Bearing fault diagnosis under unknown operating conditions using distribution robust optimization theory with distribution exploration strategy and Wasserstein constraints

Abstract In practical industrial applications, the collection of bearing fault data faces challenges such as unknown operating conditions and complex fault types. Moreover, discrepancies in feature distributions across different operating conditions hinder the effective generalization of knowledge from training domains to target domains in intelligent diagnostic models. To address these challenges, this paper proposes a novel domain generalization (DG) network for bearing fault diagnosis under unknown operating conditions. Specifically, a distributionally robust optimization (DRO) approach is integrated into the DG framework. By exploring uncertainty sets of fault features and utilizing the feature distribution of subset collections, the proposed method bridges the distribution gap between the training and unknown domains. Additionally, a distribution exploration strategy and a Wasserstein distance constraint are introduced to refine the DRO approach. These enhancements implicitly guide feature distributions toward the unknown domain by narrowing the uncertainty set’s exploration range, thereby improving model robustness. Experimental validation on the Case Western Reserve University and Huazhong University of Science and Technology bearing vibration datasets demonstrates that the proposed model outperforms several state-of-the-art fault diagnosis methods in terms of feature distribution alignment and fault type classification, achieving superior diagnostic performance under unknown operating conditions.