DocumentCode :
831896
Title :
Induction motor stator faults diagnosis by a current Concordia pattern-based fuzzy decision system
Author :
Zidani, Fatiha ; Benbouzid, M.E.H. ; Diallo, Demba ; Naït-Saïd, Mohamed Saïd
Author_Institution :
Electr. Eng. Dept., Univ. of Batna, Algeria
Volume :
18
Issue :
4
fYear :
2003
Firstpage :
469
Lastpage :
475
Abstract :
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.
Keywords :
electric current measurement; fault diagnosis; fuzzy set theory; squirrel cage motors; stators; 15 A; 220 V; 380 V; 4 kW; 50 Hz; 8.6 A; delta-connected squirrel-cage induction motor; four-pole squirrel-cage induction motor; fuzzy decision system; fuzzy logic; induction motor; knowledge extraction feasibility; load levels; operating conditions; stator current Concordia patterns; stator faults diagnosis; Artificial intelligence; Current measurement; Fault detection; Fault diagnosis; Fuzzy logic; Fuzzy systems; Induction motors; Intelligent sensors; Particle measurements; Stators;
fLanguage :
English
Journal_Title :
Energy Conversion, IEEE Transactions on
Publisher :
ieee
ISSN :
0885-8969
Type :
jour
DOI :
10.1109/TEC.2003.815832
Filename :
1247771
Link To Document :
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