Abstract

Bearings, considered crucial components in rotating machinery,
are widely used in the industry. Bearing status monitoring has become an
essential step in the deployment of preventive maintenance policy. This work
is part of the diagnosis and classification of bearing defects by vibration
analysis of signals from defective bearings using time domain and frequency
analysis and wavelet packet transformations (Wavelet Packet Transform
WPT) with Artificial Neural Networks (ANN). WPT is used for extracting
defect indicators to train the neural classifier. The main goal is the
determination of the wavelet generating the most representative indicators of
the state of the bearings for better detection and classification of defects.
Using the WPT-based neural classifier, the obtained simulation results
showed that the db6 wavelet with level 3 decomposition is best suited for
diagnosing and classifying bearing defects.

Information

Item Type:	Journal
Divisions:	» Laboratory of Electrical Engineering and Renewable Energy » Faculty of Science and Technology
ePrint ID:	1978
Date Deposited:	2019-10-30
Further Information:	Google Scholar
URI:	https://www.univ-soukahras.dz/en/publication/article/1978

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