Detecting damage in critical structures using monitored data is a fundamental task of structural health monitoring, which is extremely important for maintaining structures' safety and life-cycle management. Based on statistical pattern recognition paradigm, damage detection can be conducted by assessing changes in the 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 capable of controlling for false-positive indications. This study proposes a new distributional change-point detection method for damage detection to address these challenges. We embed the elements of a DSF distributional sequence into the Wasserstein space and construct a moving sum (MOSUM) multiple change-point detector based on Fr\'echet statistics and establish theoretical properties. Extensive simulation studies demonstrate the superiority of our proposed approach against other competitors to address 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 reveal interesting patterns from the detected changes, which provides valuable insights into cable system damage.

翻译：利用监测数据检测关键结构的损伤是结构健康监测的基本任务，对保障结构安全和使用寿命管理至关重要。基于统计模式识别范式，可通过评估恰当提取的损伤敏感特征（DSFs）的分布变化来实现损伤检测，这自然可被建模为分布变点检测问题。有效的损伤检测变点检测器需具备处理大规模DSF数据集、适应不同类型变化以及控制误报率的能力。本研究提出一种面向损伤检测的新型分布变点检测方法以应对这些挑战。我们将DSF分布序列元素嵌入Wasserstein空间，基于Fr\'echet统计量构建移动和（MOSUM）多变点检测器并建立其理论性质。大量模拟研究表明，所提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拉索张力监测数据以检测拉索损伤，对提取的DSF数据开展系统性的变点分析，从检测到的变化中揭示出有趣模式，为拉索系统损伤评估提供了重要见解。