Abstract
A new technique for an automated detection and diagnosis of rolling bearing faults is presented. The time-domain vibration signals of rolling bearings with different fault conditions are preprocessed using Laplace-wavelet transform for features' extraction. The extracted features for wavelet transform coefficients in time and frequency domains are applied as input vectors to artificial neural networks (ANNs) for rolling bearing fault classification. The Laplace-Wavelet shape and the ANN classifier parameters are optimized using a genetic algorithm. To reduce the computation cost, decrease the size, and enhance the reliability of the ANN, only the predominant wavelet transform scales are selected for features' extraction.
Original language | English |
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Pages (from-to) | 51007 |
Number of pages | 1 |
Journal | Journal of Vibration and Acoustics |
Volume | 130 |
Issue number | 5 |
DOIs | |
Publication status | Published - 1 Oct 2008 |
Keywords
- rolling bearing faults
- Laplace-wavelet transform
- mechanical engineering