Dagal, Idriss, Erol, Bilal, Fendzi Mbasso, Wulfran et al. (3 more authors) (2025) A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA. IEEE Access. pp. 124710-124733. ISSN: 2169-3536
Abstract
Aircraft 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 The Authors. |
Keywords: | Aircraft engine MRO,artificial neural networks,FMECA,predictive maintenance,reliability engineering |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 06 Aug 2025 08:30 |
Last Modified: | 06 Aug 2025 08:30 |
Published Version: | https://doi.org/10.1109/ACCESS.2025.3587090 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1109/ACCESS.2025.3587090 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:230116 |
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Description: A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA
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