An Optimized and Safety-aware Maintenance Framework: A Case Study on Aircraft Engine

TL;DR

This paper introduces an AI-assisted predictive maintenance framework for aircraft engines that optimizes costs while ensuring safety, using diagnostics and operational data to customize maintenance strategies, demonstrated through numerical experiments.

Contribution

It presents a novel integrated approach combining prognostics and simulation-based optimization for safety-aware, cost-effective aircraft engine maintenance.

Findings

Achieved significant cost savings without compromising safety.

Customized maintenance strategies yield 13% more savings than generic ones.

Framework can be extended to other safety-critical transportation systems.

Abstract

The COVID-19 pandemic has recently exacerbated the fierce competition in the transportation businesses. The airline industry took one of the biggest hits as the closure of international borders forced aircraft operators to suspend their international routes, keeping aircraft on the ground without generating revenues while at the same time still requiring adequate maintenance. To maintain their operational sustainability, finding a good balance between cost reductions measure and safety standards fulfillment, including its maintenance procedure, becomes critical. This paper proposes an AI-assisted predictive maintenance scheme that synthesizes prognostics modeling and simulation-based optimization to help airlines decide their optimal engine maintenance approach. The proposed method enables airlines to utilize their diagnostics measurements and operational settings to design a more customized maintenance strategy that takes engine operations conditions into account. Our numerical experiments on the proposed approach resulted in significant cost savings without compromising the safety standards. The experiments also show that maintenance strategies tailored to the failure mode and operational settings (that our framework enables) yield 13% more cost savings than generic optimal maintenance strategies. The generality of our proposed framework allows the extension to other intelligent, safety-critical transportation systems.