A novel domain feature disentanglement method for multi-target cross-domain mechanical fault diagnosis

Existing cross-domain mechanical fault diagnosis methods primarily achieve feature alignment by directly optimizing interdomain and category distances. However, this approach can be computationally expensive in multi-target scenarios or fail due to conflicting objectives, leading to decreased diagnostic performance. To avoid these issues, this paper introduces a novel method called domain feature disentanglement. The key to the proposed method lies in computing domain features and embedding domain similarity into neural networks to assist in extracting cross-domain invariant features. Specifically, the neural network architecture designed based on information theory can disentangle key features from multiple entangled latent variables. It employs the concept of contrastive learning to extract domain-relevant information from each data point and uses the Wasserstein distance to determine the similarity relationships across all domains. By informing the neural network of domain similarity relationships, it learns how to extract cross-domain invariant features through adversarial learning Eight multi-target domain adaptation tasks were set up on two public datasets, and the proposed method achieved an average diagnostic accuracy of 96.82%, surpassing six other advanced domain adaptation methods, demonstrating its superiority.