Certification approach for physics informed machine learning and its application in landing gear life assessment

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dc.contributor.authorEl Mir, Haroun
dc.contributor.authorPerinpanayagam, Suresh
dc.date.accessioned2022-01-26T15:11:47Z
dc.date.available2022-01-26T15:11:47Z
dc.date.issued2021-11-15
dc.description.abstractThe efficacy of fatigue life approximation methodologies for Landing Gear systems is studied and compared to the ongoing Structural Health Monitoring techniques being researched, which will forecast failures based on the system’s specific life and withstanding abilities, ranging from creating a digital simulation model to applying neural network technologies, in order to simulate and approximate locations and levels of failure along the structure. Explainable Artificial Intelligence allows for the ease-of-integration of Deep Neural Network data into Predictive Maintenance, which is a procedure focused on the health of a system and its efficient upkeep via the use of sensor-based data. Test data from a flight includes a multitude of conditions and varying parameters such as the surface of the landing strip as well as the aircraft itself, requiring the use of Deep Neural Network models for damage assessment and failure anticipation, where compliance to standards is a major question raised, as the EASA AI roadmap is followed, as well as the ICAO and FAA. This paper additionally discusses the challenges faced with respect to standardizing the Explainable AI methodologies and their parameters specifically for the case of Landing Gear.en_UK
dc.identifier.citationEl Mir H, Perinpanayagam S. (2021) Certification approach for physics informed machine learning and its application in landing gear life assessment. In: 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), 3-7 October 2021, San Antonio, USAen_UK
dc.identifier.eisbn978-1-6654-3420-1
dc.identifier.isbn978-1-6654-3421-8
dc.identifier.issn2155-7209
dc.identifier.urihttps://doi.org/10.1109/DASC52595.2021.9594374
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17502
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectExplainable AIen_UK
dc.subjectLanding Gear Systemsen_UK
dc.subjectdigital simulation modelen_UK
dc.titleCertification approach for physics informed machine learning and its application in landing gear life assessmenten_UK
dc.typeConference paperen_UK

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