Domain Adversarial and Causal Trend Alignment Transformer: A Domain Generalization Architecture for Planetary Gearbox Cross-working Fault Diagnosis

Domain generalization methods have attracted increasing attention in fault diagnosis within the Industrial Internet of Things (IIoT), as they aim to transfer diagnostic knowledge from source conditions to unseen target conditions. However, the inherent time-varying characteristics of planetary gearboxes further intensify personalized bias induced by varying working conditions, thereby making diagnostic model generalization more challenging. To address this challenge, a Transformer-based domain generalization architecture is developed. First, the architecture leverages the global attention mechanism of the Transformer, incorporating new patch encoding and Token designs. Second, a multi-source domains adversarial learning method is employed to extract domain invariant features that are robust to condition shifts. Finally, a causal trend alignment loss is introduced. It reduces personalized bias across different working conditions by aligning the causal trends of the same fault class. Experiments demonstrate that the proposed architecture improves average diagnostic accuracy by 5.68% under cross-speed settings and 2.46% under cross-machine settings, outperforming state-of-the-art baselines.