环境激励下结构模态辨识的识别概率直方图方法研究

A study on identification-probability histogram method for modal identification of structures subjected to ambient excitations

在环境激励下辨识结构模态时,系统阶次作为关键计算参数不易准确判定,通常采用基于假设系统阶次的稳定图方法来辅助进行,但其中稳定轴的判定较为主观,容易遗漏真实模态及引入虚假模态。基于数据挖掘技术,提出识别概率直方图(Identification-probability Histogram,IpHist)的新方法,对不同假设系统阶次下通过随机子空间识别得到的多组备选模态,根据频率容差和模态置信度容差准则进行一致性聚类,继而计算群组聚类结果的识别概率,并绘制相应的识别概率直方图,最后选取识别概率大的结果作为结构模态结果。通过IASC-ASCE结构健康监测工作组提供的4层框架Benchmark模型算例,阐述了所提IpHist方法在环境激励下辨识结构模态的有效性,显示了方法较强的抗噪能力。

During the modal identification of a structure subjected to ambient excitations ,the system order as a crucial computa‐tion parameter is not easy to be determined ,and the stabilization diagram method based on assumed system orders is often a‐dopted to help the identification .But how to distinguish the stabilization axes is in fact subjective ,which may lead to possible inclusion of pseudo vibration modes instead of real modes into the final results .To avoid these problems ,an identification‐probability histogram （IpHist） method in use of the data mining technique is proposed in the present paper .Firstly ,the sto‐chastic subspace method is applied to identify the alternative modes with different assumed system orders .Then ,all the alter‐native modes are clustered into several categories by using the criteria of frequency tolerance and MAC tolerance ,and the iden‐tification probability of each category is obtained along with the corresponding identification‐probability histogram .Finally ,the clustered modes with large identification probability are chosen to be the structural modes .By taking a four‐story Benchmark model provided by the IASC‐ASCE structural health monitoring workgroup as example ,numerical results are presented to il‐lustrate the effectiveness and anti‐noise capacity of the proposed IpHist method for modal identification of structures subjected to ambient excitations .