A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA
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...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11072555/ |
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| author | Idriss Dagal Bilal Erol Wulfran Fendzi Mbasso Ambe Harrison Alpaslan Demirci Umit Cali |
| author_facet | Idriss Dagal Bilal Erol Wulfran Fendzi Mbasso Ambe Harrison Alpaslan Demirci Umit Cali |
| author_sort | Idriss Dagal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6dbff3c17ec047c5a065b14d5b697bb4 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-6dbff3c17ec047c5a065b14d5b697bb42025-08-20T02:45:41ZengIEEEIEEE Access2169-35362025-01-011312471012473310.1109/ACCESS.2025.358709011072555A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECAIdriss Dagal0Bilal Erol1https://orcid.org/0000-0003-1810-6500Wulfran Fendzi Mbasso2https://orcid.org/0000-0002-4049-0716Ambe Harrison3https://orcid.org/0000-0002-4353-1261Alpaslan Demirci4https://orcid.org/0000-0002-1038-7224Umit Cali5https://orcid.org/0000-0002-6402-0479Department of Electrical-Electronics Engineering, Istanbul Beykent University, Istanbul, TürkiyeDepartment of Control and Automation Engineering, Yildiz Technical University, Istanbul, TürkiyeDepartment of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaDepartment of Biosciences, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, IndiaDepartment of Electrical Engineering, Yildiz Technical University, Istanbul, TürkiyeDepartment of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, NorwayAircraft 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.https://ieeexplore.ieee.org/document/11072555/Aircraft engine MROpredictive maintenanceartificial neural networksFMECAreliability engineering |
| spellingShingle | Idriss Dagal Bilal Erol Wulfran Fendzi Mbasso Ambe Harrison Alpaslan Demirci Umit Cali A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA IEEE Access Aircraft engine MRO predictive maintenance artificial neural networks FMECA reliability engineering |
| title | A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA |
| title_full | A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA |
| title_fullStr | A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA |
| title_full_unstemmed | A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA |
| title_short | A Data-Driven Approach to Aircraft Engine MRO Using Enhanced ANNs Based on FMECA |
| title_sort | data driven approach to aircraft engine mro using enhanced anns based on fmeca |
| topic | Aircraft engine MRO predictive maintenance artificial neural networks FMECA reliability engineering |
| url | https://ieeexplore.ieee.org/document/11072555/ |
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