Reliable and efficient damage detection is critical for the use of lightweight materials in the mechanical and aerospace industries. Within the context of Non-Destructive Testing (NDT), vibration-based tests have been applied for many decades to inspect components without damaging or debilitating their use. For posterior fault recognition, Artificial Intelligence techniques have achieved high success for a number of structural applications. In this work Testing, Simulation and Artificial Intelligence have been combined in order to develop a defect detection procedure. The use of an Optomet Scanning Laser Doppler Vibrometer (SLDV) for such tests provides an interesting solution to measure the vibration velocities on the structure surface. The algorithm for identifying the defects is based on the Local Defect Resonance (LDR) concept, which looks to the high frequency vibrations to get a localized resonant activation of the defect. Artificial Intelligence (AI) techniques were implemented with the aim of creating an automatic procedure based on features extraction for damage detection. Wavelet transformation and modal analysis were used to provide inputs to the AI techniques. In order to better understand the limitation in terms of defect detection, damaged plates were modelled and simulated in order to perform a sensitivity analysis. Finally, an overall comparative overview of different algorithms results was also obtained.

中文翻译：

---

使用人工智能技术在轻型结构中进行损伤检测

可靠且高效的损坏检测对于在机械和航空航天工业中使用轻质材料至关重要。在无损检测 (NDT) 的背景下，基于振动的测试已经应用了数十年，以检查组件而不会损坏或削弱其使用。对于后断层识别，人工智能技术在许多结构应用中取得了很高的成功。在这项工作中，测试、模拟和人工智能相结合，以开发缺陷检测程序。使用 Optomet 扫描激光多普勒测振仪 (SLDV) 进行此类测试提供了一种有趣的解决方案来测量结构表面上的振动速度。识别缺陷的算法基于局部缺陷共振 (LDR) 概念，它通过高频振动获得缺陷的局部共振激活。实施人工智能 (AI) 技术的目的是创建基于特征提取的自动程序以进行损坏检测。小波变换和模态分析用于为人工智能技术提供输入。为了更好地理解缺陷检测方面的局限性，对损坏的板进行建模和模拟，以进行灵敏度分析。最后，还获得了不同算法结果的总体比较概述。小波变换和模态分析用于为人工智能技术提供输入。为了更好地理解缺陷检测方面的局限性，对损坏的板进行建模和模拟，以进行灵敏度分析。最后，还获得了不同算法结果的总体比较概述。小波变换和模态分析用于为人工智能技术提供输入。为了更好地理解缺陷检测方面的局限性，对损坏的板进行建模和模拟，以进行灵敏度分析。最后，还获得了不同算法结果的总体比较概述。