Journal
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
Volume 378, Issue 2182, Pages -Publisher
ROYAL SOC
DOI: 10.1098/rsta.2019.0581
Keywords
ultrasound; structural health monitoring; non-destructive evaluation; machine learning; compressive sensing; transfer learning
Categories
Funding
- UK Engineering and Physical Sciences Research Council [EP/N018427/1, EP/R006768/1, EP/R003645/1, EP/S001565/1, EP/R004900/1]
- EPSRC [EP/S001565/1, EP/N018427/1, EP/R003645/1, EP/R006768/1] Funding Source: UKRI
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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'.
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