A New Transfer Learning Based on VGG-19 Network for Fault Diagnosis

Abstract

Deep learning (DL) has been widely applied in the fault diagnosis field. However, the depth of DL models in fault diagnosis is very shallow compared with benchmark convolutional neural network (CNN) models for ImageNet. But it is hard to train a very deep CNN model without the large amount well-organized datasets like ImageNet. In this research, a new transfer learning based on pre-trained VGG-19 (TranVGG-19) is proposed for fault diagnosis. Firstly, a time-domain signals to RGB images conversion method is proposed. Then, the pre-trained VGG-19 is applied as feature extractor to obtained the features of converted images. Finally, a softmax classifier is trained on the features. The proposed TranVGG-19is tested on the famous motor bearing dataset from Case Western Reserve University. The final prediction accuracy of TCNN is 99.175% and the training time of TranVGG-19is only near 200 seconds. These results outperform many DL and machining learning methods.