Hongwei Tan

The existing domain adaptation (DA) methods have been widely applied in cross-domain fault diagnosis (CDFD) of rotating machinery, but they typically assume that different domains share the same label space and rely on aggregating data from different domains for model training. However, under real industrial scenarios, the label spaces across different domains may be inconsistent due to complex and changeable working conditions, and the data from different domains may not be directly accessed due to data privacy. Therefore, a novel dual prototype-guided universal federated domain adaptation (DPUFDA) framework for CDFD is proposed. Firstly, a dual prototype-guided distribution alignment strategy is designed, where multiple class prototypes and a centroid prototype on each source client are constructed, aiming to promote the global domain and sub-domain alignments through the dual prototype contrastive learning. Secondly, an entropy-guided confidence discrepancy maximization strategy for the target client is designed to construct precise decision boundaries between the known and unknown fault classes, where the tailored discriminative confidence discrepancy entropy is adopted to enhance the classification confidence. Thirdly, a hierarchical hybrid classifier-based unknown-class rejection strategy is designed to accurately distinguish known fault classes and effectively reject unknown fault classes for the target client. Finally, a series of experiments are conducted to validate the effectiveness of the proposed framework, and the results confirm that the framework can effectively address the domain shift and label space shift while protecting data privacy, achieving excellent CDFD performance.

In actual industrial applications, due to the data privacy protection, the source domain (SD) data are often not directly accessible, while the fault classes that do not belong to the SD often appear on the target domain (TD). To address these challenges, a novel information entropy-guided source-free open-set fault diagnosis (IESOFD) approach is proposed. Firstly, a graph convolutional network (GCN)-based source model is pretrained to extract robust features. The pre-trained source model is transferred to the TD to obtain the target model, which is fine-tuned to achieve the knowledge transfer from SD to TD. Secondly, a multi-group partition pseudo-label (MGPPL) strategy is designed. It initially partitions the TD samples into multiple groups with the pseudo-labels, selects some samples from each group as the known and unknown class sample sets respectively, and minimizes and maximizes the information entropy of the known and unknown class sample sets respectively, aiming to separate the unknown fault classes in the TD and improve the reliability of the pseudo-labels. Thirdly, a difficulty class learning (DCL) strategy is designed, which reduces the intra-class distances of the difficulty classes and uses different weights to learn each fault class by information entropy, so as to more effectively separate the known and unknown fault classes. Finally, extensive experiments are conducted to verify the effectiveness of the proposed approach, and the results confirm that the proposed approach can accurately identify the known and unknown fault classes in the TD while protecting the SD data privacy.

The effectiveness of existing domain generalization-based fault diagnosis (DGFD) methods usually relies on the assumption that the label space of the source domain (SD) is consistent with that of the unseen target domain (TD). However, in actual industrial scenarios, the unknown fault classes that do not exist in the SDs may appear in the TD, resulting in the degradation of the diagnosis accuracies of DGFD methods on the unseen TD. Therefore, a novel open-set domain generalization (OSDG) approach via meta-learning-based dual-level gradient alignment (MLDGA) for intelligent fault diagnosis (FD) is proposed. Firstly, a meta-learning optimization strategy with dual-level gradient alignment is designed to optimize the gradient update directions of the inter-domain and inter-class tasks simultaneously by gradient matching, so as to ensure that the decision boundaries are located in the optimal positions between each fault class. Secondly, an entropy-guided dynamic weighting strategy is designed to improve the discrimination ability and accuracy of the model in the multi-class fault classification tasks. Finally, a classification-clustering dual-guided open decision boundary construction strategy is designed to improve the recognition capability of unknown fault classes and the adaptability of the class decision boundaries in fault classification tasks. The experimental results confirm that the proposed approach can effectively identify both known and unknown fault classes.