数据驱动与协方差驱动随机子空间法差异化分析

针对能有效从环境激励结构振动响应中获取模态参数的随机子空间法，传统观点认为无论在理论上或应用中数据驱动随机子空间法与协方差驱动随机子空间法在模态参数识别过程中表现一致，实际应用中表现不一致问题，理论上探讨两种方法出现差异的原因，并进行相应的数值模拟。研究结果表明：基于QR分解的数据驱动随机子空间法无论计算精度或对较弱势模态的识别能力均明显优于协方差驱动随机子空间法。

Performance comparison for data-driven and covariance-driven stochastic subspace identification methtod

The stochastic subspace method is a linear system identification method developed in recent years, which can effectively obtain modal parameters from the response of structure under ambient excitation. The data-driven and covariance-driven stochastic subspace identification methods traditionally were thought, theoretically and practically, to be consistent with each other for modal identification. However, the difference in practice between the two methods exists. Therefore, the reasons of the performance difference were analyzed and numerical study was conducted. Results demonstrate that data-driven stochastic subspace identification method outperforms the covariance driven subspace identification method not only on accuracy of identification parameter but also on capacity of identifying weaker mode.