Fault diagnosis of wind turbine bearing based on stochastic subspace identification and multi-kernel support vector machine

作     者：Hongshan ZHAO Yufeng GAO Huihai LIU Lang LI 

作者机构：School of Electrical and Electronic Engineering North China Electric Power University 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2019年第7卷第2期

页      面：350-356页

核心收录：

学科分类：08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：supported by National Key Technology Research and Development Program (No. 2015BAA06B03) 

主　　题：Wind turbine Bearing Fault diagnosis Stochastic subspace identification(SSI) Multi-kernel support vector machine(MSVM) 

摘      要：In order to accurately identify a bearing fault on a wind turbine, a novel fault diagnosis method based on stochastic subspace identification(SSI) and multi-kernel support vector machine(MSVM) is proposed. Firstly, the collected vibration signal of the wind turbine bearing is processed by the SSI method to extract fault feature vectors. Then, the MSVM is constructed based on Gauss kernel support vector machine(SVM) and polynomial kernel SVM. Finally, fault feature vectors which indicate the condition of the wind turbine bearing are inputted to the MSVM for fault pattern recognition. The results indicate that the SSI-MSVM method is effective in fault diagnosis for a wind turbine bearing and can successfully identify fault types of bearing and achieve higher diagnostic accuracy than that of K-means clustering, fuzzy means clustering and traditional SVM.