A Structured Data Model for Asset Health Index Integration in Digital Twins of Energy Converters

The integration of Digital Twins into energy systems is redefining asset management by enabling real-time monitoring, predictive maintenance, and lifecycle optimization. A central challenge in this context is the structured assessment of asset condition, where the Asset Health Index (AHI) plays a critical role by consolidating heterogeneous data into a single, actionable indicator. This paper presents a structured data model specifically designed to integrate AHI methodologies into Digital Twins for energy converters. The model incorporates standardized practices like RAMI 4.0, organizing asset-related information into interoperable domains including physical hierarchy, operational monitoring, reliability assessment, and risk-based decision-making. A Unified Modeling Language (UML) class diagram formalizes the model architecture, which is deployed on Microsoft Azure using native IoT and analytics services to enable automated AHI calculation. The proposed approach is validated through a case study involving three high-capacity converters in distinct operating environments, demonstrating the model’s effectiveness in anticipating failures, optimizing maintenance strategies, and improving asset resilience. These results support the implementation of scalable, cloud-based digital twin solutions for advanced asset health management in the energy sector.