A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA

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dc.authorid0000-0002-6402-0479
dc.authorid0000-0002-1038-7224
dc.authorid0000-0002-4353-1261
dc.authorid0000-0002-4049-0716
dc.contributor.authorDagal, Idriss
dc.contributor.authorErol, Bilal
dc.contributor.authorMbasso, Wulfran Fendzi
dc.contributor.authorHarrison, Ambe
dc.contributor.authorDemirci, Alpaslan
dc.contributor.authorCali, Umit
dc.date.accessioned2026-01-31T15:08:39Z
dc.date.available2026-01-31T15:08:39Z
dc.date.issued2025
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractAircraft engine MRO is essential for safe, reliable, and cost-effective aviation operations. Traditional maintenance methods, such as scheduled and condition-based maintenance, often result in excessive downtime, higher costs, and inefficient resource use. AI-driven predictive maintenance, combined with Reliability Engineering, enhances efficiency but typically lacks integration with systematic reliability assessment frameworks, limiting its ability to prioritize critical failures. This study introduces a hybrid predictive maintenance framework integrating artificial neural networks (ANN) with failure modes, effects, and criticality analysis (FMECA). Historical engine sensor data (temperature, pressure, vibration, and oil analysis) trains an ANN that predicts failure probabilities, repair durations, and costs. FMECA, utilizing the Risk Priority Number (RPN), ranks failures by severity, ensuring that the most critical issues are addressed first Weibull distribution analysis models component reliability, confirming wear-out failure modes, and supporting scheduled predictive maintenance. Validation with real aircraft engine data demonstrates the effectiveness of the ANN-FMECA model, achieving 94.3% accuracy in failure prediction and surpassing conventional methods. Maintenance prioritization efficiency improves by 15.7%, reducing maintenance costs by 35.3% and unplanned outages by 40.5%. This enhances fleet availability, improves flight safety, and reduces environmental impact.
dc.identifier.doi10.1109/ACCESS.2025.3587090
dc.identifier.endpage124733
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-105010184214
dc.identifier.scopusqualityQ1
dc.identifier.startpage124710
dc.identifier.urihttps://doi.org./10.1109/ACCESS.2025.3587090
dc.identifier.urihttps://hdl.handle.net/20.500.12662/10718
dc.identifier.volume13
dc.identifier.wosWOS:001534536400037
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260128
dc.subjectAircraft engine MRO
dc.subjectpredictive maintenance
dc.subjectartificial neural networks
dc.subjectFMECA
dc.subjectreliability engineering
dc.subjectAircraft engine MRO
dc.subjectpredictive maintenance
dc.subjectartificial neural networks
dc.subjectFMECA
dc.subjectreliability engineering
dc.titleA Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA
dc.typeArticle

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