Transfer learning has been successfully applied in fault diagnosis to solve the difficulty in constructing network models due to the lack of labeled data in practical engineering. The current transfer learning models mainly use the adaptive method to obtain the similarity between source and target domains, but the obtained similarity is incomplete. Inspired by the domain-adversarial mechanism, a novel method called ‘distance guided domain-adversarial network’ (DGDAN) is proposed in this study. DGDAN includes two modules: domain-adversarial network and maximum mean discrepancies (MMD) guided domain adaptation. In this method, a stacked autoencoder (SAE) is used as the feature extractor of the domain-adversarial network to learn domain invariant features, and MMD is used to measure the non-parametric distance of different metric spaces to improve domain alignment. Reduction of the distance of the bottleneck layer of the feature extractor is employed to improve the feature extraction capability of the network. Experimental results show that the classification accuracy rate of DGDAN is more than 98%, and DGDAN has superior robustness and generalization ability.

Domain adaptive fault diagnosis methods of bearing have made extraordinary achievements in recent years. Among these methods, the vast majority of machine learning problems are balanced classification problems. However, imbalance problems are very common in practical applications. Undersampling and oversampling methods are often used to deal with imbalance problems. Inspired by Wasserstein generative adversarial network (WGAN), a domain adaptive guided WGAN for bearing fault diagnosis under imbalanced samples is proposed in this paper. Firstly, the unbalanced data of each bearing fault is collected, and the data and random noise are generated through WGAN to generate a balanced data set, and the processed data is compared with the real and generated bearing signals through the frequency domain feature distribution. Then, stacked autoencoders (SAE) is used to extract the fault features of the target and source domains, and L2 regularization is added in each layer of fault extractor network to prevent model overfitting. Finally, the domain adaptation guided by maximum mean discrepancy (MMD) is employed to complete the feature alignment of the target and source domains. Therefore, a novel domain adaptive method named WGAN - domain adaptive (DAWGAN) is proposed for imbalanced samples. Two experiments are applied to verify the superiority of the proposed DAWGAN method. The experimental results show that the DAWGAN method can significantly improve the diagnostic accuracy of the bearing, compared with the five comparison methods in this paper, the average increase is more than 10%, and it has better stability. Comparison results of two experiments show that the DAWGAN method can significantly improve the diagnosis accuracy of bearing and has better stability than the other methods. Meanwhile, the calculation time is also shortened by an average of 40% compared with the comparison method.

Domain adaptation (DA)-based methods for fault diagnosis (FD) of rotating machinery have achieved impressive results in recent years. Most methods hold the assumption that the source domain (SD) and target domain (TD) share the same label space, which is not always satisfied in actual situations. A more practical scenario called partial domain adaptation (PDA) needs to be given more attention, where the transferable knowledge learned from a larger SD is applied to a smaller but relevant TD. A PDA method called class-weighted alignment-based transfer network (CWATN) is proposed in this article to adapt this scenario. A novel weighting method is designed to adapt the data distributions of shared classes. Except for weighted class-level alignment, global-level feature adaptation is also considered to learn more general transferable knowledge. Moreover, a domain discrepancy learning block is plugged in the shared classifier as a residual block, which could enforce the network to learn and measure the discrepancy between SD and TD explicitly, thus improving the result of DA. Three case studies are implemented to verify the superiority of CWATN. Results demonstrate that the proposed method could obtain better diagnostic performance than the selected competitive methods in the PDA scenario.

This paper presents a comprehensive review of recent advancements in bearing health monitoring and remaining useful life (RUL) prediction. It highlights key innovations in anomaly detection, health indicator construction, degradation modeling, and RUL estimation, examining developments across statistical, machine learning, and deep learning approaches while analyzing their strengths, limitations, and application contexts. Special emphasis is placed on the role of deep learning in capturing complex degradation patterns from multi-dimensional time series data and improving predictive accuracy in dynamic industrial settings. Additionally, this review explores multi-source data fusion techniques, which enhance anomaly detection robustness by integrating information from diverse sensor modalities. By identifying critical challenges and suggesting future research directions, this study aims to advance the development of robust and adaptive prediction models for intelligent maintenance in industrial applications.