Proactive Aircraft Engine Removal Planning with Dynamic Bayesian Networks

Abstract

Aircraft engine removal for maintenance is an expensive ordeal, and planning for it while balancing fleet stability objectives is a complex multi-faceted challenge. This is further compounded by uncertainties associated with usage or condition-based maintenance approaches that are becoming prevalent. Engine removal decisions rely on accurate estimation of damage growth or remaining useful life of critical components and a framework for aggregating these component-level estimates (and their uncertainties) into an engine-level removal forecasting model. An approach to this planning challenge is to develop probabilistic prognostic digital twins tailored to engine-specific operations and calibrate/update them with inspection data from the field. To this end, this work outlines a framework involving: 1) building component-level probabilistic models capable of forecasting damage growth or remaining useful life, 2) aggregating the outputs of these component-level models into a system-level view using a Dynamic Bayesian Network (DBN), and 3) updating the states of the DBN with inspection information as and when they become available.