AI-DRIVEN FAULT DIAGNOSIS IN AERONAUTICAL SYSTEMS USING CIRCUIT-LEVEL SENSORS FOR PREDICTIVE MAINTENANCE

Abstract

This study investigates the application of AI-driven fault diagnosis systems using circuit-level sensors for predictive maintenance in aeronautical systems. The research demonstrates that AI-based systems outperform traditional fault detection methods in multiple areas, including fault detection accuracy, energy efficiency, thermal management, and cost-effectiveness. Traditional techniques achieved detection accuracy of 80% but the AI-based system performed better with 98%.  Artificial intelligence technologies perform better than conventional methods and raise both reliability and efficiency together with reducing maintenance expenses by forty percent and downtime by fifty percent.  The AI-based system demonstrated that it could improve energy efficiency by 10% while thermal management achieved a 15% enhancement which guaranteed better operational stability.  Artificial intelligence optimization of predictive maintenance methods achieves better system diagnostics and reduces system failures and enhances aerospace system performance through these results.  The research outcomes demonstrate that operation effectiveness together with substantial savings in long-term maintenance costs can be achieved with AI-driven servicing solutions.  This research establishes artificial intelligence technology adoption in aircraft systems through its creation of an efficient and sustainable predictive maintenance system for aerospace components.