A New Semi-Supervised Fault Diagnosis Method via Deep CORAL and Transfer Component Analysis

Data driven method has been investigated in fault diagnosis. This kind of methods usually need of a large number of labeled data in order to obtain a good model. However, the unlabeled data dominates the data availability in the real industries, which impedes the successfully application of these methods. To remedy this situation, this research investigates a new semi-supervised fault diagnosis method, and it aims at training an adaptive model by using the small labeled data and large unlabeled data. Firstly, the fault signal is transformed to the RGB format. Then, Convolutional Neural Network (CNN) is trained to generate the fault features combining with the Correlation Alignment (CORAL) loss. Finally, the Transfer Component Analysis (TCA) is adopted to classify the fault features to improve the prediction accuracy. The proposed semi-supervised method is conducted on three bearing datasets. The experiments with different proportions of labeled data are done to validate the proposed CNN with CORAL and TCA. The comparisons between the proposed CNN model with other popular deep learning methods are also presented. The results show that CNN with CORAL and TCA is potential for fault diagnosis, and the prediction accuracies of proposed method outperform other comparisons methods.