S3M: Two-Stage-Based Semi-Self-Supervised Method for Intelligent Bearing Fault Diagnosis

Deep supervised learning-based fault diagnosis methods require a large amount of labeled data, which frequently contradicts the typical engineering scenario, in which numerous samples are available but only a small portion is labeled. To conduct a fault diagnosis in this case, unsupervised and semi-supervised representation learning has recently been proposed. However, most of these methods have been designed without much consideration for downstream classification tasks and, thus, are insufficiently relevant for providing sufficient targeted assistance. Therefore, this study proposes a semi-self-supervised method (S3M) that consists of two learning stages. In the self-supervised learning (SSL) stage, two pretext tasks are implemented. Using two auxiliary feature extractors and classifiers, the global and local features of the unlabeled samples can be learned by the feature extractor. In the supervised learning stage, the feature extractor parameters are frozen, and the classifier is trained with the limited number of labeled samples available. The performance of the proposed method was verified using the Case Western Reserve University (CWRU) open dataset and a self-built experimental dataset. Experiments on these two datasets demonstrated that the proposed method outperformed existing diagnosis methods for a few labeled samples.