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Determination of Reliable Control Parameters for Monitoring of Large Flexible Structure Using Recursive Stochastic Subspace Identification

机译:使用递增随机子空间识别测定监测大柔性结构的可靠控制参数

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The detection of the change of features or damage in large structural system, such as buildings and bridges, can improve safety and reduce maintenance costs. Therefore, feature extract and damage detection from vibration of structures play an important role in operational modal analysis. The objective of this paper is to develop on-line system parameter estimation and damage detection technique from the response measurements through using both the Stochastic Subspace identification (SSI) and Recursive Stochastic Subspace identification (RSSI) approaches. To avoid time-consumption of SVD in RSSI the Extended Instrumental Variable version of Projection Approximation Subspace Tracking is used (EIV-PAST). From numerical study the reliable control parameters of SSI and RSSI methods are examined. Discussion on the pre-processing of data using singular spectrum analysis technique (SSA) is also presented to remove the noise contaminant measurements so as to enhance the stability of data analysis. Finally, the recursive SSA-SSI-COV method is examined to identify the system dynamic characteristics with time-varying model parameters.
机译:检测大型结构系统的特征或损坏的变化,例如建筑物和桥梁,可以提高安全性并降低维护成本。因此,结构的特征提取和损伤检测结构在操作模态分析中起重要作用。本文的目的是通过使用随机子空间识别(SSI)和递归随机子空间识别(RSSI)方法来开发从响应测量的在线系统参数估计和损坏检测技术。为避免RSSI中SVD的时间消耗,使用投影近似子空间跟踪的扩展仪器变量版本(EIV-FINES)。根据数值研究,检查了SSI和RSSI方法的可靠控制参数。还介绍了关于使用奇异谱分析技术(SSA)数据的预处理以除去噪声污染物测量,以提高数据分析的稳定性。最后,检查递归SSA-SSI-COV方法以识别具有时变模型参数的系统动态特性。

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