Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study
To address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/20/8629 |
| _version_ | 1797572290683076608 |
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| author | Meijiao Mao Kaixin Zeng Zhifei Tan Zhi Zeng Zihua Hu Xiaogao Chen Changjiang Qin |
| author_facet | Meijiao Mao Kaixin Zeng Zhifei Tan Zhi Zeng Zihua Hu Xiaogao Chen Changjiang Qin |
| author_sort | Meijiao Mao |
| collection | DOAJ |
| description | To address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational mode decomposition (VMD) and K-singular value decomposition (K-SVD) algorithms to effectively denoise and enhance the collected rolling bearing signals. Initially, the VMD method is employed to separate the overall noise into intrinsic mode functions (IMFs), reducing the noise content within each IMF. To optimize the mode component, K, and the penalty factor, α, in VMD, an improved arithmetic optimization algorithm (IAOA) is employed. This ensures the selection of optimal parameters and the decomposition of the signal into a set of IMFs, forming the original dictionary. Subsequently, the signals are decomposed into multiple IMFs using VMD, and an original dictionary is constructed based on these IMFs. K-SVD is then applied to the original dictionary to further reduce the noise in each IMF, resulting in a denoised and enhanced signal. To validate the efficacy of the proposed method, rolling bearing signals collected from Case Western Reserve University (CWRU) and thrust bearing test rigs were utilized. The experimental results demonstrate the feasibility and effectiveness of the proposed approach in denoising and enhancing the rolling bearing signals. |
| first_indexed | 2024-03-10T20:54:08Z |
| format | Article |
| id | doaj.art-debca583ff5b4bdabef8c2dc49fa96c8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T20:54:08Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-debca583ff5b4bdabef8c2dc49fa96c82023-11-19T18:05:50ZengMDPI AGSensors1424-82202023-10-012320862910.3390/s23208629Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement StudyMeijiao Mao0Kaixin Zeng1Zhifei Tan2Zhi Zeng3Zihua Hu4Xiaogao Chen5Changjiang Qin6School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaSchool of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan 411105, ChinaTo address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational mode decomposition (VMD) and K-singular value decomposition (K-SVD) algorithms to effectively denoise and enhance the collected rolling bearing signals. Initially, the VMD method is employed to separate the overall noise into intrinsic mode functions (IMFs), reducing the noise content within each IMF. To optimize the mode component, K, and the penalty factor, α, in VMD, an improved arithmetic optimization algorithm (IAOA) is employed. This ensures the selection of optimal parameters and the decomposition of the signal into a set of IMFs, forming the original dictionary. Subsequently, the signals are decomposed into multiple IMFs using VMD, and an original dictionary is constructed based on these IMFs. K-SVD is then applied to the original dictionary to further reduce the noise in each IMF, resulting in a denoised and enhanced signal. To validate the efficacy of the proposed method, rolling bearing signals collected from Case Western Reserve University (CWRU) and thrust bearing test rigs were utilized. The experimental results demonstrate the feasibility and effectiveness of the proposed approach in denoising and enhancing the rolling bearing signals.https://www.mdpi.com/1424-8220/23/20/8629rolling bearingarithmetic optimization algorithmvariational mode decompositionK-singular value decomposition |
| spellingShingle | Meijiao Mao Kaixin Zeng Zhifei Tan Zhi Zeng Zihua Hu Xiaogao Chen Changjiang Qin Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study Sensors rolling bearing arithmetic optimization algorithm variational mode decomposition K-singular value decomposition |
| title | Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study |
| title_full | Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study |
| title_fullStr | Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study |
| title_full_unstemmed | Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study |
| title_short | Adaptive VMD–K-SVD-Based Rolling Bearing Fault Signal Enhancement Study |
| title_sort | adaptive vmd k svd based rolling bearing fault signal enhancement study |
| topic | rolling bearing arithmetic optimization algorithm variational mode decomposition K-singular value decomposition |
| url | https://www.mdpi.com/1424-8220/23/20/8629 |
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