Fault Monitoring Method of Wind Turbine Main Bearing

doi:10.16450/j.cnki.issn.1004-6801.2021.02.019

	Fault Monitoring Method of Wind Turbine Main Bearing
	Zheng YQ(郑玉巧); Wei JF(魏剑峰); Zhu K(朱凯); Dong B(董博)
	2021-04-01
发表期刊	Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis
ISSN	10046801
卷号	41 期号:2 页码:341-347 and 415
摘要	Aiming to the problem that high cost and error of traditional fault monitoring method, a fault monitoring method is proposed based on the temperature model for fault monitoring of the wind turbine main bearings. The multiple linear regression model, grey model, support vector machine regression model and their combination forecasting model of main bearing temperature under normal operation are established respectively. Based on the combined prediction model, the sliding window method is introduced to analyse the change of temperature residual mean and standard deviation in the faulty condition of the main bearing. The failure of the main bearing is judged by comparing the confidence interval of the mean value or standard deviation of temperature residual with the set critical value. The results indicate that, the determination coefficient of the main bearing temperature combination forecasting model value is 0.049 3, 0.002 7 and 0.000 2 higher than that of the multivariate linear regression model, the grey prediction model and the support vector machine regression model, the combination forecasting model by introducing a sliding window method can reflect the abnormal situation of main bearing temperature in time, which provides high-precision monitoring of the main bearing fault state and formulating scientific and healthy maintenance strategy. © 2021, Editorial Department of JVMD. All right reserved.
关键词	Bearings (machine parts) Forecasting Linear regression Monitoring Sliding mode control Statistics Support vector machines Support vector regression Wind turbines Change of temperatures Combination forecasting models Determination coefficients Mean and standard deviations Multiple linear regression models Multivariate linear regression model Sliding window methods Support vector machine regressions
DOI	10.16450/j.cnki.issn.1004-6801.2021.02.019
收录类别	EI
语种	英语
出版者	Nanjing University of Aeronautics an Astronautics
EI入藏号	20212110383743
EI主题词	Predictive analytics
EI分类号	601.2 Machine Components ; 615.8 Wind Power (Before 1993, use code 611 ) ; 723 Computer Software, Data Handling and Applications ; 731.1 Control Systems ; 922.2 Mathematical Statistics
引用统计	无
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/150726
专题	兰州理工大学
作者单位	兰州理工大学机电工程学院
第一作者单位	机电工程学院
第一作者的第一单位	机电工程学院
推荐引用方式 GB/T 7714	Zheng YQ,Wei JF,Zhu K,et al. Fault Monitoring Method of Wind Turbine Main Bearing[J]. Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis,2021,41(2):341-347 and 415.
APA	郑玉巧,魏剑峰,朱凯,&董博.(2021).Fault Monitoring Method of Wind Turbine Main Bearing.Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis,41(2),341-347 and 415.
MLA	郑玉巧,et al."Fault Monitoring Method of Wind Turbine Main Bearing".Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis 41.2(2021):341-347 and 415.