A limited labeled data bearing fault diagnosis method based on self-supervised learning

Abstract

The key focus of this study revolves around several crucial issues concerning bearing fault diagnosis, and presents an innovative solution. Conventional labeled approaches for bearing fault diagnosis often necessitate labeled data sets, which can be time-consuming or infeasible to obtain. To address this problem, an increasing amount of research has started to explore fault diagnosis methods that utilize limited labeled data. In our study, we introduce a framework for bearing fault diagnosis that incorporates wavelet transform and self-supervised learning techniques. The framework leverages vibration signals and transforms them into time-frequency spectrograms as inputs. To extract features, we employ the Swin Transformer as an encoder. Furthermore, we present a self-supervised learning approach named MoBy to address the challenge of limited labeled samples. Encouragingly, our approach achieves a diagnostic accuracy of 96.4% by utilizing only 1% labeled samples, through a well-trained encoder and a simple linear classification layer. This demonstrates outstanding performance in utilizing limited labeled data. To validate the superiority of our proposed approach, we conducted experiments on two rolling bearing fault datasets and achieved significant results.