Deep learning-based methods have shown great potential in intelligent bearing fault diagnosis. However, most existing approaches suffer from the scarcity of labeled data, which often results in insufficient robustness under complex working conditions and a general lack of interpretability. To address these challenges, we propose a physics-informed multimodal fault diagnosis framework based on few-shot learning, which integrates a 2D time-frequency image encoder and a 1D vibration signal encoder. Specifically, we embed prior knowledge of multi-resolution analysis from signal processing into the model by designing a Laplace Wavelet Convolution (LWC) module, which enhances interpretability since wavelet coefficients naturally correspond to specific frequency and temporal structures. To further balance the guidance of physical priors with the flexibility of learnable representations, we introduce a parametric multi-kernel wavelet that employs channel-wise dynamic attention to adaptively select relevant wavelet bases, thereby improving the feature expressiveness. Moreover, we develop a Mahalanobis-Prototype Joint Metric, which constructs more accurate and distribution-consistent decision boundaries under few-shot conditions. Comprehensive experiments on the Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets demonstrate the superior effectiveness, robustness, and interpretability of the proposed approach compared with state-of-the-art baselines.
Hu et al. (Thu,) studied this question.