Improved modal parameter identification method based on particle swarm optimization
ZHANG Jindong1, GUO Xiaonong1, LUO Xiaoqun1, ZHANG Yujian1, XU Hongjun1,2
1. College of Civil Engineering, Tongji University, Shanghai 200092, China;
2. State Grid Jiangsu Electric Power Engineering Consulting Co., Ltd., Nanjing 210000, China
摘要针对一类多模态振动衰减信号的模态参数识别，结合奇异值分解(singular value decomposition ,SVD)、解析模态分解(analytical mode decomposition ,AMD)、自回归功率谱(auto regressive power spectral density ,ARPSD)和粒子群算法(particle swarm optimization ,PSO)，提出了一种改进的模态参数识别方法(PSO-AMD)，可实现在强干扰环境下密集频率信号的模态参数识别。对模拟振动响应信号的分析结果表明，本文提出的改进方法具有更高的稳定性，对低信噪比、密集频率、大阻尼的振动信号仍保持高识别精度。本文的模态参数识别方法可应用于复杂噪声环境中的大阻尼和密集频率衰减振动信号的模态参数识别。
Abstract：Aiming at the modal parameter identification of multimodal vibration attenuation signals, an improved modal parameter identification method (PSO-AMD) is proposed by combining singular value decomposition (SVD), analytical mode decomposition (AMD), auto regressive power spectral density spectrum (ARPSD) and particle swarm optimization (PSO). The method can be used for modal parameter identification of dense frequency signals in strong noise environment. The analysis results of the simulated vibration response signal show that the improved method proposed in this paper has higher stability and maintains high recognition accuracy for vibration signals with low SNR, dense frequency and large damping. The modal parameter identification method in this paper can be applied to modal parameter identification of large damping and dense frequency vibration attenuation signals in complex noise environment.
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