桥梁健康监测中损伤特征提取的小波包方法

针对桥梁健康监测中结构损伤识别的特点,从模式识别的角度提出和分析了损伤特征提取问题.阐述了基于小波包分析的两种节点能量特征提取的方法.为了研究小波包系数节点能量和小波包信号成分节点能量对损伤信息进行特征提取的差异,通过对随机荷载激励下的连续梁进行数值模拟,得到了结构未损和损伤状态下的加速度时程信号.应用小波包变换,对不同结构状态下的加速度信号分别提取了两种小波包节点能量特征,并对它们作为结构损伤特征指标的敏感性进行了比较.认为两种特征指标的敏感性相差很小,而小波包系数节点能量特征指标的计算效率更高,更适合桥梁健康监测中损伤特征提取的要求.

Wavelet packet method of damage feature extraction in bridge health monitoring

For structural damage identification in bridge health monitoring,feature extraction of damage pattern classification was proposed and analyzed,and two feature exaction methods based on wavelet packet(WP) node energy were presented.To study the difference of WP coefficient node energy and WP signal component node energy,a numerical simulation of continuous beam impacted by random loads was conducted and acceleration signals of different structural condition were obtained.WP was applied to extract two kinds of node energy feature from the acceleration signals.Comparison of the two kinds of feature index showed that there is little difference in their sensitivity.Due to the very good computation efficiency of the WP coefficient,it was concluded that feature index of the WP coefficient node energy can satisfactorily meet the needs of bridge health monitoring.