Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM

Abstract

In view of problems of difficult extracting of fault feature vector and unsatisfactory multi-classification effect of shearer rolling bearing, a fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM was proposed. The bearing fault signal is denoised by wavelet and decomposed by empirical mode decomposition algorithm, then energy characteristic value is extracted and used as training set and test set of MSVM. The MSVM is used to identify fault status and parameters of MSVM are optimized by HGWO algorithm. The experimental results show that the fault diagnosis model of shearer bearing based on HGWO-MSVM can obviously improve accuracy and efficiency of fault identification compared with GWO, GA and PSO optimization MSVM model.

Keywords

• coal mining
• shearer
• rolling bearing
• fault diagnosis
• empirical mode decomposition
• hybrid grey wolf optimization algorithm
• multi-class support vector machine