Fault diagnosis of rolling bearing based on resonance-based sparse signal decomposition with optimal -factor
| Autor: | Yan Lu, Juan Du, Xian Tao |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Measurement + Control, Vol 52 (2019) |
| Druh dokumentu: | article |
| ISSN: | 0020-2940 00202940 |
| DOI: | 10.1177/0020294019858181 |
| Popis: | When a localized defect is induced, the vibration signal of rolling bearing always consists periodic impulse component accompanying with other components such as harmonic interference and noise. However, the incipient impulse component is often submerged under harmonic interference and background noise. To address the aforementioned issue, an improved method based on resonance-based sparse signal decomposition with optimal quality factor ( Q -factor) is proposed in this paper. In this method, the optimal Q -factor is obtained first by genetic algorithm aiming at maximizing kurtosis value of low-resonance component of vibration signal. Then, the vibration signal is decomposed based on resonance-based sparse signal decomposition with optimal Q -factor. Finally, the low-resonance component is analyzed by empirical model decomposition combination with energy operator demodulating; the fault frequency can be achieved evidently. Simulation and application examples show that the proposed method is effective on extracting periodic impulse component from multi-component mixture vibration signal. |
| Databáze: | Directory of Open Access Journals |
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