Optimization and Fault Diagnosis of Machine Tool Vibration Signal Depth Residual Network
In order to improve the classification accuracy of different machine tool faults,a deep residual network is designed.lhe network results of machine tool vibration test bench signal preprocessing were optimized and the fault diagnosis was analyzed.The results show that when the number of rows is less than the number of columns,the classification accuracy of the model decreases significantly with the increase of the difference between the two.When the number of rows exceeds the number of columns,the model achieves a higher classification accuracy and maintains a relatively stable state.The CNN network shows stronger recognition performance than the shallow model LeNet.The ShortCut structure has an obvious advantage,which helps the network to have stronger identification capability.The research is helpful to improve the running efficiency of the reducer and broaden it to other fields of mechanical transmission,which has good application value.
shield cutter headperformance evaluationt-distributed random neighborhood embeddingsignal dimension reduction
NETL
NSTL
万方数据
维普
