基于向量自回归模型的损伤识别方法
Vector Autoregression Time Series Based Damage Assessment Method

基于向量自回归（vector auto-regression,简称VAR）模型，提出了一种能同时进行损伤定位和程度识别的时间序列方法。首先，利用测试的加速度响应时程信号建立VAR模型，提取模型系数的对角线元素作为损伤敏感向量，并采用该向量的马氏距离作为损伤特征值；然后，应用统计模式识别手段，通过受试者工作特征曲线下的面积指标来判别损伤是否出现及其部位，并通过Bhattacharyya距离来度量损伤程度。数值模拟和实验室框架模型实验表明，该算法能成功识别损伤部位和损伤程度的相对大小，且具有较好的抗噪性能，为结构长期在线损伤识别提供了一种有效手段。
A damage identification method based on the vector auto-regression (VAR) time series is proposed. First, the VAR model is trained by the measured acceleration time-histories, and a new vector is extracted from the diagonal elements of the VAR coefficient matrices as a damage sensitive vector. The Mahalanobis distance of the new vector is defined as the damage feature. Then, the area under a receiver operating characteristic (ROC) curve is used to detect and localize the damage using statistics pattern recognition methodology, and the Bhattacharyya distance (BD) is implemented to quantify the magnitude of damage. Results from a simulated model and a lab-scale frame structure show that damage location and severity can be successfully identified and a reasonable amount of noise can be canceled.