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Modal contribution and state space order selection in operational modal analysis

机译:操作模态分析中的模态贡献和状态空间顺序选择

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The estimation of modal parameters of a structure from ambient measurements has attracted the attention of many researchers in the last years. The procedure is now well established and the use of state space models, stochastic system identification methods and stabilization diagrams allows to identify the modes of the structure. In this paper the contribution of each identified mode to the measured vibration is discussed. This modal contribution is computed using the Kalman filter and it is an indicator of the importance of the modes. Also the variation of the modal contribution with the order of the model is studied. This analysis suggests selecting the order for the state space model as the order that includes the modes with higher contribution. The order obtained using this method is compared to those obtained using other well known methods, like Akaike criteria for time series or the singular values of the weighted projection matrix in the Stochastic Subspace Identification method. Finally, both simulated and measured vibration data are used to show the practicability of the derived technique. Finally, it is important to remark that the method can be used with any identification method working in the state space model.
机译:近年来,根据环境测量结果估算结构的模态参数吸引了许多研究人员的注意力。现在已经很好地建立了程序,使用状态空间模型,随机系统识别方法和稳定图可以识别结构的模式。在本文中,讨论了每种识别模式对测得的振动的贡献。使用卡尔曼滤波器计算模态贡献,它是模态重要性的指标。还研究了模态贡献随模型阶数的变化。该分析建议选择状态空间模型的顺序作为包括具有较高贡献模式的顺序。将使用此方法获得的顺序与使用其他众所周知的方法获得的顺序进行比较,例如时间序列的Akaike标准或随机子空间识别方法中加权投影矩阵的奇异值。最后,通过仿真和实测振动数据来证明所推导技术的实用性。最后,重要的一点是,该方法可以与在状态空间模型中工作的任何识别方法一起使用。

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