Title :
A review on induction motor online fault diagnosis
Author :
Zhongming, Ye ; Bin, WU
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerrson Polytech. Univ., Toronto, Ont., Canada
Abstract :
Induction motor faults including mechanical and insulation faults are reviewed. The static current signatures of mechanical faults are summarized. The various applicable feature extraction methods for induction motor fault diagnosis are introduced. Application of artificial intelligence, including artificial neural networks, fuzzy logic and expert systems are reviewed. Recent achievements on the diagnosis of inverter-fed induction motor drive systems are reviewed
Keywords :
artificial intelligence; electric machine analysis computing; electrical faults; fault diagnosis; feature extraction; fuzzy logic; induction motor drives; machine testing; machine theory; neural nets; reviews; artificial intelligence; artificial neural networks; expert systems; feature extraction methods; fuzzy logic; induction motor online fault diagnosis; insulation faults; inverter-fed induction motor drive; mechanical faults; review; static current signatures; Acoustic signal detection; Dielectrics and electrical insulation; Earth; Fault detection; Fault diagnosis; Induction machines; Induction motors; Monitoring; Stators; Vibration measurement;
Conference_Titel :
Power Electronics and Motion Control Conference, 2000. Proceedings. IPEMC 2000. The Third International
Conference_Location :
Beijing
Print_ISBN :
7-80003-464-X
DOI :
10.1109/IPEMC.2000.883050
