Research on Retarder Fault Diagnosis Based on Deep Residual Network Algorithm
In order to improve the classification accuracy of different faults of machine tools,a deep residual network is designed.Through the signal preprocessing analysis of the machine tool vibration test bench,the network results are optimized and the fault diagnosis is compared and analyzed.The results show that the training set processing can make the accuracy converge to 100%,indicating that the model does not underfit.The test accuracy reaches more than 98.2%,showing a very beneficial generalization effect.When the number of rows is smaller than the of columns,the classification accuracy of the model decreases significantly with the increase of the difference between them.When the number of rows exceeds the one of columns,the model achieves higher classification accuracy and remains relatively stable.The universality verification shows that the residual network model can also achieve 99.51%accuracy in the classification of rolling bearing signals.The CNN network shows stronger recognition performance than the shallow model ResNet.ShortCut structure has obvious advantages,which helps the network to have stronger identification capability.
machine toolresidual networkfault diagnosisvibration signal
万方数据
维普
