Domain-Invariant Fault Representation Learning for Rotating Machinery via Causal Excitation and Conditional Alignment

To address the problem of fault diagnosis for rotating machinery under complex operating conditions in real industrial systems, most existing domain generalization methods fail to sufficiently consider inter-class feature structures when learning domain-invariant representations. This limitation often leads to degraded diagnostic performance in cross-domain scenarios, particularly under class imbalance or significant operating condition variations. Moreover, existing feature extraction networks specifically designed for rotating machinery are often inadequate for fault diagnosis tasks under variable operating conditions. To overcome these challenges, this paper proposes a domain-invariant fault feature representation learning framework for multi-source domain generalization. Specifically, we design a mechanism-aware multi-branch feature extraction network inspired by excitation–modulation mechanisms of fault generation, which captures fault-sensitive characteristics from both time-domain and frequency-domain perspectives. In addition, a class-conditional feature alignment strategy based on ICM (Independent Causal Mechanism) mixing is introduced to enhance cross-domain consistency. Through feature structure regularization, discriminative information across categories is effectively preserved under domain shifts. Extensive experimental results demonstrate that the proposed method significantly improves diagnostic performance and generalization ability on the CWRU bearing dataset as well as the HUST bearing and gearbox datasets. Notably, when the number of source domains increases, the proposed framework exhibits superior training efficiency.