基于迭代极点迁移的国际空间站运行模态分析
International Space Station operational modal analysis via iterative pole relocation
AI总结：
提出一种结合自然激励技术与快速松弛矢量拟合的迭代最小二乘优化方法，用于运行模态分析，并在国际空间站加速度数据上验证了其高可靠性和抗噪性。
摘要：
近年来，日益提高的航空航天安全要求加剧了对可靠结构损伤检测的需求。本文提出了一种用于精确模态参数估计的运行模态分析方法，并应用于空间结构监测。所提出的系统辨识方法创新性地将自然激励技术与快速松弛矢量拟合算法相结合，该算法采用迭代最小二乘优化。首先在数值梁模型上进行初步验证，将结果与解析解以及已建立的自然激励技术结合特征系统实现算法和随机子空间辨识结合典型变量分析方法进行比较。然后，对来自国际空间站空间加速度测量系统的真实加速度数据进行运行验证。信号处理后，NExT-FRVF和NExT-ERA识别的振动模态结果相当，模态一致性通过重复出现和物理解释进行评估，而SSI未能识别出大多数模态。该仅输出算法被证明高度可靠，在数值系统的噪声条件下优于基准方法，并在实验数据上提供了可靠的识别结果。
英文摘要：
In recent years, increasing aerospace safety requirements have intensified the demand for reliable structural damage detection. This work presents an Operational Modal Analysis approach for accurate modal parameter estimation, with an application to space structure monitoring. The proposed System Identification (SI) method innovatively combines the Natural Excitation Technique (NExT) with the Fast and Relaxed Vector Fitting (FRVF) algorithm, which uses an iterative least-squares optimisation. A preliminary validation is first carried out on a numerical beam model, comparing results with analytical solutions and the established Natural Excitation Technique with Eigensystem Realisation Algorithm (NExT-ERA) and Stochastic Subspace Identification with Canonical Variate Analysis (SSI) methods. Then, operational validation is performed on real acceleration data from the Space Acceleration Measurement Systems aboard the International Space Station. Identified vibration modes from NExT-FRVF and NExT-ERA show comparable results after signal processing, with mode consistency assessed by repeated occurrence and physical interpretation, while SSI fails to identify most. The output-only algorithm proves to be highly reliable, outperforming benchmark methods under noisy conditions on a numerical system and offering reliable identifications on the experimental data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
