Domain augmentation generalization network for real-time fault diagnosis under unseen working conditions

Recent years have witnessed the successful development of domain adaptation methods to tackle cross-domain fault diagnosis problems. However, these methods require the target domain with a prior data distribution. It limits their application in real-time diagnosis scenarios, where unseen working conditions are often encountered. In this case, the domain adaptation approach is not applicable because the target data are usually unavailable in advance. With this in mind, this paper proposes a domain generalization-based method for intelligent fault diagnosis under unseen working conditions. The core idea is to explore diverse domain-invariant representations while strengthening the feature space's discriminative structure, making the model sufficiently robust to out-of-distribution data and thus generalize well to unseen domains. Specifically, multisource augmentation is developed and combined with adversarial training to boost feature diversity and learn correlations among multiple domains, thereby enhancing the robustness and generalization of feature representations. The sample adaptive screening and weighting strategy is further deployed to dynamically optimize data augmentation and network training to obtain a more discriminative decision boundary. Experimental results of extensive diagnosis tasks built on rolling bearing and gearbox datasets validate the effectiveness and superiority of the proposed method in generalization performance improvement.