As an advanced technology driven by the Fourth Industrial Revolution (Industry 4.0), Digital Twin (DT) has generated increasing attention and has been widely adopted across various sectors, including aerospace, healthcare and manufacturing. Despite the significant progress in both theoretical development and practical applications, notable challenges remain in applying DT to prognostics and health management (PHM), particularly in the case of rotating machinery. This article reviews the existing literature on DTs, clarifying its concept and applications across different fields. The implementation of DT for rotating machinery is analysed with the use of a five-dimensional model, with detailed discussions on the specific techniques used in each stage. Additionally, the role of DTs in each phase of a PHM process is analysed, aiming to assess its effectiveness and contribution to enhance PHM workflows. Current applications of DT integration in anomaly detection, fault diagnosis and remaining useful life prediction are also examined, emphasizing the advantages of DT in overcoming the limitations of traditional PHM approaches. Furthermore, the article addresses the urgent challenges facing the DT adoption, proposes potential solutions and offers potential research perspectives to further advance its application in rotating machinery systems.
Wang et al. (2025) studied this question.