Intelligent fault diagnosis (IFD) is important for rotating machinery maintenance. Unfortunately, fault diagnosis training models often degenerate if unknown domain shifts exist between different working conditions when performing IFD. To deal with this problem, more generalized features related to rolling element bearing faults should be explored so that the generalized capacity of the training model is boosted for unseen target domain data. In this paper, a new algorithm using pseudo domain labels is proposed to explore subdomain distributions within each subdomain at the domain level. The idea behind the proposed method is that the domain shifts caused by variable working conditions, like varying speeds, should also be considered since the data may show a dynamic distribution of temporal features that are not limited to spatial distributions. That is, the original domain distribution could be further divided into several latent subdomains by introducing pseudo domain labels, which enables the proposed method to learn domain specific features. Furthermore, the diversity of learned features across subdomains ensures comprehensive feature coverage during model training, while the inherent similarities between these domains enhance the capacity of the model for domain generalization. To figure out how the domain label updates, a domain-class label is initially introduced to facilitate fine-grained feature learning, enabling the model to capture as many features as possible. Then an adversarial learning strategy is employed to separate the domain and class information. Specifically, pseudo domain labels are determined using class invariant features, while class labels are distinguished using features that are invariant across multiple latent subdomains. These two steps are equivalent to a min–max game, like adversarial learning. By exploring features from the class and domain levels, the domain generalization capabilities of the model can be improved, thereby further increasing the accuracy of results. Experiments on two public bearing datasets show that the proposed method outperforms state-of-the-art methods. Additionally, by limiting the number of accessible data from known source domains, the proposed method shows the potential to maintain satisfactory domain generalization capacities when combined with few-shot learning.

Multi-source domain adaptation (MSDA) has demonstrated superior performance in intelligent fault diagnosis (IFD) compared to single-source domain adaptation (SSDA), as it can provide more comprehensive and diverse information from multiple fully-labeled source domains. However, in many real industrial scenarios, acquiring multiple fully-labeled source domains is challenging because labeling all the source domains is as expensive and laborious as labeling the target domain. Given this concern, a cross-supervised multisource prototypical network (CSMPN) is proposed for multi-source few-shot fault diagnosis. Specifically, a domain-shared and a domain-individual branch are constructed to realize shared domain alignment across all the source and target domains and individual domain alignment of source-target domain pairs, respectively. Within two branches, domain alignment is realized by the designed prototypical contrastive learning (PCL) module. In the PCL module, we propose a prototype calibration strategy to address the issue of biased prototype estimation owing to outlier samples. In addition, a two-stage pseudo-labeled sample selection mechanism is proposed to enhance the feature representation ability of two branches. At the end of the two branches, we design a cross-supervised learning (CSL) module to realize mutual and collaborative learning between the two branches, which can further improve the diagnosis performance on the target domain. Experiments on two different bearing datasets are implemented to verify the superiority of the proposed method compared with the comparison methods. Our code is available at https://github.com/YNWA-Zhang/CSMPN .