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

Volume

3

fYear

2004

fDate

18-20 May 2004

Firstpage

1657

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE

ISSN

1091-5281

Print_ISBN

0-7803-8248-X

Type

conf

DOI

10.1109/IMTC.2004.1351399

Filename

1351399