Physics-aware dynamic spectral modeling integrated with weakly supervised few-shot learning for planetary gearbox fault diagnosis

Abstract Intelligent fault diagnosis based on deep learning has shown promising results in industrial applications, yet the requirement for large labeled datasets remains a significant limitation in real-world deployments. This paper proposes a novel physics-aware dynamic spectral modeling integrated with weakly supervised few-shot learning (PADSM-WSFL) framework for fault diagnosis in planetary gearboxes. The key innovations include: (1) integration of physics-based modeling with deep learning to enhance feature extraction, (2) a unique combination of weakly supervised and few-shot learning that effectively utilizes abundant unlabeled data while requiring only extremely limited labeled samples, and (3) a graph-based feature extraction module that captures complex fault patterns. The framework consists of three main components: a physics-aware dynamic spectral modeling approach, a graph construct module for feature extraction, and the integration of weakly supervised learning with few-shot learning models. Experimental validation on two machinery fault diagnosis datasets demonstrates that PADSM-WSFL achieves superior robustness and generalization capabilities compared to state-of-the-art methods, providing an effective solution to the critical challenge of limited labeled data in industrial fault diagnosis.