{"paper_id":"3db27513-e492-4953-b1f3-bf6db08360cd","body_text":"Predictive Maintenance Programs for Aircraft Engines Based on Remaining Useful Life Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Predictive Maintenance Programs for Aircraft Engines Based on Remaining Useful Life Prediction Fei Xue, Guodong Jin, Lining Tan, Chengxi Zhang, Ye Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6732629/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The remaining useful life (RUL) and utilization strategy of an aero-engine are related to the flight safety of an aircraft, which directly affects the flight itself and the safety of the occupants. Aiming at the complexity of aero-engine condition monitoring data, an aero-engine predictive maintenance planning framework based on RUL prediction is proposed, which aims to analyze the engine RUL and design predictive maintenance strategies. First, a deep learning integrated model (Trans-LSTM), including Transformer and Long Short Memory Network Model (LSTM), is proposed. Second, Bayesian optimization is used to optimize the hyperparameters of the integrated model to further improve the accuracy of the predictive model. Based on the prediction data, an engine alarm threshold was designed. When the threshold is triggered during engine operation, a predictive maintenance task is applied. The optimal alarm threshold under the Trans-LSTM model is calculated by comparing the total flight cost and other indicators under different flight hours. Experimental results demonstrate that the data-driven predictive maintenance strategy can monitor engine status in real time, promptly identify potential failure risks, and prevent engines from operating in an unknown state. This effectively reduces the risk of sudden engine failures and significantly enhances flight safety compared to the periodic maintenance strategy.In addition, through the accurate prediction of the engine state and reasonable arrangement of maintenance tasks, it can effectively reduce the cost of using the engine and avoid the waste of manpower, material and financial resources caused by excessive maintenance.Moreover, it enhances engine task availability, prolongs the engine's optimal operating period, better meets the actual needs of air transportation, and brings higher economic benefits and operational efficiency for airlines, thus showing great value and potential in practical application. Physical sciences/Engineering Physical sciences/Engineering/Aerospace engineering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 22 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviewers agreed at journal 15 Aug, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers invited by journal 23 Jul, 2025 Editor assigned by journal 09 Jun, 2025 Editor invited by journal 04 Jun, 2025 Submission checks completed at journal 03 Jun, 2025 First submitted to journal 23 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6732629\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":489625884,\"identity\":\"5797b285-2315-42e2-bd10-9556e625cac4\",\"order_by\":0,\"name\":\"Fei Xue\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"PLA Rocket Force University of Engineering\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fei\",\"middleName\":\"\",\"lastName\":\"Xue\",\"suffix\":\"\"},{\"id\":489625885,\"identity\":\"e70daecf-9684-40f0-af1d-b78260d3ae44\",\"order_by\":1,\"name\":\"Guodong 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Aiming at the complexity of aero-engine condition monitoring data, an aero-engine predictive maintenance planning framework based on RUL prediction is proposed, which aims to analyze the engine RUL and design predictive maintenance strategies. First, a deep learning integrated model (Trans-LSTM), including Transformer and Long Short Memory Network Model (LSTM), is proposed. Second, Bayesian optimization is used to optimize the hyperparameters of the integrated model to further improve the accuracy of the predictive model. Based on the prediction data, an engine alarm threshold was designed. When the threshold is triggered during engine operation, a predictive maintenance task is applied. The optimal alarm threshold under the Trans-LSTM model is calculated by comparing the total flight cost and other indicators under different flight hours. Experimental results demonstrate that the data-driven predictive maintenance strategy can monitor engine status in real time, promptly identify potential failure risks, and prevent engines from operating in an unknown state. 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