Title
Optimizing aircraft engine longevity: A comparative framework for dynamically adaptive predictive maintenance using autoencoders, LSTMs, and gaussian processes
Authors
[ 1 ] Instytut Napędów i Lotnictwa, Wydział Inżynierii Lądowej i Transportu, Politechnika Poznańska | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2025
Published in
Journal year: 2025 | Journal volume: vol. 156, part A
Article type
scientific article
Publication language
english
Keywords
EN
- Predictive maintenance
- Aircraft turbofan engine
- Autoencoders
- Long short-term memory networks
- Gaussian process regression
Abstract
EN The aviation industry’s reliance on fixed-interval maintenance schedules for aircraft engines poses significant inefficiencies, balancing precariously between excessive resource expenditure and catastrophic safety risks. Traditional frameworks, such as the 50-h inspection cycle, fail to account for real-time engine degradation, often leading to unnecessary downtime or undetected failures. Recent machine learning (ML) advances offer transformative solutions through predictive maintenance (PdM), which dynamically adapts schedules based on engine health data. This study evaluates three ML methodologies: Autoencoders (AEs), Long Short-Term Memory networks (LSTMs), and Gaussian Process Regression (GPR) to construct degradation indicators, predict Remaining Useful Life (RUL), and optimize maintenance timing. This work addresses a critical gap in adaptive maintenance strategies by comparing their efficacy in anomaly detection, temporal modeling, and uncertainty quantification. It offers a data-driven pathway to enhance safety, reduce costs, and extend engine operational life.
Pages (from - to)
111199-1 - 111199-8
License type
CC BY-NC (attribution - noncommercial)
Open Access Mode
czasopismo hybrydowe
Open Access Text Version
final published version
Date of Open Access to the publication
at the time of publication
Ministry points / journal
140
Impact Factor
7,5 [List 2023]
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