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VECTOR TRIGGERING RANDOM DECREMENT TECHNIQUE FOR HIGHER IDENTIFICATION ACCURACY

机译:矢量触发随机递增技术,以获得更高的识别精度

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摘要

Using the Random Decrement (RDD) technique to obtain free response estimates and combining this with time domain modal identification methods to obtain the poles and the mode shapes is acknowledged as a fast and accurate way of analyzing measured responses of structures subjected to ambient loads. Using commonly accepted triggering conditions however, one is limited to use a combination of auto-RDD and cross-RDD functions with high noise contents on the cross-RDD functions. Using only the auto-RDD functions, estimated independently for each channel, causes the loss of phase information and thus the possibility of estimating mode shapes. In this paper, a new algorithm is suggested that is based on pure auto-triggering. Auto-RDD functions are estimated for all channel to obtain functions with a minimum of noise, but using a vector triggering condition that preserves phase information, and thus, allows for estimation of both poles and mode shapes. The proposed technique (VRDD) is compared with more commonly used triggering conditions by evaluating modal parameters estimated by time domain technique on simulated data.
机译:使用随机递增(RDD)技术获得自由响应估计并将其与时域模态识别方法组合以获得极点,并且模式形状被确认为分析经受环境负载的结构的测量响应的快速和准确的方式。然而,使用常见的触发条件然而,一个仅限于在交叉RDD函数上使用具有高噪声内容的自动RDD和交叉RDD函数的组合。仅使用自动RDD函数,为每个通道独立估计,导致相位信息的丢失,从而导致估计模式形状的可能性。在本文中,建议基于纯自动触发的新算法。估计Auto-RDD功能的所有通道以获得具有最小噪声的功能,但使用保留相位信息的矢量触发条件,因此允许估计两极和模式形状。将所提出的技术(VRDD)与通过在模拟数据上的时域技术估计的估计的模态参数进行比较,与更常用的触发条件进行比较。

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