(untitled) (REC 2026 - 11th International Workshop on Reliable Engineering Computing: Reliability Computations in a Data and Model-Driven World, 2026)
This study aims to demonstrate the value of life cycle analysis in legacy aircraft based on data fusion from asset usage and respective monitoring parameters to support management decisions across all operational levels, being the extend of the useful life of assets or the configuration management. The research includes a case study focused on the propeller system, which serves as a reference for applying the methodology to the entire aircraft and its components. The proposed analysis integrates state-of-the-art models covering three dimensions: Life Cycle Investment, Sustainability-Oriented Life Cycle Assessment, and Operational Risk Analysis. These models are interconnected to identify cause-and-effect relationships across the different spectra, providing a holistic view of asset performance and degradation. The methodology considers all operational and maintenance activities, from acquisition to decommissioning, with the objective of increasing asset availability, optimizing resource allocation, reducing component cannibalization, and extending Remaining Useful Life (RUL). A key feature of the approach is real-time data updating, enabling continuous and accurate information refreshment. This dynamic data environment supports the development of machine learning models and the integration of Generative Artificial Intelligence (GenAI), enhancing predictive and prescriptive capabilities, increasing reliability, and improving risk analysis and forecasting.