Machine learning requirements for the airworthiness of structural health monitoring systems in aircraft

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dc.contributor.authorEl Mir, Haroun
dc.contributor.authorKing, Stephen
dc.contributor.authorSkote, Martin
dc.contributor.authorPerinpanayagam, Suresh
dc.date.accessioned2023-08-17T10:27:03Z
dc.date.available2023-08-17T10:27:03Z
dc.date.issued2023-06-30
dc.description.abstractIn the evolving realm of airworthiness and aircraft maintenance task scheduling, the introduction of data-driven Predictive Maintenance (PdM) and Structural Health Monitoring (SHM) has prompted a paradigm shift, which underscores the profound implications of innovative sensing techniques within damage and operational monitoring. Concurrently, the role of avionics in data acquisition and processing has drawn renewed focus, with machine learning (ML) algorithms facilitating pattern recognition, trend analysis, and anomaly detection. This paper discusses the diagnostic sequence in SHM systems, the necessity for damage information, and delves into active and passive sensing techniques within damage and operational monitoring. The role of avionics is also emphasized, especially in data acquisition and processing for operational monitoring. The utilization of ML algorithms for efficient use within SHM is explored, alongside supervised and unsupervised learning methods. The paper underlines how integrating ML in aircraft systems applications can optimize maintenance schedules and lay a solid foundation for SHM integration in aircraft health systems. The study also covers the application of ML techniques for detection, localization, and assessment of structural damage. It reviews research implementations using ML, statistical, and hybrid approaches in monitoring and predicting aircraft damage. The incorporation of non- exclusive ML in SHM to minimize environmental feature uncertainty and enable trackable model behaviour is illustrated. Lastly, the paper discusses evolving regulatory requirements and standards for ML application in aviation SHM, provided by authorities and workgroups like EASA and the SAE G-34 AI in Aviation Committee, respectively, and concludes with an overview of the future trends and standards in this dynamic domain. The aim is to spotlight the transformative potential of PdM and SHM, and their critical roles in boosting the operational efficiency of the aviation industry.en_UK
dc.identifier.citationEl Mir H, King S, Skote M, Perinanayagam S. (2023) Machine learning requirements for the airworthiness of structural health monitoring systems in aircraft. In: 38th Conference and 31st Symposium of the International Committee on Aeronautical Fatigue and Structural Integrity (ICAF 2023), 26-29 June 2023, Delft, Germanyen_UK
dc.identifier.urihttps://www.icaf.aero/icaf2023/proceedings/documents/128.pdf
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20111
dc.language.isoenen_UK
dc.publisherICAFen_UK
dc.rightsAttribution 3.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/*
dc.subjectStructural Health Monitoringen_UK
dc.subjectPredictive maintenanceen_UK
dc.subjectdata-driven modelsen_UK
dc.subjectMachine Learning assuranceen_UK
dc.subjectavionics systemsen_UK
dc.titleMachine learning requirements for the airworthiness of structural health monitoring systems in aircraften_UK
dc.typeConference paperen_UK

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