基于小波变换的结构模态参数识别

及时、准确地识别出结构的模态参数是结构健康监测与损伤识别的重要前提。小波分析是众多识别方法中较优越的一种，因其在时一频两域都具有表征信号局部特征的能力，近年来这一方法在线性及非线性系统的参数识别中开始应用。探讨了基于小波脊（Ridge）与小波骨架（Skeleton）的模态参数识别方法，针对小波变换中遇到的边端效应问题，提出了基于自回归滑动平均模型（ARMA）的“预测延拓”方法，并以美国土木工程师学会（ASCE）提供的Benchmark模型为例进行了数值模拟。结果表明，本文提出的方法可以有效地抑制小波边端效应，通过小渡变换可以准确地识别出结构的模态参数。

Structural modal parameter identification based on wavelet transform

It's important to identify structural modal parameter in time and accurately for structural health monitoring and damage identification.Wavelet analysis is one of the various kinds of identification methods,which has been used in linear and nonlinear system response data since it can decompose signals simultaneously both in time-domain and frequency-domain with adaptive windows.In this paper,the basic theories of continuous wavelet transform are firstly introduced.And then the structural modal parameter identification method based on ridge and skeleton extraction techniques is discussed,following that a novel signal extension method using auto regressive moving average(ARMA) prediction is presented,Finally a numerical simulation is carried out by use of the Benchmark model which was developed by American Society of Civil Engineers(ASCE).The results show that the method presented here can effectively correct end effects errors,and the wavelet transform can identify structural model parameter accurately.