Nondestructive testing and evaluation (NDT&E) are commonly used in the industry for their ability to identify damage and assess material conditions. Ultrasonic testing (UT) is one of the most popular NDT&E techniques. A variant of ultrasonic testing known as nonlinear ultrasonic testing (NUT) has some advantages over conventional (linear) UT as it is more sensitive to damages in their early stages; even at the microscopic levels. Furthermore, the nonlinear characteristics of ultrasonic waves can be correlated to several material properties. In the last two decades, the NUT method has been investigated from two aspects, namely the direct (modeling) problem and the inverse (NUT testing) problem. The direct problem aims to establish the nonlinear mechanism and analyze the behavior of wave-damage interaction. The inverse problem is investigated under three headings: (1) data acquisition with NUT techniques, (2) signal pre-processing and feature extraction, and (3) parameter analysis for damage characterization. The conventional data analytical methods extract nonlinear features from noisy signals and build a damage index to characterize damages. However, damage index-based analyzing model can be challenging, as other factors affect the overall system nonlinearity such as complex specimen geometry, different damage characteristics, varying ambient conditions, and measurement uncertainties. To overcome these shortcomings, machine learning (ML) methods appear promising for the analysis of complex nonlinear ultrasonic signals by exploiting data mining and pattern recognition capabilities. Therefore, this paper aims to provide a comprehensive review of the state-of-the-art ML-enriched NUT for damage characterization. Other NUT-based technologies are also reviewed, including modeling of wave-damage interaction, different NUT techniques for data acquisition, signal pre-processing methods, and damage index-based parameter analysis strategies for damage characterization. Major emphasis is placed on the application of ML methods for NDT&E applications. Additionally, future research trends on data augmentation, complex damage characterization, and baseline-free methods using NUT are also discussed.

无损检测和评估 (NDT&E) 因其识别损坏和评估材料状况的能力而常用于行业。超声波检测 (UT) 是最流行的无损检测技术之一。一种称为非线性超声波测试 (NUT) 的超声波测试变体与传统（线性）UT 相比具有一些优势，因为它在早期阶段对损坏更敏感；甚至在微观层面。此外，超声波的非线性特性可能与多种材料特性相关。近二十年来，NUT方法从两个方面进行了研究，即直接（建模）问题和逆（NUT测试）问题。直接问题旨在建立非线性机制并分析波损相互作用的行为。在三个标题下研究了逆问题：(1) 使用 NUT 技术获取数据，(2) 信号预处理和特征提取，以及 (3) 用于损伤表征的参数分析。传统的数据分析方法从噪声信号中提取非线性特征并建立损伤指数来表征损伤。然而，基于损伤指数的分析模型可能具有挑战性，因为其他因素会影响整个系统的非线性，例如复杂的试样几何形状、不同的损伤特征、变化的环境条件和测量不确定性。为了克服这些缺点，机器学习 (ML) 方法通过利用数据挖掘和模式识别功能来分析复杂的非线性超声信号，这似乎很有前景。所以，本文旨在全面回顾用于损伤表征的最先进的 ML-enriched NUT。还回顾了其他基于 NUT 的技术，包括波浪-损伤相互作用建模、用于数据采集的不同 NUT 技术、信号预处理方法和基于损伤指数的损伤表征参数分析策略。主要重点放在 ML 方法在 NDT&E 应用中的应用。此外，还讨论了使用 NUT 的数据增强、复杂损伤表征和无基线方法的未来研究趋势。和基于损伤指数的损伤表征参数分析策略。主要重点放在 ML 方法在 NDT&E 应用中的应用。此外，还讨论了使用 NUT 的数据增强、复杂损伤表征和无基线方法的未来研究趋势。和基于损伤指数的损伤表征参数分析策略。主要重点放在 ML 方法在 NDT&E 应用中的应用。此外，还讨论了使用 NUT 的数据增强、复杂损伤表征和无基线方法的未来研究趋势。