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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.
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Journal
IEEE transactions on energy conversionVolume
19Issue
2Pagination
369 - 377Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
ISSN
0885-8969Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2004, IEEEUsage metrics
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