Эта статья является препринтом и не была отрецензирована.
О результатах, изложенных в препринтах, не следует сообщать в СМИ как о проверенной информации.
AI-Powered Predictive Maintenance in Aviation Operations
This article explores the transformative impact of AI-powered predictive maintenance on base and line maintenance operations in the aviation industry. The study addresses the limitations of traditional maintenance practices by integrating advanced technologies, including machine learning, IoT sensors, and big data analytics, to enhance operational safety, reliability, and cost-efficiency. A mixed-methods research design was adopted, combining quantitative data from maintenance logs, sensor outputs, and cost reports with qualitative insights obtained through semi-structured interviews with industry experts. Analysis of key performance indicators (KPIs) such as Mean Time Between Failures (MTBF), Fault Detection Rate (FDR), and Maintenance Cost per Available Seat Kilometer (CASK) revealed significant improvements in technical performance and operational efficiency. The findings indicate that AI-driven predictive maintenance can reduce maintenance costs by 12–18% and decrease unplanned downtime by 15–20%, thereby increasing aircraft availability. However, challenges related to data quality, integration with legacy systems, regulatory compliance, and high initial investments persist. The study concludes that strategic partnerships, phased implementation, and targeted workforce training are essential for the successful adoption of AI technologies in aviation maintenance. This research contributes to the growing body of knowledge on digital transformation in aviation, providing a roadmap for enhancing maintenance practices and ensuring sustainable operational performance.
1. A. Pundir, Pratik Maheshwari, P. Prajapati (2022). Machine Learning Based Predictive Maintenance Model. Proceedings of the International Conference on Industrial Engineering and Operations Management.
2. Caricato, A., Ficarella, A., & Spada Chiodo, L. (2021). Prognostic techniques for aeroengine health assessment and Remaining Useful Life estimation. 312, 11017. https://doi.org/10.1051/E3SCONF/202131211017.
3. Chaudhary, A., Rastogi, R., Chola, A., Josan, P., & Biswas, D. (2024). Cloud based Predictive Maintenance Technique for Aviation System. 1, 2567–2572. https://doi.org/10.1109/icaccs60874.2024.10717276.
4. Hasib, A. A., Rahman, A., Khabir, M., & Shawon, Md. T. R. (2023). An Interpretable Systematic Review of Machine Learning Models for Predictive Maintenance of Aircraft Engine. arXiv.Org, abs/2309.13310. https://doi.org/10.48550/arxiv.2309.13310.
5. Kabashkin, I., & Perekrestov, V. (2024). Ecosystem of Aviation Maintenance: Transition from Aircraft Health Monitoring to Health Management Based on IoT and AI Synergy. Applied Sciences. https://doi.org/10.3390/app14114394.
6. Moghadasnian, S. (2020). Soaring Above Boundaries: A Comprehensive Guide to KPIs for the Chief Logistics Officer in the Airline Industry: Leveraging Metrics to Optimize Airline Logistics and Streamline Operations. Aviation and Tourism Research and Innovation Center (ATRIC). Digital Publication.
7. Moghadasnian, S. (2022). Flight to Excellence: A Comprehensive Guide to Key Performance Indicators in the Airline Industry: Unlocking Success Through Data-Driven Strategies and Performance Metrics. Aviation and Tourism Research and Innovation Center (ATRIC), Digital Publication.
8. Moghadasnian, S. (2023). Strategica Aeronautica: Mastering KPI-Driven Leadership Across the Airline and Tourism Ecosystem: A Comprehensive Guide for Executives: From Analytic Hierarchy Process to Zero-Based Budgeting, Navigate the Full Spectrum of Strategic Decision-Making Metrics. Aviation and Tourism Research and Innovation Center (ATRIC). Digital Publication.
9. Moghadasnian, S., & EbrahimNezhad, M. (2024). Optimizing Airline Logistics Stock Control: The Strategic Role of Key Performance Indicators (KPIs). Presented at the 9th National Conference on Interdisciplinary Research in Engineering and Management, Tehran, Iran, 20 November.
10. Moghadasnian, S., & Mirfaizi, S. (2024). Enhancing Airline Logistics Efficiency: KPI-Driven Strategies. Presented at the 4th International Conference on Advanced Research in Management and Humanities, Munich, Germany, 11 July.
11. Pathan, M. (2024). A Comprehensive Survey of Predictive Maintenance Techniques for Aircraft Engines Utilizing the C-MAPSS Dataset. Indian Scientific Journal Of Research In Engineering And Management. https://doi.org/10.55041/ijsrem34660.
12. Simon Widmer, Syed Shaukat, Cheng-Lung Wu (2023). Aircraft Line Maintenance Scheduling Using Simulation and Reinforcement Learning. Online World Conference on Soft Computing in Industrial Applications
13. Usharani, R., Sivagami, V. M., Saravanan, K., Pushparani, S., & Rekha, K. S. (2024). Cloud-Enhanced Machine Learning Models for Predictive Maintenance in Industrial IoT. 29, 1–5. https://doi.org/10.1109/tqcebt59414.2024.10545129.
