Mechanical Equipment Fault Prediction Based on Multi-sensor Vibration Signals
Current research mainly focuses on fault diagnosis and remaining useful life prediction,which cannot provide specific information about the health condition and fault types of rotating machinery equipment in advance.This article combines CNN,LSTM,and Support Vector Classification(SVC)to establish a three-stage fault prediction model for rotating machinery equipment based on multiple sensor vibration signals,achieving fault classification and simultaneous identification of fault types.Experimental results show that as the number of iterations increases,the fault prediction accuracy exhibits a nonlinear increasing trend.However,when the number of iterations exceeds 600,the increase in fault prediction accuracy becomes slower.When the number of iterations is 600,the fault prediction accuracy of the three-stage fault prediction model is optimal.Moreover,the more severe the bearing fault of the mechanical equipment is,the higher the fault prediction accuracy will be.Compared with the fault diagnosis methods of the other three models,the three-stage fault prediction model based on multi-sensor vibration signals can improve the fault diagnosis rate to a certain extent and has the characteristic of identifying new faults.
万方数据
维普
