An AI-based Digital Twin Case Study in the MRO Sector
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. More specifically, the current study aims to 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. The three 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 can then be combined with a Machine Learning (ML) model, such as a Random Forest (RF), with a multitude of decision trees. Following, the AI system determines whether the PECS operations is considered normal, aiming to optimize the performance of the system and to maximize the Useful Remaining Life (URL). The suggested AI-DT approach is based both on data-driven and physics-based models, an approach which results in increased reliability and availability, reducing possible Aircraft on Ground (AOG) events. Subsequently, the enhanced prediction capability results in the optimization of the maintenance processes and in reduced operational costs.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
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Supplemental Notes:
- © 2021 Asteris Apostolidis, et al. Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
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Authors:
- Apostolidis, Asteris
- Stamoulis, Konstantinos P
- Publication Date: 2021
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 55-62
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Serial:
- Transportation Research Procedia
- Volume: 56
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 2352-1465
- Serial URL: http://www.sciencedirect.com/science/journal/23521465/
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Aircraft; Artificial intelligence; Cooling systems; Maintenance; Predictive models; Service life
- Subject Areas: Aviation; Data and Information Technology; Design; Maintenance and Preservation; Operations and Traffic Management; Vehicles and Equipment;
Filing Info
- Accession Number: 01786415
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 26 2021 2:30PM