Rolling Bearing Fault Diagnosis Based on ResNet18 and Transfer Learning
To address the problems of deep learning-based rolling bearing fault diagnosis network with too deep layers and easy degradation of the model and difficulty in collecting a large number of fault samples,a rolling bearing fault diagnosis method based on ResNet18 and transfer learning is proposed.Firstly,the wave-let transform is used to convert the original vibration signal into a two-dimensional time-frequency image,and the time-frequency information contained in the image is highlighted by image enhancement methods;second-ly,the ResNet18 pre-trained on the ImageNet dataset is used as the initial fault diagnosis model through trans-fer learning;finally,the bearing dataset is used to fine-tune all parameters of the network to generate the final fault diagnosis model.The method is validated using the bearing dataset and compared with other methods.The results show that the average accuracy of the method for diagnosing rolling bearings is as high as 98.85%with a small number of fault label samples;the image enhancement and weight fine-tuning methods can ef-fectively improve the training speed of the model and enhance the classification accuracy of the model.
rolling bearing fault diagnosistransfer learningResNet18wavelet transform
NETL
NSTL
万方数据
