This chapter presents a structured approach for developing and applying digital twins (DTs) in asset health monitoring and lifecycle management. The proposed methodology is based on the modular construction and integration of Basic Digital Twins (BDTs), which represent individual components, failure modes and inspection properties associated with physical assets. The chapter emphasises the importance of aligning the structure of the digital twin (DT) with the functional hierarchy of the asset, and its content with data models designed to support maintenance and investment decision-making processes. Special attention is given to the definition of health indicators such as the Asset Health Index (AHI) and the Remaining Useful Life (RUL), which enable the implementation of predictive maintenance strategies and scenario-based lifecycle management. The integration of artificial intelligence and machine learning techniques is also addressed in the context of an evolution from condition-based diagnosis towards prescriptive and risk-informed interventions. A case study on the lifecycle management of a Matisa B66 track tamping machine illustrates the practical application of the methodology. The chapter concludes with a discussion of implementation challenges and future research directions, highlighting the potential of DTs to become key tools within intelligent asset management systems.