Novel hybrid prognostics of aircraft systems

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dc.contributor.authorFu, Shuai
dc.contributor.authorAvdelidis, Nicolas P.
dc.contributor.authorPlastropoulos, Angelos
dc.date.accessioned2025-06-24T12:32:28Z
dc.date.available2025-06-24T12:32:28Z
dc.date.freetoread2025-06-24
dc.date.issued2025-05-28
dc.date.pubOnline2025-05-28
dc.descriptionThis article belongs to the Special Issue Fault Detection Technology Based on Deep Learning
dc.description.abstractAccurate forecasting of the remaining useful life (RUL) of aviation equipment is crucial for enhancing safety and reducing maintenance costs. This study presents a novel hybrid prognostic methodology that integrates physics-based and data-driven models to improve RUL estimations for critical aircraft components. The physics-based approach simulates long-term degradation patterns using fundamental principles such as mass conservation and Bernoulli’s equation, while the data-driven model employs a hyper tangent boosted neural network (HTBNN) to detect short-term anomalies and deviations in real-time sensor data. The integration of various models enhances accuracy, adaptability, and reliability in prognostics. The proposed methodology is assessed using NASA’s N-CMAPSS dataset for gas turbines and a fuel system test rig, demonstrating a 15% improvement in prediction accuracy and a 20% reduction in uncertainty compared to traditional methods. These findings highlight the potential for widespread application of this hybrid methodology in predictive maintenance and prognostic and health management (PHM) of aircraft systems.
dc.description.journalNameElectronics
dc.description.sponsorshipThis research has received funding from the European Commission under the Marie Skłodowska Curie program through the H2020 ETN MOIRA project (GA 955681).
dc.identifier.citationFu S, Avdelidis NP, Plastropoulos A. (2025) Novel hybrid prognostics of aircraft systems. Electronics, Volume 14, Issue 11, May 2025, Article number 2193en_UK
dc.identifier.eissn2079-9292
dc.identifier.elementsID673593
dc.identifier.issn1450-5843
dc.identifier.issueNo11
dc.identifier.paperNo2193
dc.identifier.urihttps://doi.org/10.3390/electronics14112193
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/24047
dc.identifier.volumeNo14
dc.languageEnglish
dc.language.isoen
dc.publisherMDPIen_UK
dc.publisher.urihttps://www.mdpi.com/2079-9292/14/11/2193
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject4007 Control Engineering, Mechatronics and Roboticsen_UK
dc.subject40 Engineeringen_UK
dc.subject7 Affordable and Clean Energyen_UK
dc.subject4009 Electronics, sensors and digital hardwareen_UK
dc.subjectremaining useful lifeen_UK
dc.subjecthybrid prognosticsen_UK
dc.subjectpredictive maintenanceen_UK
dc.subjectaircraft systemsen_UK
dc.subjectdata-driven modelsen_UK
dc.subjectphysics-based modelsen_UK
dc.subjecthyper tangent boosted neural network (HTBNN)en_UK
dc.titleNovel hybrid prognostics of aircraft systemsen_UK
dc.typeArticle
dcterms.dateAccepted2025-05-23

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