Certification of machine learning algorithms for safe life assessment of landing gear

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dc.contributor.author El Mir, Haroun
dc.contributor.author Perinpanayagam, Suresh
dc.date.accessioned 2022-11-29T10:54:59Z
dc.date.available 2022-11-29T10:54:59Z
dc.date.issued 2022-11-15
dc.identifier.citation El Mir H & Perinpanayagam S (2022) Certification of machine learning algorithms for safe life assessment of landing gear, Frontiers in Astronomy and Space Sciences, Volume 9, November 2022, Article number 896877 en_UK
dc.identifier.issn 2296-987X
dc.identifier.uri https://doi.org/10.3389/fspas.2022.896877
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/18751
dc.description.abstract This paper provides information on current certification of landing gear available for use in the aerospace industry. Moving forward, machine learning is part of structural health monitoring, which is being used by the aircraft industry. The non-deterministic nature of deep learning algorithms is regarded as a hurdle for certification and verification for use in the highly-regulated aerospace industry. This paper brings forth its regulation requirements and the emergence of standardisation efforts. To be able to validate machine learning for safety critical applications such as landing gear, the safe-life fatigue assessment needs to be certified such that the remaining useful life may be accurately predicted and trusted. A coverage of future certification for the usage of machine learning in safety-critical aerospace systems is provided, taking into consideration both the risk management and explainability for different end user categories involved in the certification process. Additionally, provisional use case scenarios are demonstrated, in which risk assessments and uncertainties are incorporated for the implementation of a proposed certification approach targeting offline machine learning models and their explainable usage for predicting the remaining useful life of landing gear systems based on the safe-life method. en_UK
dc.language.iso en en_UK
dc.publisher Frontiers en_UK
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject explainable AI en_UK
dc.subject landing gear systems en_UK
dc.subject certification en_UK
dc.subject risk management en_UK
dc.subject safe-life design en_UK
dc.title Certification of machine learning algorithms for safe life assessment of landing gear en_UK
dc.type Article en_UK

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