基于延时随机子空间方法的非白噪声环境激励结构模态参数识别

Modal parameter identification of structures under non-white noise ambient excitations using delay-index-based stochastic subspace method

为了消除非白噪声环境激励在结构模态参数识别结果中产生的虚假模态,引入扩展状态模型,从状态空间方程的角度论证了虚假模态产生的原因;然后,针对一类具有典型性和代表性的(自相关函数在纵坐标轴附近一定范围内有非零值的)非白噪声环境激励信号,在传统随机子空间算法的基础上引入延时指标,建立延时随机子空间方法。数值算例表明延时随机子空间方法能够有效地剔除非白噪声环境激励在模态参数识别结果中产生的虚假模态,放宽了传统模态参数识别方法对环境输入的白噪声假设。

In order to eliminate spurious modes caused by non-white noise ambient inputs,an augmented state space model was introduced to explain how the spurious modes arise in the modal parameter identification of structures. For a kind of typical non-white ambient excitations,whose autocorrelation function values are nonzeroes near the vertical axis,the delay-index-based stochastic subspace method was proposed by introducing a delay index in the traditional stochastic subspace method.Numerical examples showed that this improved method can eliminate spurious modes due to non-white noise inputs the white noise assumption of ambient excitations in the traditional modal analysis methods.