The application progress of digital twin technology in fault prediction and health management

This study aims to review the current research status and application progress of digital twin technology in the field of Predictive Maintenance and Health Management, exploring its key technologies, challenges, and future development trends. It elucidates the fundamental concepts of digital twin technology and its significance in the PHM domain. Subsequently, it systematically organizes the key technological components of digital twin-driven fault prediction and health diagnostics, including high-fidelity model construction, multi-source data fusion, intelligent prediction algorithms, and decision support. Furthermore, it analyzes specific implementation pathways and outcomes through application cases in typical fields such as aerospace, equipment manufacturing, and energy power. Finally, it summarizes current technical challenges and outlines future research directions. By constructing dynamic virtual models that interact with physical entities in real time, digital twin technology delivers unprecedented accuracy, timeliness, and predictability for fault prediction and health diagnostics, establishing itself as a core enabling technology for intelligent operations and maintenance. However, challenges persist in model accuracy, data quality, timeliness, and system integration. Future work requires continuous breakthroughs in intelligent algorithms, standardized frameworks, and cross-domain integration.