Adversarial-Causal Representation Learning Networks for Machine fault diagnosis under unseen conditions based on vibration and acoustic signals

To address the challenges of obtaining diverse data, domain generalization (DG) methods for fault diagnosis have been developed. Domain adversarial methods are currently the most popular, due to their ability to handle data from unknown domains without requiring target domain information. However, their capacity to extract domain-irrelevant features remains challenging, often resulting in accuracy below 90% in many DG scenarios. This limitation stems from their inability to fully capture global dependencies, causing feature entanglement and redundant dependencies. To address these issues, we proposed a novel intelligent fault diagnosis method called Adversarial-Causal Representation Learning Networks (ACRLN), which is based on causal learning. By spatial mask domain adversarial method, ACRLN can significantly enhance data utilization by fully capturing the global dependency that are often ignored by domain adversarial algorithms. At the same time, causal learning is integrated into the ACRLN to further accomplish feature decoupling and the reduction of redundant dependency. This is achieved through channel feature orthogonality method combined with a loss function rooted in correlation analysis. Moreover, it adeptly addresses the spill-over effect often encountered in causal learning. Finally, ACRLN achieves better results and proves its effectiveness by comparison with several state-of-the-art fault diagnosis and DG algorithms on multiple datasets.