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

Journal: Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
Published Date: Oct 16, 2020

Abstract

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'.

Authors

  • P Gardner
    Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.
  • R Fuentes
    Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.
  • N Dervilis
    Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.
  • C Mineo
    Centre for Ultrasonic Engineering, University of Strathclyde, 204 George Street, Glasgow G1 5PJ, UK.
  • S G Pierce
    Centre for Ultrasonic Engineering, University of Strathclyde, 204 George Street, Glasgow G1 5PJ, UK.
  • E J Cross
    Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.
  • K Worden
    Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK.

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