A hybrid approach of rough set theory and genetic algorithm for fault diagnosis |
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Authors: | CL Huang TS Li TK Peng |
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Affiliation: | (1) Department of Information Management, National Kaohsiung First University of Science and Technology, 2, Juoyue Rd., Nantz District, Kaohsiung, 811, Taiwan;(2) Department of Industrial Engineering and Management, Ming Hsin University of Science and Technology, Hsinchu, Taiwan |
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Abstract: | This paper proposes an integrated intelligent system that builds a fault diagnosis inference model based on the advantage
of rough set theory and genetic algorithms (GAs). Rough set theory is a novel data mining approach that deals with vagueness
and can be used to find hidden patterns in data sets. Based on this approach, minimal condition variable subsets and induction
rules are established and illustrated using an application for motherboard electromagnetic interference (EMI) test fault diagnosis.
This integrated system successfully integrated the rough set theory for handling uncertainty with a robust search engine,
GA. The result shows that the proposed method can reduce the number of conditional attributes used in motherboard EMI fault
diagnosis and maintain acceptable classification accuracy. The average diagnostic accuracy of 80% shows that this hybrid model
is a promising approach to EMI diagnostic support systems . |
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Keywords: | Data mining Data reduction Electromagnetic compatibility Electromagnetic interference Genetic algorithm Rough set theory Rule generation |
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