An intelligent fault diagnosis model based on deep neural network for few-shot fault diagnosis

The most existing deep neural networks (DNN)-based methods for fault diagnosis only focus on prediction accuracy without considering the limitation of labeled sample size. In practical applications of DNN-based methods, it is time-consuming and costly to collect massive labeled samples. In this paper a task named few-shot fault diagnosis is defined as training model given small labeled samples in source domain and testing given small samples in target domain. We develop a novel intelligent fault diagnosis model for few-shot fault diagnosis which is using similarities of sample pairs to classify samples, rather than end-to-end classification. The proposed model contains modules of feature learning and metric learning. The module of feature learning has twin neural networks aiming to extract features from the sample pair. The module of metric learning is to predict similarity of the sample pair. The similarities of sample pairs combined the test sample with each labelled sample are utilized to complete the classification task. Label smoothing is utilized to further improve performance of classification. The performance of the proposed model is verified by two fault diagnosis cases which are bearing fault diagnosis cross different working conditions and cross bearing locations. The comparison studies with other models demonstrate the superiority of the proposed model.