Self-supervised signal representation learning for machinery fault diagnosis under limited annotation data

Recently, convolutional neural networks (CNNs) have achieved remarkable success in machinery fault diagnosis. However, these methods usually require mass of manually labeled data, which is expensive and impractical. To this end, this paper explores the application of self-supervised learning (SS-Learning) paradigm in the field of machinery fault diagnosis, and proposes a new fault diagnosis framework based on self-supervised representation learning. This method can directly learn representative features that can be used for signal classification from unlabeled signals. In addition, it enables the network to have a deeper semantic understanding of vibration signals. In this way, the proposed method can significantly improve the performance of the diagnostic model in the case of limited labeled data. Furthermore, this paper deeply analyzes the mechanism behind the SS-Learning algorithm and the reasons for its excellent performance. The proposed SS-Learning algorithm is verified on three real fault diagnosis datasets high-speed train (HST) wheelset bearing dataset, CWRU dataset and motor bearing dataset). When there are only 50 labeled samples, the proposed SS-Learning algorithm achieves an accuracy of 85% on the motor dataset, which is 17.86% higher than the ordinary CNN. It is proven that the proposed method can provide a powerful supervision signal for feature learning of unlabeled samples and obtain quite competitive fault diagnosis performance with limited labeled samples.