Mutual Information-Assisted Wavelet Function Selection for Enhanced Rolling Bearing Fault Diagnosis
This paper presents an enhanced rolling bearing fault diagnosis approach, based on optimized wavelet packet transform (WPT) assisted with quantitative wavelet function selection. Mutual information is utilized as a quantitative measure to select the most suitable wavelet function for the WPT-based v...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2015-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2015/794921 |
| Summary: | This paper presents an enhanced rolling bearing fault diagnosis approach, based on optimized wavelet packet transform (WPT) assisted with quantitative wavelet function selection. Mutual information is utilized as a quantitative measure to select the most suitable wavelet function for the WPT-based vibration analysis. Energy features from coefficients of an optimal set of orthogonal wavelet subspaces which resulted from the WPT-based vibration analysis are input to different classifiers. The fault states of the rolling bearings can then be identified. Experiment studies conducted on a rolling bearing test system have verified the effectiveness of the proposed approach for rolling bearing fault diagnosis. |
|---|---|
| ISSN: | 1070-9622 1875-9203 |