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Induction motor stator faults diagnosis by a current concordia pattern-based fuzzy decision system

2003; Institute of Electrical and Electronics Engineers; Volume: 18; Issue: 4 Linguagem: Inglês

10.1109/tec.2003.815832

1558-0059

F. Zidani, Mohamed Benbouzid, Demba Diallo, Mohamed-Said Naït-Saïd,

Machine Learning in Bioinformatics

This paper deals with the problem of detection and diagnosis of induction motor faults. Using the fuzzy logic strategy, a better understanding of heuristics underlying the motor faults detection and diagnosis process can be achieved. The proposed fuzzy approach is based on the stator current Concordia patterns. Induction motor stator currents are measured, recorded, and used for Concordia patterns computation under different operating conditions, particularly for different load levels. Experimental results are presented in terms of accuracy in the detection of motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis if the input data are processed in an advantageous way, which is the case of the Concordia patterns.