Causality-inspired multi-source domain generalization method for intelligent fault diagnosis under unknown operating conditions

Currently, fault diagnosis methods based on domain generalization have received widespread attention due to their advantages of not requiring target domain data. Therefore, in this paper, A multi-source domain generalization fault diagnosis method is proposed, consisting of feature diversity activation and non-causal feature suppression from a causal perspective. In the first part, a 3D-Dynamic convolution-based residual network is designed to adaptively learn task related features from different source domains, encouraging the model to focus more on causal feature learning. Furthermore, based on the maximum entropy idea, channel attention diversification is proposed to activate more potential causal features. In the second part, a feature suppression method based on domain discriminator guidance is proposed to explicitly discard non-causal features, specifically, the domain discriminator progressively locates and distinguishes between causal and non-causal features at the layer and channel level and creates binary mask matrices to suppress non-causal related features. Experiments are conducted on the PU and SDUST bearing datasets, and the proposed method can productively solve the cross-domain diagnosis problem under unknown operating conditions.