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Machine learning at the interface of structural health monitoring and non-destructive evaluation

Gardner, P. ; Fuentes, R. ; Dervilis, N. ; Mineo, C. ; Pierce, S.G. ; Cross, E.J. ; Worden, K.

Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences, 2020-10, Vol.378 (2182), p.20190581-20190581 [Periódico revisado por pares]

The Royal Society Publishing

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  • Título:
    Machine learning at the interface of structural health monitoring and non-destructive evaluation
  • Autor: Gardner, P. ; Fuentes, R. ; Dervilis, N. ; Mineo, C. ; Pierce, S.G. ; Cross, E.J. ; Worden, K.
  • É parte de: Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences, 2020-10, Vol.378 (2182), p.20190581-20190581
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    One contribution of 15 to a theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.
  • Descrição: While both non-destructive evaluation (NDE) and structural health monitoring (SHM) share the objective of damage detection and identification in structures, they are distinct in many respects. This paper will discuss the differences and commonalities and consider ultrasonic/guided-wave inspection as a technology at the interface of the two methodologies. It will discuss how data-based/machine learning analysis provides a powerful approach to ultrasonic NDE/SHM in terms of the available algorithms, and more generally, how different techniques can accommodate the very substantial quantities of data that are provided by modern monitoring campaigns. Several machine learning methods will be illustrated using case studies of composite structure monitoring and will consider the challenges of high-dimensional feature data available from sensing technologies like autonomous robotic ultrasonic inspection. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.
  • Editor: The Royal Society Publishing
  • Idioma: Inglês
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