基于欠定盲源分离的结构模态参数识别

针对欠定情况下传统盲源分离(blind source separation,简称BSS)算法无法有效识别结构模态参数的问题,研究了一种不受传感器数量限制的BSS算法。算法主要分为振型矩阵估计与单模态信号分离两步。首先,利用各阶模态响应信号在时频域中的聚类特性估计结构的模态振型;然后,在已知振型矩阵的基础上,通过L1范数最小化算法分离出多个单模态信号;最后,利用单模态参数识别方法提取各阶模态的频率与阻尼比。经仿真与实验验证,本研究方法可以准确识别出结构的各阶模态参数,同时对测量噪声不敏感,具有很好的噪声鲁棒性,在工程实践中具有一定的应用价值。

Identification of Modal Parameters Using Under-Determined Blind Source Separation

The under-determined blind source separation(BSS) was employed in identifying modal parameters when there were fewer sensors than modal responses. The separation process was divided into two steps: first, estimation of the mixing matrix, and second, recovery of sources. A new method for estimating the mixing matrix was proposed that utilizes the scatter of the local energy maximum value in the frequency domain. Then the L1-norm minimum was used for recovering sources. Finally, frequency and damping ratios could be obtained from the modal responses using each single degree of freedom mode. The effectiveness of the proposed method was verified by theoretical analysis and simulation and experimental data, and the background noise was greatly suppressed.