【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.更多还原

【Abstract】 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 principal-component matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobis-distance 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.更多还原