A Novel Comprehensive Semi-Supervised Learning Method for Fault Diagnosis Under Extremely Low Label Rate

Intelligent fault diagnosis methods based on deep supervised learning require massive labeled data, which contradicts the typical engineering scenarios. Industrial datasets usually have low label rates, because labeling large amounts of data requires a lot of labor costs and expert knowledge. To address the challenge in fault diagnosis under extremely low label rate, this article proposes a novel semi-supervised learning framework based on multiscale and multilevel contrastive learning (MMCL), where representation learning on massive unlabeled data is integrated with joint time-frequency (JTF) domain mechanism on few labeled data to provide targeted assistance for fault diagnosis. Specifically, we utilize the MMCL method to pretrain the encoder, which combines the correlation between signals and time dependency within signals to enable robust and intrinsic representation for each timestamp of signals. Furthermore, based on the pretrained encoder, we design a JTF domain mechanism to enhance the generalization of representation from both time domain and frequency domain, which further enhance the overall diagnostic performance. The performance of the proposed method was verified using the Case Western Reserve University (CWRU) open dataset, PHM dataset, and a self-built experimental dataset. Extensive experimental results on these two datasets demonstrated that the proposed method outperformed existing semi-supervised diagnosis methods under an extremely low label rate.