| Autor: |
Xiaoyang Zheng, Zijian Lei, Zhixia Feng, Lei Chen |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Applied Sciences, Vol 14, Iss 1, p 219 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2076-3417 |
| DOI: |
10.3390/app14010219 |
| Popis: |
Bearing failures often result from compound faults, where the characteristics of these compound faults span across multiple domains. To tackle the challenge of extracting features from compound faults, this paper proposes a novel fault detection method based on the Legendre multiwavelet transform (LMWT) combined with envelope spectrum analysis. Additionally, to address the issue of identifying suitable wavelet decomposition coefficients, this paper introduces the concept of relative energy ratio. This ratio assists in identifying the most sensitive wavelet coefficients associated with fault frequency bands. To assess the performance of the proposed method, the results obtained from the LMWT method are compared with those derived from the empirical wavelet transform (EWT) method using different datasets. Experimental findings demonstrate that the proposed method exhibits more effective frequency spectrum segmentation and superior detection performance across various experimental conditions. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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