Application of Rough Set in Fault Diagnosis of Rolling Bearing
Zhixin Chen · Machinery Design and Manufacture · 2012
The characteristic values of vibration signal of several common pitting fault of the rolling bearing are analyzed in this paper.Discretization algorithm of rough set(RS)based on entropy is used for discretization,and heuristic reduction algorithm based on attribute importance is used for attribute reduction.Then the feature vectors after attribute reduction are input support vector machine(SVM)with RBF kernel function to training,SVM model is built for fault recognition and diagnosis.Experimental results show that by applying the hybrid intelligent diagnostic of rough set combined with support vector machine(RS-SVM),RS is used as the front system of SVM to realize the pre-processing of data,while RS is utilized to reduce the attribute number of information express and the rule number of decision systems of fault diagnosis,through which the input data of SVM can be greatly reduced and the system processing speed is also well improved.Hence,good results of the failure identification of vibration signal of rolling bearing are finally obtained,which verifies the efficiency and value of rough set theory for the fault diagnosis of rolling bearing.