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首页> 外文期刊>Mechanics research communications >Quantile autoregressive modeling for non-linear change detection in vibrating structural systems
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Quantile autoregressive modeling for non-linear change detection in vibrating structural systems

机译:振动结构系统中非线性变化检测的定量自回归模型

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This article presents quantile autoregressive modeling (QAR), a new tool in relation to structural health monitoring addressing the problems associated with sudden changes in linear stiffness, transitions from linear to non-linear structural states and contiguous non-linear changes of states. Acceleration data is modeled using QAR process which involves fitting autoregressive (AR) coefficients at various pre-selected quantiles. The necessity of such a modeling stems from the inability of traditional AR models to account for non-stationary variance and the presence of non-linearity. Percentage changes in the QAR coefficients at different quantiles is proposed as a conditional indicator to detect changes in state of the structural systems. Numerical simulations on a hysteretic system subjected to El-Centro ground motion and a laboratory-scale experiment on a two-storied shear building model undergoing real time mass-loss demonstrate the robustness of the proposed methodology. (C) 2019 Elsevier Ltd. All rights reserved.
机译:本文介绍了定量的自回归建模(QAR),一种与结构健康监测相关的新工具,解决了与线性刚度突然变化相关的问题,从线性到非线性结构状态和状态的连续非线性变化。加速数据使用QAR过程进行建模,QAR过程涉及在各种预先选择的量级处拟合自回归(AR)系数。这种建模的必要性源于传统的AR模型的无法,以考虑非静止方差和非线性的存在。提出了不同量程的QAR系数的百分比变化作为检测结构系统状态变化的条件指示。对El-Centro地面运动的滞后系统的数值模拟及实验室尺度实验在经历实时质量损失的两层剪切建筑模型上证明了提出的方法的鲁棒性。 (c)2019年elestvier有限公司保留所有权利。

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