Hybrid Neural Networks and Genetic Algorithm for Fault Detection and Diagnosis of a Plant
Any plant, in fact, is combined of many items and systems undergoing processes to come out with output. If the plant is manufacturing plant, the output, then, is production. Thermal power plants are a complex and highly reliable special combination of systems to produce power. With expert knowledge,...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
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2008
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| Online Access: | http://eprints.utp.edu.my/4195/1/Firas_NPC08_paper_1_page_.pdf http://eprints.utp.edu.my/4195/ |
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| Summary: | Any plant, in fact, is combined of many items and systems undergoing processes to come out with output. If the plant is manufacturing plant, the output, then, is production. Thermal power plants are a complex and highly reliable special combination of systems to produce power. With expert knowledge, fault confirmation in the thermal power plants can be prevented by illusive and real- time signals manipulation. The recent work presents a new method of fault diagnosis based on artificial neural networks and genetic algorithms. The networking is under development to resolve this problem. The proposed fault diagnosis method combines ANNs, GAs and classical probability with an expert knowledge base. By assessing the state of the thermal power plant, the ANNs and GAs colonies undergoes a transformation that produces an individual adapted the plant conditions. Results from a preliminary case study are presented by applying the ANNs on a small chemical plant.
Keywords: Fault Detection, Thermal Power Plant, Neural Networks (ANNs), Genetic Algorithms (GAs). Intelligent Networking.
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