An AI-based Digital Twin Case Study in the MRO Sector
Publication - january 2021
In this work, the concept of an Artificial Intelligence-based (AI) Digital Twin (DT) of an aircraft system is introduced, with the goal to improve the corresponding MRO operations. The current study aims at obtaining knowledge on the optimal placement of sensors in an ideal Power Electronics Cooling System (PECS) of a modern airliner, aiming to improve input data as a basis for an AI-based DT.
Measurement Parameters
The 3 main fluid parameters to be measured directly or indirectly at various physical locations at the PECS are mass flow rate, temperature, and static pressure. The physics-based model is then combined with a Machine Learning (ML) model, such as a Random Forest (RF), with multiple decision trees.
AI System and Benefits
The AI system determines whether the PECS operation is normal, optimizing system performance and maximizing Useful Remaining Life (URL). The suggested AI-DT approach, based on both data-driven and physics-based models, results in increased reliability, availability, and reduced Aircraft on Ground (AOG) events.
Conclusion
The enhanced prediction capability leads to the optimization of maintenance processes and reduced operational costs.
Authors
- Asteris Apostolidis
- Konstantinos Stamoulis
Aviation Engineering research group
The aviation industry must become smarter and more sustainable. The Aviation Engineering research group is ensuring the sector has all the knowledge and insights it needs to transition to, and develop, more-efficient en more-environmentally friendly engineering and operational practices.