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Entropy 2019, 21(4), 386; https://doi.org/10.3390/e21040386

Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample

1
College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
2
Taian Power Supply Company, State Grid Shandong Electric Power Co. Ltd., Taian 271000, China
3
Hangzhou Municipal Electric Power Supply Company of State Grid, Hangzhou 310009, China
4
Dezhou Power Supply Company, State Grid Shandong Electric Power Co. Ltd., Dezhou 253000, China
5
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
*
Author to whom correspondence should be addressed.
Received: 4 March 2019 / Revised: 29 March 2019 / Accepted: 8 April 2019 / Published: 10 April 2019
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
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PDF [2233 KB, uploaded 12 April 2019]
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Abstract

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. View Full-Text
Keywords: bearing fault diagnosis; empirical wavelet transform; one-class support vector machine; random forest; imbalanced training data bearing fault diagnosis; empirical wavelet transform; one-class support vector machine; random forest; imbalanced training data
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Lin, L.; Wang, B.; Qi, J.; Wang, D.; Huang, N. Bearing Fault Diagnosis Considering the Effect of Imbalance Training Sample. Entropy 2019, 21, 386.

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