自适应随机共振降噪下的结构模态参数识别

Structural modal parameters identification based on adaptive stochastic resonance denoising

针对强背景噪声下结构模态参数难识别以及传统自适应随机共振单参数优化的不足,提出一种基于改进多粒子群协同优化算法的多参数同步优化的自适应随机共振方法,结合利希尔伯特变换来识别出结构的模态参数.该算法能够更快得到最佳随机共振系统结构参数,自适应地实现非线性系统、输入信号和噪声之间的最佳匹配,削弱强背景噪声响应中的噪声,提高响应的输出信噪比.数值仿真和试验均表明,该方法参数寻优效率高,简单易行,能够成功识别出强背景噪声下结构的模态参数.

Based on the challenge of structural modal parameters identification in the case of strong noise and low signal-to-noise ratio （SNR） environment and the deficiency of single parameter optimi-zation in traditional adaptive stochastic resonance,this paper presents a new adaptive stochastic reso-nance method based on improved multi-particle swarm collaborative optimization （IMPSCO）,which can perform multi-parameter synchronous optimization. Combined with Hilbert transform,the adaptive stochastic resonance algorithm can identify modal parameters of a structure. This algorithm can obtain the optimal structure parameters more quickly and adaptively realize optimal matching among the non-linear system,input signal and noise. Therefore the noise of multi-frequency noisy signal is weakened and signal-noise-ratio （SNR） of the output is improved. The results from numerical simulation and a laboratory test conducted show that the proposed algorithm is simple and efficient in searching optimal parameters,furthermore enables to identify the structural modal parameters in case of strong noise.