Data-driven damage detection methods achieve damage identification by analyzing changes in damage-sensitive features (DSFs) derived from structural health monitoring (SHM) data. The core reason for their effectiveness lies in the fact that damage or structural state transition can be manifested as changes in the distribution of DSF data. This enables us to reframe the problem of damage detection as one of identifying these distributional changes. Hence, developing automated tools for detecting such changes is pivotal for automated structural health diagnosis. Control charts are extensively utilized in SHM for DSF change detection, owing to their excellent online detection and early warning capabilities. However, conventional methods are primarily designed to detect mean or variance shifts, making it challenging to identify complex shape changes in distributions. This limitation results in insufficient damage detection sensitivity. Moreover, they typically exhibit poor robustness against data contamination. This paper proposes a novel control chart to address these limitations. It employs the probability density functions (PDFs) of subgrouped DSF data as monitoring objects, with shape deformations characterized by warping functions. Furthermore, a nonparametric control chart is specifically constructed for warping function monitoring in the functional data analysis framework. Key advantages of the new method include the ability to detect both shifts and complex shape deformations in distributions, excellent online detection performance, and robustness against data contamination. Extensive simulation studies demonstrate its superiority over competing approaches. Finally, the method is applied to detecting distributional changes in DSF data for cable condition assessment in a long-span cable-stayed bridge, demonstrating its practical utility in engineering.

翻译：数据驱动的损伤检测方法通过分析从结构健康监测数据中提取的损伤敏感特征的变化来实现损伤识别。其有效性的核心原因在于，损伤或结构状态转变可以表现为DSF数据分布的变化。这使我们能够将损伤检测问题重新定义为识别这些分布变化的问题。因此，开发用于检测此类变化的自动化工具对于自动化结构健康诊断至关重要。控制图因其优异的在线检测和预警能力，在SHM中被广泛用于DSF变化检测。然而，传统方法主要设计用于检测均值或方差偏移，难以识别分布中复杂的形状变化。这一限制导致损伤检测灵敏度不足。此外，它们通常对数据污染表现出较差的鲁棒性。本文提出了一种新的控制图来解决这些局限性。它采用分组DSF数据的概率密度函数作为监测对象，其中形状变形通过扭曲函数来表征。此外，在函数数据分析框架下，专门构建了一种用于监测扭曲函数的非参数控制图。新方法的主要优势包括能够检测分布中的偏移和复杂形状变形、优异的在线检测性能以及对数据污染的鲁棒性。大量的仿真研究证明了其相对于竞争方法的优越性。最后，该方法被应用于大跨度斜拉桥缆索状态评估中DSF数据的分布变化检测，证明了其在工程中的实际效用。