• Rahul Kumar

Abstract: Intensifying intricacy of contemporary fighter aircrafts necessitates sophisticated maintenance approaches for their operational efficiency and mission readiness. In this respect the proposed research is aimed at the AI-based predictive maintenance and threat analysis system that will be based on real-time sensor data and deep learning techniques. Objectives: The research is aimed at improving the reliability of aircraft by implementing an intelligent system that can predict failures before they occur and identify potential threats in real time. Methods: The System proposed utilizes machine learning algorithms to examine historical and real-time sensor data to enable predictive diagnostics for aircraft components. It also incorporates a deep learning-based threat analysis module for anomaly detection and situational awareness improvement. The system is developed using C# .NET for backend processing and SQL for structured data storage and retrieval. Findings: The results of the experiments prove the important increase in the accuracy of prediction of failures comparing to usual maintenance strategies. The implementation of the AI-driven threat detection improves the safety and makes the operation more informed. Novelty: This study offers the novelty of the dual-function AI system: predictive maintenance and real-time threat analysis, which together provide full solution to the present-day problems of the fighter jet operation.

Predictive maintenance, Fighter jet AI, Deep learning, Anomaly detection, Real-time threat analysis.

"AI-POWERED PREDICTIVE MAINTENANCE AND THREAT ANALYSIS SYSTEM FOR FIGHTER JETS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.f457-f461, August-2025, Available :http://www.jetir.org/papers/JETIR2508561.pdf

"AI-POWERED PREDICTIVE MAINTENANCE AND THREAT ANALYSIS SYSTEM FOR FIGHTER JETS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. ppf457-f461, August-2025, Available at : http://www.jetir.org/papers/JETIR2508561.pdf