Application of Variational Mode Decomposition and Multiscale Permutation Entropy in Rolling Bearing Failure Analysis
The rolling bearing fault test signal has nonstationary and nonlinear characteristics. The feature extraction method based on variational mode decomposition (VMD) and permutation entropy can effectively measure the regularity of the signal and detect weak changes. Since the center frequency of the i...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2022/7294795 |
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| _version_ | 1832548625635344384 |
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| author | Haorui Liu Haijun Li Rongyan Wang Hengwei Zhu Jianchen Zhang |
| author_facet | Haorui Liu Haijun Li Rongyan Wang Hengwei Zhu Jianchen Zhang |
| author_sort | Haorui Liu |
| collection | DOAJ |
| description | The rolling bearing fault test signal has nonstationary and nonlinear characteristics. The feature extraction method based on variational mode decomposition (VMD) and permutation entropy can effectively measure the regularity of the signal and detect weak changes. Since the center frequency of the intrinsic mode function (IMF) of each fault test signal contains more details, this paper further extracts the multiscale permutation entropy feature for each IMF. The training samples and test samples of each IMF are constructed, and then the support vector machine (SVM) and the K-nearest neighbor algorithm (KNN) are used to identify the faults. The test results of the IMF components are used to determine the classification results combined with the maximum attribution index. Compared with the relevant feature extraction, the experimental results show that the method achieves a certain improvement in the accuracy of fault identification. The research results of rolling bearing fault data show that the multiscale permutation entropy and SVM/KNN can more accurately diagnose different fault modes, different fault sizes, and different operating states of rolling bearings. |
| format | Article |
| id | doaj-art-a7eef2510a774299aa57630fd0e3d681 |
| institution | Kabale University |
| issn | 1875-9203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-a7eef2510a774299aa57630fd0e3d6812025-02-03T06:13:33ZengWileyShock and Vibration1875-92032022-01-01202210.1155/2022/7294795Application of Variational Mode Decomposition and Multiscale Permutation Entropy in Rolling Bearing Failure AnalysisHaorui Liu0Haijun Li1Rongyan Wang2Hengwei Zhu3Jianchen Zhang4College of Computer and InformationCollege of Computer and InformationCollege of Computer and InformationCollege of Computer and InformationCollege of Computer and InformationThe rolling bearing fault test signal has nonstationary and nonlinear characteristics. The feature extraction method based on variational mode decomposition (VMD) and permutation entropy can effectively measure the regularity of the signal and detect weak changes. Since the center frequency of the intrinsic mode function (IMF) of each fault test signal contains more details, this paper further extracts the multiscale permutation entropy feature for each IMF. The training samples and test samples of each IMF are constructed, and then the support vector machine (SVM) and the K-nearest neighbor algorithm (KNN) are used to identify the faults. The test results of the IMF components are used to determine the classification results combined with the maximum attribution index. Compared with the relevant feature extraction, the experimental results show that the method achieves a certain improvement in the accuracy of fault identification. The research results of rolling bearing fault data show that the multiscale permutation entropy and SVM/KNN can more accurately diagnose different fault modes, different fault sizes, and different operating states of rolling bearings.http://dx.doi.org/10.1155/2022/7294795 |
| spellingShingle | Haorui Liu Haijun Li Rongyan Wang Hengwei Zhu Jianchen Zhang Application of Variational Mode Decomposition and Multiscale Permutation Entropy in Rolling Bearing Failure Analysis Shock and Vibration |
| title | Application of Variational Mode Decomposition and Multiscale Permutation Entropy in Rolling Bearing Failure Analysis |
| title_full | Application of Variational Mode Decomposition and Multiscale Permutation Entropy in Rolling Bearing Failure Analysis |
| title_fullStr | Application of Variational Mode Decomposition and Multiscale Permutation Entropy in Rolling Bearing Failure Analysis |
| title_full_unstemmed | Application of Variational Mode Decomposition and Multiscale Permutation Entropy in Rolling Bearing Failure Analysis |
| title_short | Application of Variational Mode Decomposition and Multiscale Permutation Entropy in Rolling Bearing Failure Analysis |
| title_sort | application of variational mode decomposition and multiscale permutation entropy in rolling bearing failure analysis |
| url | http://dx.doi.org/10.1155/2022/7294795 |
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