Hybrid Turbo-Shaft Engine Digital Twinning for Autonomous Aircraft via AI and Synthetic Data Generation

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dc.authoridArdebili, Ali Aghazadeh/0000-0002-3557-9986
dc.authoridKhalil, Adem/0009-0001-9362-4992
dc.authoridFicarella, Antonio/0000-0003-3206-4212
dc.authoridKHALIL, Sabri/0009-0005-7227-1920
dc.authoridLongo, Antonella/0000-0002-6902-0160
dc.contributor.authorArdebili, Ali Aghazadeh
dc.contributor.authorFicarella, Antonio
dc.contributor.authorLongo, Antonella
dc.contributor.authorKhalil, Adem
dc.contributor.authorKhalil, Sabri
dc.date.accessioned2024-09-11T19:53:07Z
dc.date.available2024-09-11T19:53:07Z
dc.date.issued2023
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description.abstractAutonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital twins (DTs) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution based on AI, proper data management is essential in implementing DT as a service (DTaaS). One of the challenges in DT development is the availability of real-life data, particularly for training algorithms and verifying the functionality of DT. The current article focuses on data augmentation through synthetic data generation. This approach can facilitate the development of DT in case the developers do not have enough data to train the machine learning (ML) algorithm. The current twinning approach provides a prospective ideal state of the engine used for proactive monitoring of the engine's health as an anomaly detection service. In line with the track of unmanned aircraft vehicles (UAVs) for urban air mobility in smart city applications, this paper focuses specifically on the common hybrid turbo-shaft in drones/helicopters. However, there is a significant gap in real-life similar synthetic data generation in the UAV domain literature. Therefore, rolling linear regression and Kalman filter algorithms were implemented on noise-added data, which simulate the data measured from the engine in a real-life operational life cycle. For both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters except the engine torque.en_US
dc.description.sponsorshipPh.D. school of the University of Salento [CUP F87G22000270002]en_US
dc.description.sponsorshipThis research was supported by the Ph.D. school of the University of Salento, Department of Complex Systems Engineering-XXXVI cycle; and the RIPARTI regional project-dataEnrichment for Resilient UAS (assegni di RIcerca per riPARTire con le Imprese)-POC PUGLIA FESRTFSE 2014/2020, CUP F87G22000270002. We also like to express our heartfelt gratitude to Maria Pia Romano for English proofreading of the article.en_US
dc.identifier.doi10.3390/aerospace10080683
dc.identifier.issn2226-4310
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85168668624en_US
dc.identifier.urihttps://doi.org/10.3390/aerospace10080683
dc.identifier.urihttps://hdl.handle.net/11363/8074
dc.identifier.volume10en_US
dc.identifier.wosWOS:001119087000001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofAerospaceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240903_Gen_US
dc.subjectautonomous aircraften_US
dc.subjecturban air mobilityen_US
dc.subjectdigital twinsen_US
dc.subjectunmanned aircraft systemsen_US
dc.subjectunmanned aircraft vehiclesen_US
dc.subjectsynthetic data generationen_US
dc.subjectdata for resilienceen_US
dc.subjecttransport complex systemsen_US
dc.subjectsmart citiesen_US
dc.subjectdigitalizationen_US
dc.titleHybrid Turbo-Shaft Engine Digital Twinning for Autonomous Aircraft via AI and Synthetic Data Generationen_US
dc.typeArticleen_US

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