Advancing fault diagnosis in aircraft landing gear: an innovative two-tier machine learning approach with intelligent sensor data management

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dc.contributor.authorKadripathi, K. N.
dc.contributor.authorIgnatyev, Dmitry
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorPerrusquía, Adolfo
dc.date.accessioned2024-02-02T11:48:47Z
dc.date.available2024-02-02T11:48:47Z
dc.date.issued2024-01-04
dc.description.abstractRevolutionizing aircraft safety, this study unveils a pioneering two-tier machine learning model specifically designed for advanced fault diagnosis in aircraft landing gear systems. Addressing the critical gap in traditional diagnostic methods, our approach deftly navigates the challenges of sensor data anomalies, ensuring robust and accurate real-time health assessments. This innovation not only promises to enhance the reliability and safety of aviation but also sets a new benchmark in the application of intelligent machine-learning solutions in high-stakes environments. Our method is adept at identifying and compensating for data anomalies caused by faulty or uncalibrated sensors, ensuring uninterrupted health assessment. The model employs a simulation-based dataset reflecting complex hydraulic failures to train robust machine learning classifiers for fault detection. The primary tier focuses on fault classification, whereas the secondary tier corrects sensor data irregularities, leveraging redundant sensor inputs to bolster diagnostic precision. Such integration markedly improves classification accuracy, with empirical evidence showing an increase from 95.88% to 98.76% post-imputation. Our findings also underscore the importance of specific sensors—particularly temperature and pump speed—in evaluating the health of landing gear, advocating for their prioritized usage in monitoring systems. This approach promises to revolutionize maintenance protocols, reduce operational costs, and significantly enhance the safety measures within the aviation industry, promoting a more resilient and data-informed safety infrastructure.en_UK
dc.identifier.citationKadripathi KN, Ignatyev D, Tsourdos A, Perrusquia A. (2024) Advancing fault diagnosis in aircraft landing gear: an innovative two-tier machine learning approach with intelligent sensor data management. In: AIAA SCITECH 2024 Forum, 8-12 January 2024, Orlando, USA. Paper number AIAA 2024-0759en_UK
dc.identifier.urihttps://doi.org/10.2514/6.2024-0759
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20737
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectFault Diagnosisen_UK
dc.subjectAircraft Landing Gear Systemsen_UK
dc.subjectMachine Learningen_UK
dc.subjectSensor Data Imputationen_UK
dc.subjectHydraulic Failure Simulationen_UK
dc.subjectSafety Enhancement in Aviationen_UK
dc.subjectReal-time Health Assessmenten_UK
dc.subjectDiagnostic Accuracy Improvementen_UK
dc.titleAdvancing fault diagnosis in aircraft landing gear: an innovative two-tier machine learning approach with intelligent sensor data managementen_UK
dc.typeConference paperen_UK

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