Gearbox Fault Diagnosis Method Based on NGO-VMD and Improved GoogLeNet
The current fault diagnosis methods of gear box have the problems of overfitting and poor diagnosis effect under multi-speed conditions and noise interference.To solve this problem,a northern goshawk optimization(NGO)algorithm optimized variational mode decomposition(VMD)combined an improved GoogLeNet gearbox fault diagnosis method was proposed.NGO was used to optimize VMD parameters,and the optimized VMD was used to remove noise from fault signals.The structure of the original GoogLeNet was dele-ted reasonably and improved with delayed dropout and trainable ReLU(TReLU).Finally,the denoised fault signals were converted into 2D graphs as input data of improved GoogLeNet for network training and classification,and fault diagnosis results were obtained.The ex-perimental results show that compared with other noise reduction methods,NGO-VMD method has obvious noise reduction effect and can significantly improve the accuracy of fault diagnosis.Compared with the common convolutional neural network,the improved GoogLeNet can further improve the accuracy of fault diagnosis,reaching 97.2％.
万方数据
维普
