Lili Deng

Domain adaptation methods have demonstrated considerable potential in enhancing the performance of fault diagnosis models across diverse operating conditions. While single-target domain adaptation has been extensively explored, its efficacy is reduced in scenarios involving multi-target and incremental-target domains. However, such scenarios are common in real-world projects, necessitating the exploration of more sophisticated, multi-domain, and incremental domain adaptation methods. In response, we propose an iterative-cluster domain adaptation (ICDA) method, which is tailored to address incremental multi-target domain adaptation challenges. On the basis of single-target domain adaptation, ICDA enhances domain alignment through cyclic iteration and clustering algorithms. Notably, it has been demonstrated to be effective in a range of domain adaptation scenarios, including single-target, multi-target, and incremental-target adaptation, even in the unlabeled target domains. The efficacy and superiority of ICDA over baseline models has been substantiated through validation on the CWRU and WINDEY datasets.

Cross-domain fault diagnosis using deep learning plays a critical role in ensuring the reliability and safety of mechanical systems. However, real-world industrial scenarios often involve unknown fault classes, which introduce significant challenges beyond environmental differences between training and testing phases. These unknown fault classes, which do not appear in the training data, create a cross-domain open set fault diagnosis problem where the target domain includes both known and unknown fault types with distinct distribution characteristics. Traditional domain adaptation methods that align source and target domains often overlook the spatial distribution of each class in the feature space, leading to potential negative transfer and misclassification of unknown faults. To address these challenges, this paper proposes a k-nearest neighbors based adaptive thresholding (KNNAT) method, which dynamically adjusts classification thresholds based on the spatial distribution of each class in the feature space. This approach effectively isolates unknown faults, reducing their impact on domain adaptation and improving the reliability of the diagnostic process. Extensive experiments on the publicly available CWRU bearing and PHM09 datasets demonstrate that the proposed KNNAT method outperforms other state-of-the-art methods, achieving higher accuracy and robustness in identifying known faults while successfully isolating unknown faults. These results highlight the potential of using the KNNAT method to enhance the reliability of mechanical systems in cross-domain fault diagnosis applications.

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No-show is a thorny issue within the social scope. It not only affects the sustainability of service system operation but also causes heavy irretrievable losses. To maintain and develop the sustainability of service, this paper adopts bibliometric technology to reflect the current status and future prospects about no-show research. And we strive to explore and summarize appointment scheduling methods for no-show problems. The bibliometric analysis was carried out from various aspects including research areas, countries/regions, institutions, journals, authors and author keywords based on papers harvested from Web of Science Core Collection database. The total 1197 papers show that the United States is in a leading position in this field, followed by England and Canada. University of London is the most productive institution with the highest total citations and H-Index. BMC Health Services Research ranks first as the most productive journal, followed by European Journal of Operational Research and Production and Operations Management. Through the analysis of hot articles, we can conclude that how to reduce the impact of no-shows on the sustainability of service systems has become the main research direction. In addition to appointment scheduling, other effective methods are also mentioned. Further study on these methods will be a major research direction in the future.