Digital twins assisted for fault diagnosis of bearing: a literature review

Digital twin (DT) technology, increasingly applied in bearing fault diagnosis, effectively overcomes the key limitations of traditional bearing fault diagnosis methods, such as over-reliance on samples, poor adaptability to complex conditions, and inadequate lifecycle management, thereby enabling high-precision and intelligent fault diagnosis for bearings. This paper provides a systematic review of the latest developments in DT-assisted bearing fault diagnosis. It begins by reviewing the key technologies in the application of DT for bearing fault diagnosis, specifically examining the methods for constructing bearing DT (BDT) models and the post-processing of these models. Regarding the modeling methods of BDT, this paper offers a comprehensive review and comparative analysis, classifying them into physics-driven models, phenomenological models, and data-driven models. Besides, the post-processing of BDT models is summarized in two key aspects: strategies for model correction and updates, and model verification. Subsequently, this paper discusses the core applications of DT technology in bearing fault diagnosis, categorizing them into four key scenarios: small-sample or imbalanced-sample diagnosis, cross-domain or variable-operating-condition diagnosis, fault evolution and remaining useful life prediction, and real-time condition monitoring. Furthermore, the key challenges of DT technology in bearing fault diagnosis are explored, including model accuracy, real-time data integration, and system complexity. In response to these challenges, the paper offers targeted recommendations for the future development of DT applications. Overall, this study clarifies the technological framework and development path, aiming to provide comprehensive theoretical references and practical guidance for future academic research and engineering applications of DT technology in the field of bearing fault diagnosis.