Vol.130, No.1, 2022, pp.543-558, doi:10.32604/cmes.2022.018123
Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction
  • Debiao Meng1,2,3,*, Hongtao Wang1, Shiyuan Yang1, Zhiyuan Lv1, Zhengguo Hu1, Zihao Wang1
1 School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
2 Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, 523808, China
3 Yangzhou Yangjie Electronic Technology Co., Ltd., Yangzhou, 225008, China
* Corresponding Author: Debiao Meng. Email:
(This article belongs to this Special Issue: Computer-Aided Structural Integrity and Safety Assessment)
Received 30 June 2021; Accepted 04 August 2021; Issue published 29 November 2021
In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by the kurtosis criterion. Consequently, the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained. Then the fault analysis is performed. To enhance the accuracy of fault diagnosis, based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing, four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.
Wind turbine; rolling bearing; fault diagnosis; empirical mode decomposition
Cite This Article
Meng, D., Wang, H., Yang, S., Lv, Z., Hu, Z. et al. (2022). Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction. CMES-Computer Modeling in Engineering & Sciences, 130(1), 543–558.
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