CFDNet: An Interpretable Causal Filtering Disentanglement Domain Generalization Network for Fault Diagnosis Under Unseen Conditions

Domain generalization-based fault diagnosis (DGFD) has emerged as a promising approach for addressing mechanical fault diagnosis under unseen working conditions. However, mainstream DGFD methods based on statistical dependencies typically model only explicit relationships between temporal data and labels. They often fail to uncover implicit causal connections across different conditions, impairing the reliability and interpretability of the diagnosis results. To address this issue, an interpretable causal filtering and disentanglement domain generalization network is proposed. Specifically, discrete wavelet prior knowledge is embedded in the model to expand the signal from the time domain to the wavelet space, a causal filter is then designed to mine and filter causal features in the wavelet domain. Furthermore, a causal clustering loss and a noncausal discrimination loss are introduced to guide the network in disentangling causal information from latent representations within the causal feature space, thereby enhancing causal disentanglement. Extensive generalization experiments conducted on four machines demonstrate that the proposed method achieves superior generalization performance and interpretability.