Fault diagnosis for wind turbine bearings based on CEEMD energy entropy and VNWOA-LSSVM

WAN Xiao-jing, SUN Wen-lei, CHEN Kun
（School of Mechanical Engineering, Xinjiang University, Urumqi 830047, China）

Abstract: Aiming at the problem of fault diagnosis of wind turbine bearing under extremely complicated working conditions, the fault diagnosis methods commonly used in the condition monitoring of wind turbine operation were studied, and the fault diagnosis method for wind turbine bearings based on CEEMD energy entropy and VNWOA-LSSVM was proposed. CEEMD method was used to reduce the interference of noise on weak fault signal, energy entropy of each IMF was extracted to construct fault feature set and serve as the input of diagnosis model. Von Neumann structure was used to overcome the problems of slow convergence and low optimization accuracy in WOA algorithm, and the VNWOA-LSSVM diagnostic model classifier was constructed to realize the accurate classification of characteristic parameters of different fault types. The results indicate that the fault diagnosis method proposed has fast computing speed, strong generalization ability and high classification accuracy, and its diagnosis result is better than WOA-LSSVM, far better than the traditional LSSVM method.
Key words: wind turbine bearings; CEEMD; energy entropy; VNWOA; LSSVM