In the context of detecting changes in structural systems, several vibration-based damage detection methods have been proposed and successfully applied to both mechanical and civil structures over the past years. These methods involve computing data-based features, which are then evaluated in statistical tests to detect damages. While being sensitive to damages, the data-based features are affected by changes in the ambient excitation properties that potentially lead to false alarms in the statistical tests, a characteristic that renders their use impractical for structural monitoring. In this paper, a damage detection method is presented that is robust to changes in the covariance of the ambient excitation. The proposed approach is based on the Mahalanobis distance of output covariance Hankel matrices, which are normalized with respect to possibly changing excitation properties. The statistical properties of the developed damage feature are reported and used for efficient hypothesis testing. Its robustness towards changes in the excitation covariance is illustrated on numerical simulations and successfully tested on a numerical offshore foundation model.

中文翻译：

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基于子空间的 Mahalanobis 损伤检测对激励协方差变化具有鲁棒性

在检测结构系统变化的背景下，已经提出了几种基于振动的损伤检测方法，并在过去几年中成功应用于机械和土木结构。这些方法涉及计算基于数据的特征，然后在统计测试中评估这些特征以检测损坏。虽然对损坏很敏感，但基于数据的特征会受到环境激励特性变化的影响，这可能会导致统计测试中的误报，这一特性使其无法用于结构监测。在本文中，提出了一种对环境激励协方差变化具有鲁棒性的损伤检测方法。所提出的方法基于输出协方差 Hankel 矩阵的马氏距离，这些是关于可能改变的激发特性归一化的。报告开发的损坏特征的统计特性并用于有效的假设检验。它对激励协方差变化的鲁棒性在数值模拟中得到了说明，并在数值海上基础模型上进行了成功测试。