An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis
Bearings play an important role in the industrial system. Among all kinds of diagnosis methods, empirical wavelet transform has been widely used for its characteristic of separating empirical modes from the spectrum. Although the empirical wavelet transform restrains the mode aliasing of extracting...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8657932/ |
| _version_ | 1818854491327299584 |
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| author | Yonggang Xu Kun Zhang Chaoyong Ma Zhipeng Sheng Hongchen Shen |
| author_facet | Yonggang Xu Kun Zhang Chaoyong Ma Zhipeng Sheng Hongchen Shen |
| author_sort | Yonggang Xu |
| collection | DOAJ |
| description | Bearings play an important role in the industrial system. Among all kinds of diagnosis methods, empirical wavelet transform has been widely used for its characteristic of separating empirical modes from the spectrum. Although the empirical wavelet transform restrains the mode aliasing of extracting modal functions from the time domain, it runs slowly and separates a large number of invalid components. In this paper, an adaptive and fast empirical wavelet transform method is proposed. The method extracts the first feature cluster in the Fourier transform of the spectrum to reconstruct the trend spectrum. The minimum points are regarded as the initial boundaries. The key spectral negentropy is proposed to extract the frequency band which may contain main information. This method reduces the number of invalid components and computation time by filtering components before reconstruction. The simulation signal proves that the proposed method is effective and the proposed key spectral negentropy has stronger anti-noise ability than kurtosis. The experimental signals show that the method can successfully extract the fault features of inner or outer rings of bearings, and is suitable for the diagnosis of composite faults. |
| first_indexed | 2024-12-19T07:53:33Z |
| format | Article |
| id | doaj.art-3516e453054f4b8c98a9db0b1f677aae |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:53:33Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3516e453054f4b8c98a9db0b1f677aae2022-12-21T20:30:03ZengIEEEIEEE Access2169-35362019-01-017304373045610.1109/ACCESS.2019.29026458657932An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault DiagnosisYonggang Xu0Kun Zhang1https://orcid.org/0000-0002-3513-8988Chaoyong Ma2Zhipeng Sheng3Hongchen Shen4Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, ChinaKey Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, ChinaKey Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, ChinaKey Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, ChinaKey Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing, ChinaBearings play an important role in the industrial system. Among all kinds of diagnosis methods, empirical wavelet transform has been widely used for its characteristic of separating empirical modes from the spectrum. Although the empirical wavelet transform restrains the mode aliasing of extracting modal functions from the time domain, it runs slowly and separates a large number of invalid components. In this paper, an adaptive and fast empirical wavelet transform method is proposed. The method extracts the first feature cluster in the Fourier transform of the spectrum to reconstruct the trend spectrum. The minimum points are regarded as the initial boundaries. The key spectral negentropy is proposed to extract the frequency band which may contain main information. This method reduces the number of invalid components and computation time by filtering components before reconstruction. The simulation signal proves that the proposed method is effective and the proposed key spectral negentropy has stronger anti-noise ability than kurtosis. The experimental signals show that the method can successfully extract the fault features of inner or outer rings of bearings, and is suitable for the diagnosis of composite faults.https://ieeexplore.ieee.org/document/8657932/Empirical wavelet transformspectral negentropyspectral segmentationtrend spectrumrolling bearing fault diagnosis |
| spellingShingle | Yonggang Xu Kun Zhang Chaoyong Ma Zhipeng Sheng Hongchen Shen An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis IEEE Access Empirical wavelet transform spectral negentropy spectral segmentation trend spectrum rolling bearing fault diagnosis |
| title | An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis |
| title_full | An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis |
| title_fullStr | An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis |
| title_full_unstemmed | An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis |
| title_short | An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis |
| title_sort | adaptive spectrum segmentation method to optimize empirical wavelet transform for rolling bearings fault diagnosis |
| topic | Empirical wavelet transform spectral negentropy spectral segmentation trend spectrum rolling bearing fault diagnosis |
| url | https://ieeexplore.ieee.org/document/8657932/ |
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