Novel formulations and metaheuristic algorithms for predictive maintenance of aircraft engines with remaining useful life prediction

Lubing Wang, Xufeng Zhao and Hoang Pham

Reliability Engineering and System Safety, 2025, vol. 261, issue C

Abstract: Advanced sensor technology has driven the remaining useful life (RUL) prediction of aircraft engines. However, only a few studies have considered incorporating RUL prediction results into maintenance plans. To address this problem, this paper investigates a novel predictive maintenance framework for aircraft engines. First, a hybrid deep learning model is developed to predict the aircraft engine RUL. Based on the predicted RUL, two new mixed integer linear programming models are developed to deal with the predictive maintenance problem of aircraft engines, which targets to minimize the maximum maintenance completion time for all aircraft engines. Since commercial solvers (e.g. CPLEX) solving it is time-consuming as the problem scale increases, we develop a new fast and effective hybrid metaheuristic algorithm based on the problem features, which combines a genetic algorithm and a variable neighborhood search algorithm. Finally, numerical experiments from the NASA aircraft engine dataset validate the proposed predictive maintenance framework can provide the optimal predictive maintenance plan in less than 10 s for large-scale maintenance problems, thereby reducing aircraft maintenance completion time.

Keywords: Aircraft engines; Predictive maintenance; Remaining useful life; Deep learning; Metaheuristic algorithms (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:261:y:2025:i:c:s0951832025002650

DOI: 10.1016/j.ress.2025.111064

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