Causal prototype variational information bottleneck framework for cross-domain fault diagnosis

In practical applications of modern industry, the safe and reliable operation of rotating machinery is crucial. However, due to the domain shift problem caused by complex and variable working conditions, the generalization ability of Artificial Intelligence (AI)-based cross-domain fault diagnosis models is challenged. Traditional data-driven models rely on statistical associations and are prone to capturing non-causal spurious correlations, leading to performance degradation under various working conditions. To overcome this limitation, this paper proposes a novel Causal Prototypical Variational Information Bottleneck (CP-VIB) framework. The generation mechanism of vibration signals is modeled as a Structural Causal Model (SCM) to serve as a prior for feature decoupling, cutting off the non-causal confounding paths caused by working condition characteristics. By combining the information bottleneck principle with approximate causal intervention, working condition information is compressed while fault-related causal mutual information is retained. To implement this framework, the classification task is formulated as a Euclidean distance minimization problem between Monte Carlo sampled representations and causal prototypes. Experimental results on multiple datasets containing severe compound working condition shifts demonstrate that this AI diagnostic method can achieve robust fault diagnosis under varying working condition scenarios, possessing practical application value.