Critical assets such as power generation equipment, industrial machinery, transportation infrastructure, and manufacturing systems are essential to the reliability and safety of modern socio-technical environments. Unexpected failures in these assets can result in costly downtime, safety risks, and service disruptions. Conventional condition monitoring and fault diagnosis approaches—often based on periodic inspections, fixed thresholds, or isolated data analysis—are increasingly inadequate for complex assets operating under variable and uncertain conditions. In this context, Digital Twin-enabled condition monitoring and predictive fault diagnosis offers a powerful, data-driven paradigm for proactive asset health management.

This study proposes a Digital Twin-based framework that integrates real-time sensor data, physical system models, and advanced analytics to enable continuous monitoring and predictive fault diagnosis of critical assets. The Digital Twin acts as a dynamic virtual replica of the physical asset, continuously updated through operational data streams such as vibration, temperature, electrical, and process signals. Machine learning and statistical inference techniques are employed to detect anomalies, identify fault signatures, and capture degradation trends, while physics-informed constraints ensure consistency with underlying system behaviour. A key feature of the proposed approach is its predictive capability. By embedding remaining useful life estimation and fault progression modelling within the Digital Twin, the framework enables early warning of impending failures and supports proactive maintenance planning. Asset operators can evaluate “what-if” scenarios, assess the impact of operating conditions on asset health, and optimise maintenance strategies to minimise downtime and lifecycle costs. The effectiveness of the framework is demonstrated through illustrative use cases involving representative critical assets, showing improved fault detection accuracy and earlier diagnosis compared to conventional monitoring methods. Overall, this work highlights Digital Twin–enabled condition monitoring as a foundation for intelligent, predictive asset management, supporting enhanced reliability, safety, and operational efficiency in critical infrastructure and industrial systems.