A RAM-Based Analytical Framework for analysis of Critical Components in Turbofan Engines

Abstract

 Turbofan jet engines are critical to commercial aviation, where unplanned failures pose severe risks to safety, operational continuity, and airline finances, with each unplanned engine removal costing approximately USD 1.5–2.2 million due to flight cancellations, crew repositioning, and maintenance expenses. Therefore, the present study develops and validates a comprehensive Reliability–Availability–Maintainability (RAM) statistical framework specifically designed for critical turbofan engine components. Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) were applied to identify 38 critical failure modes across high-pressure compressors, bearing systems, turbine sections, and control systems. Operational and maintenance data from three airlines spanning 2015–2024 were analyzed using trend tests and reliability modeling techniques. The findings indicate pronounced wear-out behavior in high pressure compressor blades and turbine nozzles, while rotor seals exhibit predominantly random failure patterns. Overall, the proposed RAM framework enables a transition from conservative fixed-interval maintenance to evidence-based, reliability-driven strategies, leading to reduced unplanned removals and maintenance costs. Pilot implementations demonstrated significant operational and financial benefits, supporting the framework’s broader adoption across airline operations.