson,et al.  Hydraulic noise in theme parks , 1996 .

[23]  Eugene I. Rivin,et al.  Vibration Of Bearings , 1989 .

[24]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[25]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[26]  Hsi-Wen Nein,et al.  Techniques and Applications of Integrated Neural Network-Based Fuzzy Logic Control Systems , 1999 .

[27]  Jacek M. Leski,et al.  Fuzzy and Neuro-Fuzzy Intelligent Systems , 2000, Studies in Fuzziness and Soft Computing.

[28]  Chin-Teng Lin,et al.  Neural-Network-Based Fuzzy Logic Control and Decision System , 1991, IEEE Trans. Computers.

[29]  Won Y. Lee,et al.  Classification Techniques for Fault Detection and Diagnosis of an Air-Handling Unit | NIST , 1999 .

[30]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[31]  M. Chow,et al.  An adaptive backpropagation through time training algorithm for a neutral controller , 1991, Proceedings of the 1991 IEEE International Symposium on Intelligent Control.

[32]  Enrico Zio,et al.  Model identification by neuro-fuzzy techniques: Predicting the water level in a steam generator of a PWR , 2004 .