Enhancing aircraft safety through advanced engine health monitoring with long short-term memory

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dc.contributor.authorYildirim, Suleyman
dc.contributor.authorRana, Zeeshan A.
dc.date.accessioned2024-02-10T16:54:08Z
dc.date.available2024-02-10T16:54:08Z
dc.date.issued2024-01-14
dc.description.abstractPredictive maintenance holds a crucial role in various industries such as the automotive, aviation and factory automation industries when it comes to expensive engine upkeep. Predicting engine maintenance intervals is vital for devising effective business management strategies, enhancing occupational safety and optimising efficiency. To achieve predictive maintenance, engine sensor data are harnessed to assess the wear and tear of engines. In this research, a Long Short-Term Memory (LSTM) architecture was employed to forecast the remaining lifespan of aircraft engines. The LSTM model was evaluated using the NASA Turbofan Engine Corruption Simulation dataset and its performance was benchmarked against alternative methodologies. The results of these applications demonstrated exceptional outcomes, with the LSTM model achieving the highest classification accuracy at 98.916% and the lowest mean average absolute error at 1.284%.en_UK
dc.identifier.citationYildirim S, Rana ZA. (2024) Enhancing aircraft safety through advanced engine health monitoring with long short-term memory. Sensors, Volume 21, Issue 2, January 2024, Article number 518en_UK
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/s24020518
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20771
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectremaining useful lifeen_UK
dc.subjectpredictive maintenanceen_UK
dc.subjectaircraft health monitoringen_UK
dc.titleEnhancing aircraft safety through advanced engine health monitoring with long short-term memoryen_UK
dc.typeArticleen_UK
dcterms.dateAccepted2024-01-11

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