Post-flight maintenance is a critical component of aviation safety, directly influencing the airworthiness, reliability, and operational readiness of aircraft. As aviation systems grow more complex, the industry faces the challenge of evolving from traditional reactive inspection methods to intelligent, data-driven maintenance strategies. This article explores current limitations in post-flight diagnostics, such as dependence on manual inspections, fragmented data systems, and a lack of integration across maintenance processes. It further highlights the transformative potential of emerging technologies including condition-based maintenance (CBM), prognostics and health management (PHM), and digital twins. These innovations enable real-time monitoring, predictive analytics, and proactive maintenance planning, ultimately reducing operational costs and minimizing unscheduled downtime. The study emphasizes the need for a shift towards integrated diagnostic infrastructures that combine AI, IoT, and advanced analytics to support timely decision-making and enhance flight safety. The paper also examines turnaround time optimization and addresses the human factor challenges in manual workflows. By proposing a framework for modernizing post-flight maintenance, the article contributes to the development of safer and more efficient aviation operations.
Maturazov et al. (Mon,) studied this question.
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