Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings
Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/1063645 |
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| _version_ | 1832550444101009408 |
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| author | Fan Jiang Zhencai Zhu Wei Li Bo Wu Zhe Tong Mingquan Qiu |
| author_facet | Fan Jiang Zhencai Zhu Wei Li Bo Wu Zhe Tong Mingquan Qiu |
| author_sort | Fan Jiang |
| collection | DOAJ |
| description | Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults. |
| format | Article |
| id | doaj-art-b8494a28bd3847a8a890714b4413c910 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-b8494a28bd3847a8a890714b4413c9102025-02-03T06:06:46ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/10636451063645Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of BearingsFan Jiang0Zhencai Zhu1Wei Li2Bo Wu3Zhe Tong4Mingquan Qiu5Key Laboratory of Mechanical and Electrical Equipment of Jiangsu Province, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Mechanical and Electrical Equipment of Jiangsu Province, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Mechanical and Electrical Equipment of Jiangsu Province, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Mechanical and Electrical Equipment of Jiangsu Province, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Mechanical and Electrical Equipment of Jiangsu Province, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaKey Laboratory of Mechanical and Electrical Equipment of Jiangsu Province, School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaFeature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.http://dx.doi.org/10.1155/2018/1063645 |
| spellingShingle | Fan Jiang Zhencai Zhu Wei Li Bo Wu Zhe Tong Mingquan Qiu Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings Shock and Vibration |
| title | Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings |
| title_full | Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings |
| title_fullStr | Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings |
| title_full_unstemmed | Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings |
| title_short | Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings |
| title_sort | feature extraction strategy with improved permutation entropy and its application in fault diagnosis of bearings |
| url | http://dx.doi.org/10.1155/2018/1063645 |
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