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

• 作者: Hongshan ZHAO Huihai LIU Lang LI Yufeng GAO
• 来源期刊: 现代电力系统与清洁能源学报(英文)
• 学科: 工学
• 关键词: Wind TURBINE Bearing Fault diagnosis Stochastic SUBSPACE identification(SSI) Multi-kernel support vector machine(MSVM)
• 年，卷（期）: 2019,（2）
• 页码范围: 350-356
• 页数: 7页
• 分类号: TM614

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.