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中文摘要：
针对结构健康监测中如何基于在线监测数据实现损伤诊断的问题，提出了一种利用时间序列分析ARMA模型的特征提取和损伤预警方法．首先对所有监测数据样本建立ARMA模型，以模型中AR部分参数的主成分矩阵构建Mahalanobis距离判别函数，提出了一种新的结构损伤敏感指标DDSF，然后，采用t-检验考察该指标在损伤前后是否存在显著性变化，从而可以有效地实现结构损伤预警。三跨连续梁数值算例表明，提出的结构损伤特征指标对结构的微小损伤具有敏感性，具备结构在线实时损伤预警的应用价值．
英文摘要：
Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis is presented. The monitoring data were first modeled as ARMA models, while a principalcomponent matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobisdistance criterion functions. Then, a new damage-sensitive feature index DDSF is proposed. A hypothesis test involving the t-test method is further applied to obtain a decision of damage alarming as the mean value of DDSF had significantly changed after damage. The numerical results of a three-span-girder model shows that the defined index is sensitive to subtle structural damage, and the proposed algorithm can be applied to the on-line damage alarming in SHM.
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