Gear fault diagnosis method based on Transformer and convolutional neural network
Aiming at complex operating environment of partial gears which leading to the lack of sample data,a method for diagnosis transfer learning gear fault based on Transformer and convolutional neural network(CNN)was present.First,Gaussian filter was employed to preprocess the original vibration signal.It was able to smooth the signal and reduce interference from noisy signals.Then,the signal as an input signal to the Transformer was transformed into a patch sequence with position information.It enhanced the Transformer's feature extraction capabilities,and it improved model's diagnostic accuracy.Besides,the Transformer output sequence was input into a one-dimensional CNN to keep extracting fault information,and a residual block was added to the model to prevent network degradation.What's more,the gear dataset collected in the laboratory and the gearbox dataset of Southeast University were divided into source and target domains,the model with source domain data was pretrained,and 100 samples of each type of gear were selected as the target domain.Finally,four sets of ten replicates were performed with different datasets as the source and target domains in order to test the accuracy of the model.The experimental results show that the accuracy of gear fault diagnosis with the method of Transformer-CNN transfer learning was more than90%.Among them,the highest fault diagnosis accuracy can reach 100%.Transformer-CNN also compares the gear fault diagnosis accuracy of other convolutional neural networks,multi-scale convolutional neural networks and two-dimensional convolutional neural networks without Transformer,with an average accuracy of 99.64%,which is higher than that of the above networks.Therefore,the transfer learning method based on Transformer-CNN is able to diagnose gear faults under small samples.
万方数据
维普
