Collaborative Completion and Alignment for Class-incomplete Multi-source Domain Adaptation in Rotating Machinery Fault Diagnosis

Abstract

In rotating machinery fault diagnosis, domain adaptation performance is frequently hindered by class-incomplete training data and distribution shifts between operating conditions–scenarios under which conventional methods tend to breakdown. To address this, we introduce a synergistic two-stage framework for multi-source domain adaptation. First, to resolve the critical absence of fault classes and enhance data diversity, the Incomplete Class Sample Completion (ICSC) framework synthesizes high-fidelity pseudo-samples for the missing classes. Subsequently, the Prototype-aware Class Conditional Adversarial Network (PCCAN) performs multi-granularity feature alignment, using global, class-conditional, and prototype-based constraints to enforce intra-class compactness and inter-class separability. The synergy between these class-completion and feature-alignment mechanisms enhances cross-domain recognition accuracy. The method’s efficacy is validated across the JNU, BJTU, and SDUST datasets. The experimental results confirm the framework’s effectiveness in handling cross-domain fault diagnosis tasks under class-incomplete conditions.