Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

[1]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[2]  B. Moore,et al.  ART1 and pattern clustering , 1989 .

[3]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[4]  Stephen Grossberg,et al.  ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network , 1991, [1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering.

[5]  LiMin Fu,et al.  Rule Learning by Searching on Adapted Nets , 1991, AAAI.

[6]  Stephen Grossberg,et al.  Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.

[7]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[8]  Jude Shavlik,et al.  THE EXTRACTION OF REFINED RULES FROM KNOWLEDGE BASED NEURAL NETWORKS , 1993 .

[9]  Sebastian Thrun,et al.  Extracting Provably Correct Rules from Artificial Neural Networks , 1993 .

[10]  Guy Denney,et al.  F16 jet engine trending and diagnostics with neural networks , 1993, Defense, Security, and Sensing.

[11]  Jude W. Shavlik,et al.  Using Sampling and Queries to Extract Rules from Trained Neural Networks , 1994, ICML.

[12]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[13]  Marios M. Polycarpou,et al.  Automated fault detection and accommodation: a learning systems approach , 1995, IEEE Trans. Syst. Man Cybern..

[14]  Robert F. Harrison,et al.  Evaluating a Neural Network Decision-Support Tool for the Diagnosis of Breast Cancer , 1995, AIME.

[15]  Ah-Hwee Tan,et al.  Rule Extraction: From Neural Architecture to Symbolic Representation , 1995 .

[16]  Lennart Ljung,et al.  Nonlinear black-box modeling in system identification: a unified overview , 1995, Autom..

[17]  Jonathan S. Maltz,et al.  NEURAL NETWORKS FOR PNEUMATIC ACTUATOR FAULT DETECTION , 1999 .

[18]  L. B. Jack,et al.  Diagnosis of Rolling Element Bearing Faults Using Radial Basis Function Networks , 1999 .

[19]  M. R. Dellomo.  Helicopter Gearbox Fault Detection: A Neural Network Based Approach , 1999 .

[20]  Stefan Wermter,et al.  Rule generation from neural networks for student assessment , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[21]  Noel E. Sharkey,et al.  A Multi-Net System for the Fault Diagnosis of a Diesel Engine , 2000, Neural Computing & Applications.

[22]  S. Grossberg,et al.  Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.

[23]  Stephen Grossberg,et al.  Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.