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Unsupervised identification of arbitrarily-damped structures using time-scale independent component analysis: Part II

机译:使用时间尺度独立分量分析无预测识别任意阻尼结构:第二部分

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摘要

To identify the dynamic parameters of the structures with sufficient accuracy, a new method is developed and used in this study. In this method, the wavelet-transformed (WT) representation of system responses is conducted on the measured responses, and then the independent component analysis (ICA) is implemented to obtain the modal features. Effectiveness of the proposed method under applied loading condition is shown by applying the white noise, and extracting the simulation results of a multi-degree-of-freedom system to illustrate the applicability of the proposed methodology for both lightly- and highly-damped structures. In this study, it is determined that continuous wavelet transform (CWT) is indicated to have a better efficiency due to its higher adaptive resolution in time-frequency to be incorporated into independent component analysis compared to other conventional methodologies. The applicability of the proposed method for assessing the natural modal frequencies and mode shapes of the existent structures is investigated by studying the IASCASCE structural health monitoring benchmark. It is shown that in all the cases the modal properties along with the modal assurance criterion (MAC) values are in satisfactory agreement with their exact values and the proposed method is sufficiently robust in accurate extraction of higher modes of vibration.
机译:为了以足够的精度识别结构的动态参数,在本研究中开发并使用了一种新方法。在该方法中,对测量的响应进行系统响应的小波变换(WT)表示,然后实施独立的分量分析(ICA)以获得模态特征。通过施加白噪声示出了所提出的方法的有效性,并通过施加白噪声,提取多程度自由度系统的模拟结果以说明所提出的方法对轻型和高度阻尼结构的适用性。在该研究中,确定连续小波变换(CWT)被指示由于其在与其他传统方法相比的时间频率中以时频的较高自适应分辨率而具有更好的效率。通过研究IASCASCE结构健康监测基准,研究了提出的评估存在结构的自然模态频率和模式形状的应用方法。结果表明,在所有情况下,模态特性以及模态保证标准(MAC)值与其精确值令人满意的协议,并且所提出的方法在准确提取更高振动模式的准确提取时足够稳健。

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