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'.

Highlights

  • At first sight, the current paper may seem like rather an outlier in a special issue on Advanced Electromagnetic NonDestructive Evaluation and Smart Monitoring; this is not the case

  • The objective function is formed from a robust novelty index, as this describes the dissimilarity of a given data point against the group, whilst ensuring the measure is not biased by noise or the presence of damage in the group

  • These results demonstrate that the inferred mapping is extremely beneficial in transferring label information from the source to target panel and that transfer learning is useful in progressing to a fully autonomous non-destructive evaluation (NDE) process

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Summary

Introduction

The current paper may seem like rather an outlier in a special issue on Advanced Electromagnetic NonDestructive Evaluation and Smart Monitoring; this is not the case. The intention here is to focus on matters of ‘smart monitoring’ with a particular emphasis on the power and efficacy of machine learning in that context. A number of points will be made regarding the distinctions between non-destructive evaluation (NDE). The discussion will be in the context of ultrasonic inspection methods, the authors believe that it will be of interest and c. By/4.0/, which permits unrestricted use, provided the original author and source are credited

Ultrasound
Compressive Sensing and Ultrasonic NDE
Machine Learning-Based Autonomous Inspection
Towards Fully Autonomous Ultrasonic NDE – the Potential of Transfer Learning
Findings
Method

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