Detecting damage in important structures using monitored data is a fundamental task of structural health monitoring, which is very important for the structures' safety and life-cycle management. Based on the statistical pattern recognition paradigm, damage detection can be achieved by detecting changes in distribution of properly extracted damage-sensitive features (DSFs). This can be naturally formulated as a distributional change-point detection problem. A good change-point detector for damage detection should be scalable to large DSF datasets, applicable to different types of changes and able to control the false-positive indication rate. To address these challenges, we propose a new distributional change-point detection method for damage detection. We embed the elements of a DSF distributional sequence into the Wasserstein space and develop a MOSUM-type multiple change-point detector based on Fr\'echet statistics. Theoretical properties are also established. Extensive simulation studies demonstrate the superiority of our proposal against other competitors in addressing the aforementioned practical requirements. We apply our method to the cable-tension measurements monitored from a long-span cable-stayed bridge for cable damage detection. We conduct a comprehensive change-point analysis for the extracted DSF data, and find some interesting patterns from the detected changes, which provides important insights into the damage of the cable system.

翻译：利用监测数据检测重要结构损伤是结构健康监测的基本任务，对结构安全与全生命周期管理至关重要。基于统计模式识别范式，可通过检测合理提取的损伤敏感特征（DSF）的分布变化来实现损伤检测。这自然可归结为分布变点检测问题。用于损伤检测的优良变点检测器应具备可扩展至大规模DSF数据集的能力、适用于多种变化类型并具备虚警率控制能力。针对这些挑战，我们提出了一种新的分布变点检测方法。我们将DSF分布序列元素嵌入Wasserstein空间，基于Fr\'echet统计量开发了MOSUM型多变点检测器，并建立了理论性质。大量仿真研究表明，该方法在满足上述实际需求方面优于其他竞争方法。我们将该方法应用于某大跨度斜拉桥的缆索张力监测数据以实现缆索损伤检测，对提取的DSF数据进行了全面变点分析，从检测到的变化中发现了若干有趣模式，为缆索系统损伤研究提供了重要洞见。