Digital twin-driven full lifecycle intelligent equipment maintenance: Progress and prospects

Abstract

Digital twin map physical entities through simulation modeling, significantly enhancing system reliability and reducing maintenance costs by utilizing data fusion, behavior simulation, optimization decision-making, and visualization through virtual-physical interaction. In intelligent operation and maintenance practices, common challenges include poor data quality, scarcity of abnormal samples, and unclear degradation processes. Digital twin technology offers a novel paradigm to address these issues. This paper systematically reviews simulation and modeling techniques within digital twin applications, summarizing recent research advancements in key areas such as anomaly detection, remaining useful life prediction, fault diagnosis, and operation and maintenance decision-making. Focusing on the demands for intelligent equipment operation and maintenance, we summarize the research findings and technical pathways related to digital twin-driven intelligent maintenance. Based on prior theoretical research and practical applications, we propose a four-level hierarchy for digital twin-driven intelligent operation and maintenance. Furthermore, we illustrate the application of digital twin-driven intelligent maintenance in real-world scenarios with a case study on naval equipment. Finally, considering current research and engineering practices, this paper proposes future research directions to provide insights and guidance for digital twin-driven intelligent maintenance across the equipment life cycle.