稳定图方法在随机子空间识别模态参数中的应用

APPLICATION OF STABILIZATION DIAGRAM FOR MODAL PARAMETER IDENTIFICATION USING STOCHASTIC SUBSPACE METHOD

  • 摘要: 参数识别是结构健康监测领域研究中的重点。随机子空间法是近年来发展起来的一种线性系统辩识方法,可以有效地从环境激励的结构响应中获取模态参数。在随机子空间识别方法中,确定系统的阶数是该方法的关键工作。稳定图方法是一种比较新颖的确定系统阶次的方法,但该方法容易识别出虚假模态。针对这种情况对稳定图方法进行了改进,避免了虚假模态的出现,进而提高了随机子空间方法的识别精度。稳定图方法改进的重点是用模态置信因子来消除虚假模态。同时由于通常采用的阻尼理论与实际情况尚存在差距,影响了识别效果。在稳定图中将阻尼比的标准放松或取消,得到更加理想的识别效果。最后对此方法在一三跨连续梁的数值模型上进行了验证,结果表明,该方法具有良好的识别效果。

     

    Abstract: Parameter identification is currently one of the main research topics in the area of structural health monitoring. Stochastic subspace identification is a novel approach developed in recent years. It can identify modal parameters of linear structure from its ambient vibration. The key issue in stochastic subspace identification is to obtain the order of the system. Stabilization diagram is a novel approach to identify the order of the system, but it may yield false modes. An improved stabilization diagram is presented so that false modes can be distinguished and more precise identification results can be obtained. The improvement to stabilization diagram is to distinguish the false modes by using modal assurance criteria. At the same time, because the present damping theory does not accord to actual damping, the identified results are not satisfactory. Therefore, structural damping is eliminated or loosed in stabilization diagram, and more precise results are obtained. Improved stabilization diagram method is evaluated by numerical simulation on a three-span continuous beam.

     

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