A new fault diagnosis method of rolling bearing of shearer
GONG Maofa
GUO Yixuan
YAN Peng
WU Na
ZHANG Chao
Abstract：In view of unstable problem existed in fault diagnosis result for rolling bearing of shearer based on K-means clustering algorithm,a new fault diagnosis method of rolling bearing of shearer based on TDKM-RBF neural network was proposed.The method adopts Tree Distribution algorithm to determine initial clustering center of the K-means clustering algorithm,so as to eliminate volatility of K-means clustering results.The method uses K-means algorithm to determine the parameters of the RBF neural network,then the trained neural network was used for fault diagnosis.The simulation results show that the method has quick clustering process,higher steability,and obviously improves accuracy of fault diagnosis for rolling bearing of shearer.
Keywords：shearerrolling bearingfault diagnosisvolatilityRBFK-means clustering algorithmTD algorithm
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 50-53 )