Abstract
In order to deal with the cross-domain distribution offset problem in mechanical fault diagnosis under different operating conditions. Domain-adaptive (DA) methods, such as domain adversarial neural networks (DANNs), maximum mean discrepancy (MMD), and correlation alignment (CORAL), have been advanced in recent years, producing notable outcomes. However, these techniques rely on the accessibility of target data, restricting their use in real-time fault diagnosis applications. To address this issue, effectively extracting fault features in the source domain and generalizing them to unseen target tasks becomes a viable strategy in machinery fault detection. A fault diagnosis domain generalization method using numerical simulation data is proposed. Firstly, the finite element model (FEM) is used to generate simulation data under certain working conditions as an auxiliary domain. Secondly, this auxiliary domain is integrated with measurement data obtained under different operating conditions to form a multi-source domain. Finally, adversarial training is conducted on the multi-source domain to learn domain-invariant features, thereby enhancing the model's generalization capability for out-of-distribution data. Experimental results on bearings and gears show that the generalization performance of the proposed method is better than that of the existing baseline methods, with the average accuracy improved by 2.83% and 8.9%, respectively.