Fault diagnosis method for rolling bearing of shearer based on HGWO-MSVM
SUN Mingbo
MA Qiuli
ZHANG Yanliang
LEI Junhui
Abstract：In view of problems of difficult extracting of fault feature vector and unsatisfactory multiclassification 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 miningshearerrolling bearingfault diagnosisempirical mode decompositionhybrid grey wolf optimization algorithmmulti-class support vector machine
Publication Date：2018-03-02
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 81-86 )