An AI-based Digital Twin Case Study in the MRO Sector

Abstract In this work, the concept of an Artificial Intelligence-based (AI) Digital Twin (DT) aircraft system is introduced, with goal to improve corresponding MRO Operations. More specifically, current study aims obtaining knowledge on optimal placement sensors in ideal Power Electronics Cooling System (PECS) a modern airliner, aiming input data as basis for AI-based DT. The three main fluid parameters be measured directly or indirectly at various physical locations PECS are mass flow rate, temperature and static pressure. physics-based model can then combined Machine Learning (ML) model, such Random Forest (RF), multitude decision trees. Following, AI determines whether operations considered normal, optimize performance maximize Useful Remaining Life (URL). suggested AI-DT approach based both data-driven models, which results increased reliability availability, reducing possible Aircraft Ground (AOG) events. Subsequently, enhanced prediction capability optimization maintenance processes reduced operational costs.