基于左矩阵分式模型的短记录数据模态参数识别

Modal parameters identification of short data sequence based on left matrix fraction description

在测量数据有限情况下,由于难以获得频响函数(FRF)的准确估计,使用FRF作为原始数据的传统模态参数识别方法将不再适用。针对该问题,提出一种基于频响函数左矩阵分式模型的模态参数识别方法。该方法直接使用输入输出数据FFT谱(IO谱)作为原始数据,避免了频响函数估计。通过最小二乘估计在Z域内求解模态参数,改善了矩阵的求解性态。针对左矩阵分式模型的特点,给出了一种通过主分量分析(PCA)建立稳定图的方法。最后采用GAR-TEUR飞机模型建立仿真算例对所提出的方法进行了验证。

Owing that the FRF cannot be estimated exactly when only a limited test data is available,classical modal parameters identification algorithms using FRF as primary data are not suitable any more.Focusing on this problem,a modal parameters identification algorithm based on left matrix fraction description of FRF was proposed.The algorithm uses FFT spectrums of input and output data as primary data.The modal parameters were then achieved by using LS estimator.The bad numerical condition of matrix can be avoided in Z domain.Considering the feature of left matrix fraction description,a method for construction of stabilization diagram was presented via principal component analysis(PCA).A simulation case of GARTEUR model was employed to validate the algorithm.