Fuzzy-Expert Diagnosis Technology in Detecting and Locating the Internal Faults of the Three Phase Induction Motors

Ming Chui Dong, Tak Son Cheang, Boomadevi Sekar, Si Long Chan

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Internal faults in three phase induction motors can result in serious performance degradation and eventual system failures if not properly detected and treated in time. Artificial intelligence techniques, the core of soft-computing, have numerous advantages over conventional fault diagnostic approaches; therefore, a soft-computing system was developed to detect and diagnose electric motor faults. The fault diagnostic system for three-phase induction motors samples the fault symptoms and then uses a fuzzy-expert forward inference model to identify the fault. This paper describes how to define the membership functions and fuzzy sets based on the fault symptoms and how to construct the hierarchical fuzzy inference nets with the propagation of probabilities concerning the uncertainty of faults. The designed hierarchical fuzzy inference nets efficiently detect and diagnose the fault type and exact location in a three phase induction motor. The validity and effectiveness of this approach is clearly shown from obtained testing results.
Original languageEnglish
Pages (from-to)817-822
Number of pages6
JournalTsinghua Science and Technology
Volume13
Issue number6
DOIs
Publication statusPublished - Dec 2008

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