Year: 2024
Type: conference-paper
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.
Authors: Bharath Pidaparthi, Ryan Jacobs, Sayan Ghosh, Sandipp Krishnan Ravi, Ahmad W. Amer, Lele Luan, Murali Krishnan Rajasekharan Pillai, Feng Zhang, Victor Perez, Liping Wang
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4404098697