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http://dx.doi.org/10.5391/JKIIS.2008.18.3.367

State Transition Fault Diagnosis in Brushless DC Motor Based on Fuzzy System  

Baek, Gyeong-Dong (부산대학교 전기공학과)
Kim, Youn-Tae (부산대학교 전기공학과)
Kim, Sung-Shin (부산대학교 전기공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.18, no.3, 2008 , pp. 367-372 More about this Journal
Abstract
In this paper we proposed a model of a fault diagnosis expert system with high reliability to compare identical well-functioning motors. The purpose of the survey was to determine if any differences exit among these identical motors and to identify exactly what these differences were, if in fact they were found. Using measured data for many identical brushless dc motors, this study attempted to find out whether normal and fault can be classified by each other. Measured data was analyzed using the State Transition Model (STM). Based on a proposed STM method, the effect of a different normal state is minimized and the detection of fault is improved in identical motor system. Experimental results are presented to prove that STM method could be a useful tool for diagnosing the condition of identical BLDE motors.
Keywords
Fault Diagnosis; BLDC; State Transition Model;
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