Design and Test of Fault Prediction Model of Coal Miner Cutting Gear
The underground shearer structure is complex,the working environment is poor,prone to all kinds of faults,and the disposal is difficult.According to the gear fault diagnosis requirements of MG1000/2500-WD shearer,combined with the fault feature extraction principle of CNN(convolution neural network),Fenxi Mining Group Equipment Repair Factory designed a DCNN(deep convolution neural network)fault prediction model,and implemented the data training and model testing.The results show that the fault screening accuracy of the prediction model reached 98.17%,the accuracy of fault training value and standard value reached 99.13%.
万方数据
维普
