DocumentCode :
2905367
Title :
Experimental validation on stator fault detection via fuzzy logic
Author :
Azgomi, Hamid Fekri ; Poshtan, Javad ; Poshtan, M.
Author_Institution :
Dept. of Electr. Eng., Iran Univ. of Sci. & Technol. (IUST), Tehran, Iran
fYear :
2013
fDate :
2-4 Oct. 2013
Firstpage :
1
Lastpage :
6
Abstract :
The detection of faults in induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose induction motor faults. This work presents a reliable method for the detection of stator winding faults based on monitoring the line current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. Therefore, this paper applies fuzzy logic to induction motors fault detection and diagnosis. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. The induction motor condition is diagnosed using a compositional rule of fuzzy inference. Experimental results are presented in terms of accuracy in the detection motor faults and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.
Keywords :
electric machine analysis computing; fault diagnosis; fuzzy logic; fuzzy reasoning; fuzzy set theory; induction motors; knowledge acquisition; stators; compositional rule; data bases; fault diagnosis; fuzzy inference; fuzzy logic approach; fuzzy subsets; human thinking processes; induction motor condition; knowledge base; knowledge extraction feasibility; line current amplitudes; linguistic variables; membership functions; motor condition; natural language; rule bases; stator current amplitudes; stator winding fault detection; Communities; Electronic mail; Equations; Monitoring; Silicon compounds; Experimental Data; Fault Detection; Fuzzy Logic; Induction Motor; Stator;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Power and Energy Conversion Systems (EPECS), 2013 3rd International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4799-0687-1
Type :
conf
DOI :
10.1109/EPECS.2013.6713039
Filename :
6713039
Link To Document :
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