FEATURE EXTRACTION ENHANCEMENT BASED ON PARAMETERLESS EMPIRICAL WAVELET TRANSFORM: APPLICATION TO BEARING FAULT DIAGNOSIS | ||||
| The International Conference on Applied Mechanics and Mechanical Engineering | ||||
| Article 15, Volume 18, 18th International Conference on Applied Mechanics and Mechanical Engineering., April 2018, Page 1-19 PDF (1.7 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/amme.2018.34731 | ||||
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| Author | ||||
| H. H. El-Mongy | ||||
| Assistant professor, Dept. of Mechanical Design, Faculty of Engineering-Mataria, Helwan University, Cairo, Egypt. | ||||
| Abstract | ||||
| ABSTRACT Rolling-element bearings are usually subject to faults that need prompt detection in order to prevent sudden failures. Many time-frequency analysis techniques have been used for the purpose of bearing fault detection and diagnosis. From these techniques, wavelets and empirical mode decomposition (EMD) stand out as the most widely applied methods in bearing fault diagnosis. Recently, a novel method named the parameterless empirical wavelet transform (PEWT) has been proposed to combine the wavelet formulation with the adaptability of the empirical mode decomposition. In this paper, the parameterless empirical wavelet transform (PEWT) is combined with envelope detection (ED) to present a new scheme named PEWT-ED for non-stationary signal analysis. The capabilities and limitations of the new method in bearing fault diagnosis are investigated using simulation and experiment. The results show that the new approach can effectively extract the bearing fault characteristics. The PEWT-ED is found to be a powerful tool in signal de-noising and enhancement for fault diagnosis purposes. | ||||
| Keywords | ||||
| Rotating machinery; fault diagnosis; empirical wavelet transform; signal processing | ||||
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