二阶统计量盲辨识在模态参数识别中的应用

APPLICATION OF SECOND-ORDER STATISTICS BLIND IDENTIFICATION ON IDENTIFYING MODAL PARAMETERS

快速、准确地识别出结构的模态参数,是结构损伤精确识别与健康监测的重要前提。该文提出一种结构模态参数识别的新方法。该方法以盲源分离理论中基于二阶统计量的AMUSE算法为基础,以振动系统的自由响应或脉冲响应为分析对象,通过对数据进行Hilbert变换增加虚拟测点,以不同时滞下数据协方差矩阵构建联合矩阵,通过求解时滞联合矩阵的特征值问题实现对结构模态参数的识别。联合矩阵的引入克服了AMUSE算法仅采用两个时滞协方差矩阵所带来的不稳定性。数值算例结果表明,该文提出的方法计算简单,识别精度高,不受时滞选择的限制,对测量白噪声不敏感,具有很好的鲁棒性。

It is important to identify structural modal parameters in time and accurately for accurate damage identification and health monitoring of structures. A new method of identifying the structural modal parameters is developed in this paper. This method is based on the AMUSE algorithm in blind source separation theory. It considers the free responses or pulse responses of an oscillatory system as the analysis object and increased virtual measured points by using Hilbert transformation to the data. This method uses a variance covariance matrix with different time lags to build joint matrices. It realizes identifying the structural modal parameters by solving the eigen value of joint matrices with different time lags. Introducing the joint matrices conquers the instability coming from the AMUSE algorithm, which only uses two covariance matrices. A set of applications have been performed. The numerical results show that the method developed in this paper has the following advantages： the calculation is simple; the identification has high accuracy, not limited by the choice of time lags, not sensitive to measure white noise and having good robustness.