Damage detection is important in maintaining the integrity and safety of structures. The vibration-based Structural Health Monitoring (SHM) methods have been explored and applied extensively by researchers due to its non-destructive manner. The damage sensitivity of features used can significantly affect the accuracy of the vibration-based damage identification methods. The Frequency Response Function (FRF) was used as a damage sensitive feature in several works due to its rich yet compact representation of dynamic properties of a structure. However, utilizing the full size of FRFs in damage assessment requires high processing and computational time. A novel reduction technique using Principal Component Analysis (PCA) and peak detection on raw FRFs is proposed to extract the main damage sensitive feature while maintaining the dynamic characteristics. A rectangular Perspex plate with ground supports, simulating an automobile, was used for damage assessment. The damage sensitivity of the extracted feature, i.e. PCA-FRF is then evaluated using unsupervised [Formula: see text]-means clustering results. The proposed method is found to exaggerate the shift of damaged data from undamaged data and improve the repeatability of the PCA-FRF. The PCA-FRF feature is shown to have higher damage sensitivity compared to the raw FRFs, in which it yielded well-clustered results even for low damage conditions.

中文翻译：

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无监督机器学习中的损伤敏感 PCA-FRF 特征用于板状结构的损伤检测

损坏检测对于维护结构的完整性和安全性很重要。基于振动的结构健康监测（SHM）方法由于其非破坏性的方式已被研究人员广泛探索和应用。所用特征的损伤敏感性会显着影响基于振动的损伤识别方法的准确性。频率响应函数 (FRF) 在几部作品中被用作损伤敏感特征，因为它可以丰富而紧凑地表示结构的动态特性。然而，在损伤评估中使用完整大小的 FRF 需要大量的处理和计算时间。提出了一种在原始 FRF 上使用主成分分析 (PCA) 和峰值检测的新型减少技术，以在保持动态特性的同时提取主要损伤敏感特征。使用模拟汽车的带有地面支撑的矩形有机玻璃板进行损坏评估。然后使用无监督[公式：见正文]-均值聚类结果评估提取特征的损伤敏感性，即 PCA-FRF。发现所提出的方法夸大了损坏数据与未损坏数据的偏移，并提高了 PCA-FRF 的可重复性。与原始 FRF 相比，PCA-FRF 特征具有更高的损伤敏感性，即使在低损伤条件下也能产生良好的聚类结果。用于损害评估。然后使用无监督[公式：见正文]-均值聚类结果评估提取特征的损伤敏感性，即 PCA-FRF。发现所提出的方法夸大了损坏数据与未损坏数据的偏移，并提高了 PCA-FRF 的可重复性。与原始 FRF 相比，PCA-FRF 特征具有更高的损伤敏感性，即使在低损伤条件下也能产生良好的聚类结果。用于损害评估。然后使用无监督[公式：见正文]-均值聚类结果评估提取特征的损伤敏感性，即 PCA-FRF。发现所提出的方法夸大了损坏数据与未损坏数据的偏移，并提高了 PCA-FRF 的可重复性。与原始 FRF 相比，PCA-FRF 特征具有更高的损伤敏感性，即使在低损伤条件下也能产生良好的聚类结果。