摘要
基于振动的结构健康监测的前提是从振动测试信号中提取结构模态参数。随机子空间方法是近年来发展起来的一种线性系统辨识方法,可以有效地从环境激励的结构响应信号中提取结构模态参数。随机子空间识别方法的应用前提是输入满足白噪声的假定,输出信号应当是平稳信号。论文对随机子空间方法的使用前提进行了拓展。将非平稳信号划分为分段平稳随机信号进行处理,为非平稳信号的研究提供一种新的分析方法。基本思想是将在现场采集的结构输出信号进行分段,各段信号应满足稳定的条件,即分段平稳。将各段信号用随机子空间结合稳定图进行识别,然后将所有各段所识别的模态参数再一次用稳定图方法进行分析,得出结构的模态参数。最后用一3跨连续梁的数值模型进行验证,结果表明用随机子空间方法结合两次稳定图可以有效地识别分段平稳的随机信号。
The premise of stochastic subspace identification method is that the input must be white noise,and the output must be stationary stochastic signal.The paper loosens this premise.A new way to analyse nonstationary stochastic process using stochastic subspace identification is to divide the output signal of structure into several parts,and each part must satisfy stationary condition,that is,the signal is supposed to be piecewise stationary.The stochastic subspace identification and stabilization diagram are used for each part of output signal.Then the identified results are analyzed by stabilization diagram again to gain the real modal parameters of the structure.The method is evaluated by numerical simulation on a three-span continuous beam and ideal results are obtained.
出处
《振动与冲击》
EI
CSCD
北大核心
2007年第6期17-20,共4页
Journal of Vibration and Shock
基金
973项目(编号:2002CB412709)
关键词
分段平稳随机信号
参数识别
随机子空间
稳定图
piecewise stationary stochastic signal,parameter identification,stochastic subspace identification,stabilization diagram
