Feature Distillation-Based Uniformity Few-Shot Domain Adaptation for Cross-Domain Fault Diagnosis With Sample Shortage

Abstract

In this article, we propose a feature distillation-based uniformity few-shot domain adaptation (FUFD), for cross-domain fault diagnosis with sample shortage. To address the the few-shot problem, a uniformity prototypical contrastive network is designed to improve the data sensitivity of the model. Compared to the vanilla prototypical network, the learned prototypes contain more information about fault classes by encoding semantic structure information into the feature space while dynamically estimating the distribution concentration around each class prototype. Uniformity and correlation principles are introduced to alleviate prototype collapse: the uniformity principle ensures balanced prototype distribution, while the correlation principle enhances the diversity and distinctiveness of prototypical features. In addition, a cross-domain feature distillation-based domain adaptation module is designed to address significant domain shift. This module softens the class-specific information to capture more domain-consistent information and avoid overfitting to source working condition. Finally, experiments and ablation studies on cross-domain bearing fault diagnosis tasks with limited samples validate the effectiveness of FUFD and its individual modules in enhancing few-shot cross-domain fault diagnosis performance.