Digital Twin-Based Health Management for Complex Aircraft Systems: Case Studies and Applications

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dc.contributor.authorWang, Chengwei
dc.contributor.authorFan, Ip-Shing
dc.contributor.authorPlastropoulos, Angelos
dc.date.accessioned2025-05-23T08:21:35Z
dc.date.available2025-05-23T08:21:35Z
dc.date.freetoread2025-05-23
dc.date.issued2025-03-24
dc.date.pubOnline2025-05-09
dc.description.abstractDigital Twin technology, initially conceptualized during the NASA's Apollo program, has evolved into a transformative tool for system health management, particularly in aviation. By integrating high-fidelity simulations, real-time sensor data, and predictive analytics, DTs enable significant innovation in Prognostics and Health Management methods. This paper explores the application of DTs in health management for complex aircraft systems, focusing on two critical subsystems: Flight Control Electrical Actuators and Main Landing Gear. Leveraging MATLAB Simscape, modular DT frameworks were developed to simulate these systems under nominal, degraded, and fault conditions. The inclusion of fault injection models enables the generation of realistic datasets to support predictive maintenance, alleviating difficulties in data availability. Two case studies are presented to illustrate the potential of DT-based approaches to reduce downtime, optimizing performance, and enhancing system reliability. This paper provides a comparative analysis of existing DT tools, highlighting their capabilities and limitations in aerospace contexts. While platforms such as MATLAB Simulink and ANSYS Twin Builder offer robust modeling capabilities, operational tools like AVIATAR and IBM Maximo excel in fleet management and predictive analytics. This comparison highlights the need for tailored DT solutions that balance real-time capabilities, scalability, and configurability. This study contributes to the growing body of knowledge on DT technology, offering insights into its role in enhancing aviation safety, efficiency, and sustainability. It serves as a guide for applying DT-based health management, paving the way for broader adoption in next-generation aerospace systems.
dc.description.conferencename2025 IEEE Wireless Communications and Networking Conference (WCNC)
dc.description.sponsorshipInnovate UK
dc.description.sponsorshipThe research on Main Landing Gear Force and Torque DT was funded by Innovate UK grant number 10040817, under the ATI/IUK. The project was part of the OLLGA (Optimised Life for Landing Gear Assemblies), with Safran Landing Systems UK Ltd as Industrial Lead.
dc.identifier.citationWang C, Fan I-S, Plastropoulos A. (2025) Digital twin-based health management for complex aircraft systems: case studies and applications. In: 2025 IEEE Wireless Communications and Networking Conference (WCNC), 24-27 March 2025, Milan, Italy
dc.identifier.elementsID673124
dc.identifier.urihttps://doi.org/10.1109/wcnc61545.2025.10978545
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23917
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10978545
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4007 Control Engineering, Mechatronics and Robotics
dc.subject40 Engineering
dc.subject4010 Engineering Practice and Education
dc.subject7 Affordable and Clean Energy
dc.titleDigital Twin-Based Health Management for Complex Aircraft Systems: Case Studies and Applications
dc.typeConference paper
dcterms.coverageMilan, Italy
dcterms.dateAccepted2025-01-16
dcterms.temporal.endDate27 Mar 2025
dcterms.temporal.startDate24 Mar 2025

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