Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample
To improve the accuracy of the recognition of complicated mechanical faults in bearings, a large number of features containing fault information need to be extracted. In most studies regarding bearing fault diagnosis, the influence of the limitation of fault training samples has not been considered....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-04-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/4/386 |
| _version_ | 1811298437636816896 |
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| author | Lin Lin Bin Wang Jiajin Qi Da Wang Nantian Huang |
| author_facet | Lin Lin Bin Wang Jiajin Qi Da Wang Nantian Huang |
| author_sort | Lin Lin |
| collection | DOAJ |
| description | To improve the accuracy of the recognition of complicated mechanical faults in bearings, a large number of features containing fault information need to be extracted. In most studies regarding bearing fault diagnosis, the influence of the limitation of fault training samples has not been considered. Furthermore, commonly used multi-classifiers could misidentify the type or severity of faults without using normal samples as training samples. Therefore, a novel bearing fault diagnosis method based on the one-class classification concept and random forest is proposed for reducing the impact of the limitations of the fault training sample. First, the bearing vibration signals are decomposed into numerous intrinsic mode functions using empirical wavelet transform. Then, 284 features including multiple entropy are extracted from the original signal and intrinsic mode functions to construct the initial feature set. Lastly, a hybrid classifier based on one-class support vector machine trained by normal samples and a random forest trained by imbalanced fault data without some specific severities is set up to accurately identify the mechanical state and specific fault type of the bearings. The experimental results show that the proposed method can significantly improve the classification accuracy compared with traditional methods in different diagnostic target. |
| first_indexed | 2024-04-13T06:19:17Z |
| format | Article |
| id | doaj.art-af9507e3b0a4428b912b23b1c815e0e3 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T06:19:17Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-af9507e3b0a4428b912b23b1c815e0e32022-12-22T02:58:42ZengMDPI AGEntropy1099-43002019-04-0121438610.3390/e21040386e21040386Bearing Fault Diagnosis Considering the Effect of Imbalance Training SampleLin Lin0Bin Wang1Jiajin Qi2Da Wang3Nantian Huang4College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaTaian Power Supply Company, State Grid Shandong Electric Power Co. Ltd., Taian 271000, ChinaHangzhou Municipal Electric Power Supply Company of State Grid, Hangzhou 310009, ChinaDezhou Power Supply Company, State Grid Shandong Electric Power Co. Ltd., Dezhou 253000, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin 132012, ChinaTo improve the accuracy of the recognition of complicated mechanical faults in bearings, a large number of features containing fault information need to be extracted. In most studies regarding bearing fault diagnosis, the influence of the limitation of fault training samples has not been considered. Furthermore, commonly used multi-classifiers could misidentify the type or severity of faults without using normal samples as training samples. Therefore, a novel bearing fault diagnosis method based on the one-class classification concept and random forest is proposed for reducing the impact of the limitations of the fault training sample. First, the bearing vibration signals are decomposed into numerous intrinsic mode functions using empirical wavelet transform. Then, 284 features including multiple entropy are extracted from the original signal and intrinsic mode functions to construct the initial feature set. Lastly, a hybrid classifier based on one-class support vector machine trained by normal samples and a random forest trained by imbalanced fault data without some specific severities is set up to accurately identify the mechanical state and specific fault type of the bearings. The experimental results show that the proposed method can significantly improve the classification accuracy compared with traditional methods in different diagnostic target.https://www.mdpi.com/1099-4300/21/4/386bearing fault diagnosisempirical wavelet transformone-class support vector machinerandom forestimbalanced training data |
| spellingShingle | Lin Lin Bin Wang Jiajin Qi Da Wang Nantian Huang Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample Entropy bearing fault diagnosis empirical wavelet transform one-class support vector machine random forest imbalanced training data |
| title | Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample |
| title_full | Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample |
| title_fullStr | Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample |
| title_full_unstemmed | Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample |
| title_short | Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample |
| title_sort | bearing fault diagnosis considering the effect of imbalance training sample |
| topic | bearing fault diagnosis empirical wavelet transform one-class support vector machine random forest imbalanced training data |
| url | https://www.mdpi.com/1099-4300/21/4/386 |
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