Proactive Aircraft Engine Removal Planning with Dynamic Bayesian Networks

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Published Nov 5, 2024
Bharath Pidaparthi Ryan Jacobs Sayan Ghosh Sandipp Krishnan Ravi Ahmad W. Amer Lele Luan Murali Krishnan Rajasekharan Pillai Feng Zhang Victor Perez Liping Wang

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.

How to Cite

Pidaparthi, B., Jacobs, R. ., Ghosh, S., Ravi, S. K., Amer, A. W., Luan, L., Rajasekharan Pillai, M. K., Zhang, F., Perez, V., & Wang, L. (2024). Proactive Aircraft Engine Removal Planning with Dynamic Bayesian Networks. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4148
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Keywords

Prognostics, Probabilistic Digital Twins, Dynamic Bayesian Networks, Cumulative Damage Modeling, Aircraft Engine Maintenance

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Section
Industry Experience Papers