Bearing Fault Diagnosis Based on Recursive Graph and Transfer Learning in Small Sample
To address the problems of different data distributions and small data size of fault vibration signals in practical engineer-ing,a transfer learning method based on convolutional neural network was proposed for rolling bearing diagnosis.The 1D time series data of rolling bearings were transformed into images by using recursive graphs,and the source domain data and target domain data were ob-tained in the 2D image domain.Then,the source domain data were input into the ResNet network with ECA attention mechanism for pre-training,and the pre-trained weights were obtained.The pre-trained weights were transferred to the model,and a small number of sam-ples were used for training.The validation accuracy was used as the criterion to obtain the training weights at this time,and they were saved to the target model.Finally,the test set data were input into the model at this time for validation.The results show that the proposed method can achieve high fault identification accuracy in the target domain with only a small number of training samples,and it has strong robustness and generalization performance.
万方数据
维普
