Structural Health Monitoring (SHM) is increasingly applied in civil engineering. One of its primary purposes is detecting and assessing changes in structure conditions to increase safety and reduce potential maintenance downtime. Recent advancements, especially in sensor technology, facilitate data measurements, collection, and process automation, leading to large data streams. We propose a function-on-function regression framework for (nonlinear) modeling the sensor data and adjusting for covariate-induced variation. Our approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis and uses the corresponding out-of-sample Phase-II scores for monitoring. The method proposed can also be described as a combination of an ``input-output'' and an ``output-only'' method.

翻译：结构健康监测（SHM）在土木工程中的应用日益广泛。其主要目的之一是检测和评估结构状况的变化，以提高安全性并减少潜在的维护停机时间。传感器技术等领域的近期进展促进了数据测量、收集与处理自动化，从而产生了大规模数据流。我们提出了一种函数对函数回归框架，用于（非线性）建模传感器数据并调整协变量引起的变异。该方法特别适用于拥有数月或数年训练数据时的长期监测场景。它将高度灵活且可解释的半参数建模与功能主成分分析相结合，并利用相应的样本外第二阶段评分进行监测。所提出的方法亦可描述为“输入-输出”方法与“仅输出”方法的结合体。