International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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AI-Based Predictive Analytics for Aircraft Engine Failures: From Pattern Recognition to Real-Time Intervention

Author(s) Aishani Sharma
Country India
Abstract In aviation, engine reliability is not just a technical requirement; it is a cornerstone of safety, efficiency, and trust. Aircraft engines are the heart of flight operations, and any compromise in them can jeopardize safety, disrupt schedules, and increase maintenance costs. As aircraft systems grow more complex, traditional maintenance strategies struggle to keep pace with the demands of modern flight. With rising passenger expectations and increasingly stringent fuel efficiency standards, aircraft systems must deliver peak performance with minimal downtime. These pressures make real-time diagnostics and predictive maintenance essential. This has paved the way for AI-based predictive analytics, a transformative approach that utilizes machine learning algorithms to predict engine failures before they occur. The scope of this technology extends beyond failure prediction; it encompasses real-time diagnostics, anomaly detection, flight path optimization, and intelligent resource management. As AI systems evolve, they are poised to become integral to every layer of aircraft operations, transforming maintenance from a reactive task into a strategic advantage.
Keywords aviation, engine reliability, predictive maintenance, machine learning, anomaly detection, AI-based diagnostics
Field Physics > Astronomy
Published In Volume 7, Issue 5, September-October 2025
Published On 2025-10-17
DOI https://doi.org/10.36948/ijfmr.2025.v07i05.56362
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E-ISSN 2582-2160
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