Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

• Published in: Computer modeling in engineering & sciences Vol. 130; no. 1; pp. 543 - 558
• Main Authors: Meng, Debiao, Wang, Hongtao, Yang, Shiyuan, Lv, Zhiyuan, Hu, Zhengguo, Wang, Zihao
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2022
• Subjects: Algorithms; Classifiers; Critical components; Empirical analysis; Failure analysis; Failure rates; Fault diagnosis; Feature extraction; Frequency analysis; Frequency spectrum; Kurtosis; Resonant frequencies; Roller bearings; Wind power; Wind turbines
• ISSN: 1526-1506; 1526-1492; 1526-1506
• DOI: 10.32604/cmes.2022.018123

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.