Title :
Neural network based motor bearing fault detection
Author :
Eren, Levent ; Karahoca, Adem ; Devaney, Michael J.
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Bahcesehir, Turkey
Abstract :
Bearing faults are the biggest single cause of motor failures. The bearing defects induce vibration resulting in the modulation of the stator current. The stator current can be analyzed via wavelet packet decomposition to detect bearing defects. This method enables the analysis of frequency bands that can accommodate the rotational speed dependence of the bearing defect frequencies. In this study, radial basis function neural networks are used to improve bearing fault detection procedure.
Keywords :
condition monitoring; curve fitting; electric machine analysis computing; fault diagnosis; learning (artificial intelligence); machine bearings; machine testing; preventive maintenance; radial basis function networks; wavelet transforms; ball defect frequency; bearing defect frequencies; curve-fitting; motor bearing fault detection; motor current signature analysis; motor failures; neural network based detection; neurocomputing approach; preventive maintenance; race defect frequency; radial basis function neural networks; rolling element bearing; rotational speed dependence; stator current modulation; supervised training; wavelet transform; Auditory system; Discrete wavelet transforms; Electrical fault detection; Equations; Fault detection; Frequency; Induction motors; Neural networks; Production; Wavelet packets;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE
Print_ISBN :
0-7803-8248-X
DOI :
10.1109/IMTC.2004.1351399
