基于HHT的结构模态参数自动化识别方法和试验验证

AN AUTOMATIC STRUCTURAL MODAL PARAMETERS IDENTIFICATION METHOD BASED ON HHT AND ITS EXPERIMENTAL VERIFICATION

建筑结构的模态参数识别是健康监测系统中的核心算法。模态参数识别经过多年的发展已经非常成熟,种类繁多。但是基于Hilbert-Huang变换(Hilbert-Huang transform,HHT)的结构模态参数识别中多个步骤均需要研究人员对参数进行主观判断与筛选,不能直接用于长期的结构健康自动监测。该文提出了一种基于HHT的结构模态自动识别方法,利用深度神经网络(Deep neural network,DNN)结合K-L散度实现了EMD(Empirical mode decomposition)虚假分量的识别与剔除,利用奇异谱分析(Singular spectrum analysis,SSA)结合Butterworth滤波器对EMD产生的模态混叠现象进行分离,对只包含单一模态信息的固有模态函数(Intrinsic mode function,IMF)进行Hilbert变换后通过最小二乘法拟合实现模态参数识别。将上述方法应用于一3层混凝土结构振动台试验的监测数据分析,结果表明:该方法可以在不依赖研究人员的主观参数选择前提下,有效实现结构模态参数的自动化识别。

Structural modal parameter identification is a core algorithm in structural health monitoring system.Modal parameter identification methods have been very mature after years of development,and there are many types.The modal parameter identification method based on Hilbert-Huang Transform require researchers to subjectively select parameters in multiple steps,and cannot be directly used for long-term automatic structural monitoring.An automatic structural modal parameter identification method based on HHT is proposed.K-L divergence and DNN are used to identify and eliminate the false components generated by EMD in the first step of HHT.SSA and Butterworth filter are used to separate the aliasing modes of IMF.Hilbert transform is applied to IMF that only contains single mode information and least squares fitting is used to realize the modal parameter identification.The method is applied to a shaking table test of a three-story concrete structure,and the results show that the method can effectively and automatically identify the structural modal parameters without subjective parameter selection process.