Few-Shot Learning-Based Fault Diagnosis Using Prototypical Contrastive-Based Domain Adaptation Under Variable Working Conditions

Unsupervised domain adaptation (UDA) methods have found broad application in fault diagnosis under variable working conditions. However, the performance of these models significantly deteriorates when the samples in the source and target working conditions are limited. To tackle this issue of few-shot fault diagnosis under variable working conditions, a prototypical contrastive-based domain adaptation method (PCDA) is proposed. The proposed method contains three key modules: a feature extractor, a prototypical contrastive-based few-shot classifier and a domain adaptation module. Initially, the feature extractor is used to extract features from limited samples. Subsequently, the prototypical contrastive-based few-shot classifier is used to identify the fault categories. Alongside, a corresponding prototypical contrastive loss function is designed to dynamically estimate the concentration of feature distribution around each prototype and encourage the representations learned from the same category to be closer. Simultaneously, the method is guided to generate meaningful representations in a self-supervised setting, thereby enhancing the representation ability of the model. Furthermore, the domain adaptation module is utilized to bridge the great gap between two working conditions. Ultimately, PCDA is applied to two cross-domain few-shot bearing fault diagnosis tasks. These tasks include working conditions transfer tasks and machine-type transfer tasks to verify the effectiveness of the proposed method.