Politecnico di Milano, Department of Mechanical Engineering, Via La Masa 1, 20156 Milano, Italy.
Sensors (Basel). 2022 Mar 10;22(6):2177. doi: 10.3390/s22062177.
Due to the need for controlling many ageing and complex structures, structural health monitoring (SHM) has become increasingly common over the past few decades. However, one of the main limitations for the implementation of continuous monitoring systems in real-world structures is the effect that benign influences, such as environmental and operational variations (EOVs), have on damage sensitive features. These fluctuations may mask malign changes caused by structural damages, resulting in false structural condition assessment. When damage identification is implemented as novelty detection due to the lack of known damage states, outliers may be part of the data set as the result of the benign and malign factors mentioned above. Thanks to the developments in the field of robust outlier detection, the current paper presents a new data fusion method based on the use of cointegration and minimum covariance determinant estimator (MCD), which allows us to visualize and to classify outliers in SHM data, depending on their origin. To validate the effectiveness of this technique, the recent case study of the KW51 bridge has been considered, whose natural frequencies are subjected to variations due to both EOVs and a real structural change.
由于需要控制许多老化和复杂的结构，结构健康监测（SHM）在过去几十年中变得越来越普遍。然而，在实际结构中实施连续监测系统的主要限制之一是环境和操作变化（EOVs）等良性影响对损伤敏感特征的影响。这些波动可能会掩盖结构损伤引起的恶性变化，导致虚假的结构状况评估。由于缺乏已知的损伤状态，当由于损伤识别作为新颖性检测实施时，异常值可能是数据集的一部分，这是由于上述良性和恶性因素造成的。由于在稳健异常值检测领域的发展，本文提出了一种新的数据融合方法，该方法基于使用协整和最小协方差行列式估计器（MCD），允许我们根据异常值的来源对 SHM 数据中的异常值进行可视化和分类。为了验证该技术的有效性，考虑了 KW51 桥的最近案例研究，其固有频率由于 EOVs 和真实结构变化而发生变化。
