Principal component analysis (PCA) methods have been widely applied to damage identification in the long-term structural health monitoring (SHM) of infrastructure. Usually, the first few eigenvector components derived by PCA methods are treated as damage-sensitive features. In this paper, the effective method of double-window PCA (DWPCA) and novel features are proposed for better damage identification performance. In the proposed method, spatial and temporal windows are introduced to the traditional PCA method. The spatial windows are applied to group damage-sensitive sensors and exclude those sensors insensitive to damage, while the temporal window is applied to better discriminate eigenvectors between the damaged and healthy states. In addition, the length and directional angle of the eigenvector variation between the healthy and damaged states are used as the damage-sensitive features, instead of the components of the eigenvector variation used in previous studies. Numerical simulations based on a large-scale bridge reveal that the proposed features are successful in identifying the damage located far from sensors due to the use of both spatial and temporal windows as well as the length of the eigenvector variation. In addition, compared to the previous PCA and moving PCA methods, the novel features have higher sensitivity and resolution in damage identification.

主成分分析（PCA）方法已广泛应用于基础设施长期结构健康监测（SHM）中的损伤识别。通常，由 PCA 方法导出的前几个特征向量分量被视为损伤敏感特征。在本文中，提出了有效的双窗口主成分分析（DWPCA）方法和新颖的特征，以获得更好的损伤识别性能。在所提出的方法中，空间和时间窗口被引入到传统的 PCA 方法中。空间窗口用于对损伤敏感的传感器进行分组并排除那些对损伤不敏感的传感器，而时间窗口用于更好地区分损伤和健康状态之间的特征向量。此外，健康和受损状态之间的特征向量变化的长度和方向角被用作损伤敏感特征，而不是先前研究中使用的特征向量变化的分量。基于大型桥梁的数值模拟表明，由于使用空间和时间窗口以及特征向量变化的长度，所提出的特征可以成功识别远离传感器的损坏。此外，与以往的 PCA 和移动 PCA 方法相比，新特征在损伤识别方面具有更高的灵敏度和分辨率。基于大型桥梁的数值模拟表明，由于使用空间和时间窗口以及特征向量变化的长度，所提出的特征可以成功识别远离传感器的损坏。此外，与以往的 PCA 和移动 PCA 方法相比，新特征在损伤识别方面具有更高的灵敏度和分辨率。基于大型桥梁的数值模拟表明，由于使用空间和时间窗口以及特征向量变化的长度，所提出的特征可以成功识别远离传感器的损坏。此外，与以往的 PCA 和移动 PCA 方法相比，新特征在损伤识别方面具有更高的灵敏度和分辨率。