A transfer convolutional neural network for fault diagnosis based on ResNet 50
With the rapid development of smart manufacturing, data driven fault diagnosis has attracted increasing attentions. As one of the most popular methods applied in fault diagnosis, deep learning (DL) has achieved remarkable results. However, due to the fact that the volume of labeled samples is small in fault diagnosis, the depths of DL models for fault diagnosis are shallow compared with convolutional neural network in other areas (including ImageNet) which limits their final prediction accuracies. In this research, a new TCNN(ResNet 50) with the depth of 51 convolutional layers is proposed for fault diagnosis. By combining with transfer learning, TCNN(ResNet 50) applies ResNet 50 trained on ImageNet as feature extractor for fault diagnosis. Firstly, a signal to image method is developed to convert time domain fault signals to RGB images format as the input datatype of ResNet 50. Then, a new structure of TCNN(ResNet 50) is proposed. Finally, the proposed TCNN(ResNet 50) has been tested on three datasets, including bearing damage dataset provided by KAT datacenter, motor bearing dataset provided by Case Western Reserve University (CWRU) and self priming centrifugal pump dataset. It achieved state of the art results. The prediction accuracies of TCNN(ResNet 50) are as high as 98.95% 0.0074, 99.99% 0 and 99.20% 0, which demonstrates that TCNN(ResNet 50) outperforms other DL models and traditional methods.
Neural Computing and Applications