In this article, the autoregressive time series analysis is used to extract reliable features from vibration measurements of civil structures for damage diagnosis. To guarantee the adequacy and applicability of the time series model, Leybourne–McCabe hypothesis test is used. Subsequently, the probability density functions of the autoregressive model parameters and residuals are obtained with the aid of a kernel density estimator. The probability density function sets are considered as damage-sensitive features of the structure and fast distance correlation method is used to make decision for detecting damages in the structure. Experimental data of a well-known three-story laboratory frame and a large-scale bridge benchmark structure are used to verify the efficiency and accuracy of the proposed method. Results indicate the capability of the method to identify the location and severity of damages, even under the simulated operational and environmental variability.

在本文中，自回归时间序列分析用于从民用建筑的振动测量结果中提取可靠的特征，以进行损伤诊断。为了保证时间序列模型的充分性和适用性，使用了Leybourne–McCabe假设检验。随后，借助于核密度估计器获得自回归模型参数和残差的概率密度函数。概率密度函数集被视为结构的损伤敏感特征，并且使用快速距离相关方法来确定检测结构中的损伤的决策。利用著名的三层实验室框架和大型桥梁基准结构的实验数据来验证该方法的效率和准确性。