基于相关性方差贡献率的高坝泄洪振动数据级融合方法

A data level fusion method for high dam flood discharge vibration based on correlation variance contribution rate

针对单一传感器信号只能反映结构的局部特性及易受干扰的问题,提出了基于相关性方差贡献率的数据级融合方法;通过计算振动信号的相关性方差贡献率来分配融合系数,从而实现多传感器信号之间的数据融合。锦屏一级高拱坝原型观测信号和自制的碾压混凝土坝水弹性模型实测信号的数据融合结果表明,该方法具有良好的抗噪性,可挖掘振动信号中的高频微弱信息和密频信息。与基于相关函数的数据级融合方法相比,基于相关性方差贡献率的数据级融合方法使融合后的信号能更加全面地反映坝体整体振动特性,更适用于高拱坝及碾压混凝土坝振动信号的模态参数识别。

Aiming at the problem that a single sensor signal can only reflect the local characteristics of a structure and is susceptible to interference,a data level fusion method based on correlation variance contribution rate is proposed.Data fusion can be realized through allocating the fusion coefficients by calculating the variance contribution rate of the signals from multi-sensors.The data fusion results of the observed prototype signal of Jinping I high arch dam and the measured signal of a self-made RCC dam hydroelastic model show that the proposed method has good noise resistance,and it can mine high-frequency weak information and dense frequencies in vibration signals.Compared with the method based on correlation function,this method make the fused signal can fully reflect the overall vibration characteristics of the dam body,and it is suitable for modal parameter identification of the vibration signals from high arch dams and roller compacted concrete dams.