A novel hybrid data-driven domain generalization approach with dual-perspective feature fusion for intelligent fault diagnosis

Domain generalization-based fault diagnosis (DGFD) approaches do not require access to the target domain during model training, but they usually rely on numerous labeled source domain data. However, only few labeled source domain data can be obtained in actual diagnosis scenarios. Therefore, a novel hybrid data-driven domain generalization (DG) approach with dual-perspective feature fusion for intelligent fault diagnosis (FD) is proposed. Firstly, to solve the problem of scarce training samples in the source domains, the rolling bearing (RB) and the gear simulated vibration models are established to generate numerous labeled simulated vibration data, and the improved auxiliary classifier generative adversarial network (ACGAN) is used to effectively balance the simulated and real data. Secondly, a simulated and real data-driven DG network that fuses intra-domain invariant features and mutually-invariant features between domains (SRDGN-IM) is proposed, where the intra-domain invariant features are learned through distillation idea and the mutually-invariant features are learned through adversarial training, which can make the diagnosis model better learn the key generalization features from source domains to obtain more accurate diagnosis results. Finally, a series of DG experiments are conducted on the gearbox and bearing datasets, and the average FD accuracies of the proposed approach reach 87.45% and 89.10% respectively under different DG tasks. © 2024 Elsevier Ltd