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’.

中文翻译：

---

结构健康监测和无损评估界面的机器学习

虽然无损评估 (NDE) 和结构健康监测 (SHM) 都具有结构损伤检测和识别的目标，但它们在许多方面是不同的。本文将讨论差异和共性，并将超声波/导波检测视为两种方法的接口技术。它将讨论基于数据/机器学习分析如何在可用算法方面为超声波 NDE/SHM 提供强大的方法，更一般地说，不同的技术如何适应现代监测活动提供的大量数据。将使用复合结构监测的案例研究来说明几种机器学习方法，并将考虑来自自主机器人超声波检测等传感技术的高维特征数据的挑战。本文是主题问题“高级电磁无损评估与智能监测”的一部分。