Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection
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 (L...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/14/1/219 |
| _version_ | 1797359172150362112 |
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| author | Xiaoyang Zheng Zijian Lei Zhixia Feng Lei Chen |
| author_facet | Xiaoyang Zheng Zijian Lei Zhixia Feng Lei Chen |
| author_sort | Xiaoyang Zheng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T15:11:55Z |
| format | Article |
| id | doaj.art-df2c122de7164b2db542dfefb523c3cb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T15:11:55Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-df2c122de7164b2db542dfefb523c3cb2024-01-10T14:51:23ZengMDPI AGApplied Sciences2076-34172023-12-0114121910.3390/app14010219Legendre Multiwavelet Transform and Its Application in Bearing Fault DetectionXiaoyang Zheng0Zijian Lei1Zhixia Feng2Lei Chen3School of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, ChinaSchool of Artificial Intelligence, Chongqing University of Technology, Chongqing 400054, ChinaBearing 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.https://www.mdpi.com/2076-3417/14/1/219rolling bearingsfault diagnosisLegendre multiwavelet decompositionenvelope spectrum analysisspectral segmentation |
| spellingShingle | Xiaoyang Zheng Zijian Lei Zhixia Feng Lei Chen Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection Applied Sciences rolling bearings fault diagnosis Legendre multiwavelet decomposition envelope spectrum analysis spectral segmentation |
| title | Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection |
| title_full | Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection |
| title_fullStr | Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection |
| title_full_unstemmed | Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection |
| title_short | Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection |
| title_sort | legendre multiwavelet transform and its application in bearing fault detection |
| topic | rolling bearings fault diagnosis Legendre multiwavelet decomposition envelope spectrum analysis spectral segmentation |
| url | https://www.mdpi.com/2076-3417/14/1/219 |
| work_keys_str_mv | AT xiaoyangzheng legendremultiwavelettransformanditsapplicationinbearingfaultdetection AT zijianlei legendremultiwavelettransformanditsapplicationinbearingfaultdetection AT zhixiafeng legendremultiwavelettransformanditsapplicationinbearingfaultdetection AT leichen legendremultiwavelettransformanditsapplicationinbearingfaultdetection |