Intelligent Diagnosis for Rolling Bearing Fault Based on Quotient Space and Support Vector Machine
ZHANG Jin-feng1,LI Xue1,YANG Rui1,LI Ji-meng2
1. Liren College, Yanshan University, Qinhuangdao, Hebei 066004, China
2. School of electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
Abstract Aiming at the problems in rolling bearing fault diagnosis that the types of faults are various, and effective features are difficult to select, an intelligent diagnosis model is proposed based on quotient space and support vector machine (SVM). Firstly, based on the stratification idea of quotient space, the model granulates input samples into different granular layers according to different equivalence relations, then time domain and frequency domain features are reduced to obtain the sensitive feature set of each granular layer. Secondly, the sensitive feature set of each layer is inputted into SVM for fault identification. Finally, the final diagnosis result is obtained by weighted fusion of the fault recognition results of each granularity layer. The model is applied to process the bearing run-to-failure test data, and the recognition accuracy reaches 96.92%, indicating the validity and practicability of the model.
ZHANG Jin-feng,LI Xue,YANG Rui, et al. Intelligent Diagnosis for Rolling Bearing Fault Based on Quotient Space and Support Vector Machine[J]. Acta Metrologica Sinica, 2020, 41(7): 835-841.
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2020, Vol. 41 
