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Fleet monitoring and diagnostics framework based on digital twin of aero-engines
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0001-6101-2863
Mälardalen University, School of Business, Society and Engineering.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-8466-356X
2018 (English)In: Proceedings of the ASME Turbo Expo, American Society of Mechanical Engineers (ASME) , 2018, Vol. 6Conference paper, Published paper (Refereed)
Abstract [en]

Monitoring aircraft performance in a fleet is fundamental to ensure optimal operation and promptly detect anomalies that can increase fuel consumption or compromise flight safety. Accurate failure detection and life prediction methods also result in reduced maintenance costs. The major challenges in fleet monitoring are the great amount of collected data that need to be processed and the variability between engines of the fleet, which requires adaptive models. In this paper, a framework for monitoring, diagnostics, and health management of a fleet of aircrafts is proposed. The framework consists of a multi-level approach: starting from thresholds exceedance monitoring, problematic engines are isolated, on which a fault detection system is then applied. Different methods for fault isolation, identification, and quantification are presented and compared, and the related challenges and opportunities are discussed. This conceptual strategy is tested on fleet data generated through a performance model of a turbofan engine, considering engine-to-engine and flight-to-flight variations and uncertainties in sensor measurements. Limitations of physics-based methods and machine learning techniques are investigated and the needs for fleet diagnostics are highlighted. 

Place, publisher, year, edition, pages
American Society of Mechanical Engineers (ASME) , 2018. Vol. 6
Keywords [en]
Aircraft engines, Engines, Fault detection, Learning systems, Turbofan engines, Turbomachinery, Uncertainty analysis, Aircraft performance, Fault detection systems, Life prediction methods, Machine learning techniques, Monitoring and diagnostics, Physics-based methods, Reduced maintenance costs, Sensor measurements, Fleet operations
National Category
Aerospace Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-41129DOI: 10.1115/GT2018-76414ISI: 000456908700036Scopus ID: 2-s2.0-85053863979ISBN: 9780791851128 (print)OAI: oai:DiVA.org:mdh-41129DiVA, id: diva2:1254117
Conference
ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition, GT 2018, 11 June 2018 through 15 June 2018
Available from: 2018-10-08 Created: 2018-10-08 Last updated: 2019-02-14Bibliographically approved

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Zaccaria, ValentinaAslanidou, IoannaKyprianidis, Konstantinos

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Zaccaria, ValentinaStenfelt, MikaelAslanidou, IoannaKyprianidis, Konstantinos
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Future Energy CenterSchool of Business, Society and Engineering
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  • apa
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