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

Tan, Shing Chiang and Lim, Chee Peng 2004, Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant, IEEE transactions on energy conversion, vol. 19, no. 2, pp. 369-377.

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Title Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant
Author(s) Tan, Shing Chiang
Lim, Chee Peng
Journal name IEEE transactions on energy conversion
Volume number 19
Issue number 2
Start page 369
End page 377
Total pages 9
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2004-06
ISSN 0885-8969
Keyword(s) fault diagnosis
intelligent systems
knowledge-based systems
neural networks
power system monitoring
Summary 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.
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2004, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048755

Document type: Journal Article
Collection: Institute for Frontier Materials
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