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A novel time-domain representation of transmissibility and its applications on operational modal analysis in the presence of non-white stochastic excitations

机译:非白随机激励存在下传播性的新型时域表示及其对运营模态分析的应用

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摘要

Transmissibility based operational modal analysis (TOMA) method is an effective solution to modal parameter estimation problem for a linear system under non-white stochastic excitations. However, the TOMA method has two main limitations. Firstly, the transmissibility function defined in frequency domain necessitates Fourier transform to acquire frequency-domain data, thus leading to leakage errors and window function selection problem. Secondly, it needs multiple sufficiently different load cases to estimate the modal parameters, and how to quantify the difference between different load cases remains a problem. To remove these two limitations, this paper proposes the time-domain representation of transmissibility based operational modal analysis technique. This technique contains two methods, namely time-domain transmissibility based method and correlation function transmissibility (CFT) based method. Both methods employ the time-domain data directly and thus avoid the Fourier transform. The CFT based method combines CFT functions with different transfer outputs instead of different load cases to estimate modal parameters. Numerical and laboratory examples show that the proposed two methods eliminate the leakage errors thoroughly and window function selection problem, showing higher accuracy and lower sensitivity to short response data than the existing frequency-domain methods. Furthermore, two methods can be expressed in a unified mathematical form through which the modal parameters can be obtained by solving an eigenvalue problem in low computational cost. (C) 2019 Elsevier Ltd. All rights reserved.
机译:基于传导性的操作模态分析(TOMA)方法是非白色随机激励下线性系统模态参数估计问题的有效解决方案。但是,Toma方法有两个主要限制。首先,频域中定义的传输功能需要傅立叶变换来获取频域数据,从而导致泄漏错误和窗口功能选择问题。其次,它需要多个足够不同的负载盒来估计模态参数,以及如何量化不同负载案例之间的差异仍然是一个问题。为了消除这两个限制,本文提出了基于传导性的运算模态分析技术的时域表示。该技术包含两种方法,即基于时域传输性的方法和相关函数传输(CFT)的方法。两种方法直接采用时域数据,从而避免傅里叶变换。基于CFT的方法将CFT函数与不同的传输输出组合而不是不同的负载箱来估计模态参数。数值和实验室示例表明,提出的两种方法彻底消除了泄漏误差,窗口功能选择问题,比现有频域方法更高的精度和对短响应数据的敏感性较高。此外,可以以统一的数学形式表达两种方法,通过该方法,通过该方法可以通过求解低计算成本的特征值问题来获得模态参数。 (c)2019 Elsevier Ltd.保留所有权利。

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