AI-Driven Predictive Maintenance for Aircraft Engine Health Monitoring
Keywords:
predictive maintenance, aircraft engine health monitoring, generative AI, failure prognosticsAbstract
Optimising aircraft engine health monitoring with the help of AI-driven predictive maintenance has emerged as an evolutionary approach which uses generative AI (GenAI) models in addition with high frequency sensor data. The aim of this research is to investigate the application of transformer-based architecture for real-time anomaly detection, failure prognostics, and predictive analytics in aviation maintenance.
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References
H. R. Choi, J. Kim, and H. Kim, “Artificial intelligence-based predictive maintenance for aircraft systems: A review,” Aerospace Science and Technology, vol. 125, pp. 107241, 2022.
George, Jabin Geevarghese. "Advancing Enterprise Architecture for Post-Merger Financial Systems Integration in Capital Markets laying the Foundation for Machine Learning Application." Aus. J. ML Res. & App 3.2 (2023): 429.
Dash, S. "Architecting Intelligent Sales and Marketing Platforms: The Role of Enterprise Data Integration and AI for Enhanced Customer Insights." Journal of Artificial Intelligence Research 3.2 (2023): 253-291.
Singu, Santosh Kumar. "Migration strategies for legacy data warehousing systems to cloud platforms." Internafional Journal of Science and Research (IJSR) 12, no. 12 (2023): 2164-2167.
George, Jabin Geevarghese. "HARNESSING GENERATIVE AI FOR ENTERPRISE APPLICATION MODERNIZATION: ENHANCING CYBERSECURITY AND DRIVING INNOVATION." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) 15.3 (2024): 377-392.
Dash, S. "Designing Modular Enterprise Software Architectures for AI-Driven Sales Pipeline Optimization." Journal of Artificial Intelligence Research 3.2 (2023): 292-334.
Godbole, Aditi, Jabin Geevarghese George, and Smita Shandilya. "Leveraging Long-Context Large Language Models for Multi-Document Understanding and Summarization in Enterprise Applications." arXiv preprint arXiv:2409.18454 (2024).
Akhilandeswari, P., and Jabin G. George. "Secure Text Steganography." Proceedings of International Conference on Internet Computing and Information Communications: ICICIC Global 2012. Springer India, 2014.
Singu, Santosh Kumar. "Impact of Data Warehousing on Business Intelligence and Analytics." ESP Journal of Engineering & Technology Advancements 2.2 (2022): 101-113.
Santosh Kumar, Singu. "Maximizing financial intelligence-the role of optimized etl in fintech data warehousing." INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET) 15, no. 4 (2024): 464-471.
J. Sun, R. Du, and J. Zhang, “AI-driven condition monitoring for aircraft engines using deep reinforcement learning,” IEEE Access, vol. 9, pp. 42120–42134, 2021.
S. Kang and D. Choi, “A data-driven framework for predictive aircraft maintenance using federated learning,” in Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 2571–2580.
H. He and Y. Bai, “Application of convolutional neural networks in aircraft fault detection,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1–10, Jan. 2022.
Y. Zhao, H. Wu, and Y. Xu, “Optimizing predictive maintenance schedules with AI-based decision support systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 1, pp. 19–30, Jan. 2023.
J. Pei, Y. Xie, and R. Huang, “AI-driven diagnostics and prognostics for next-generation aircraft maintenance,” Aerospace Science and Technology, vol. 116, pp. 106807, Apr. 2021.
