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Parameters Optimization of Wavelet Transform in Modal Parameter Identification with Closely Spaced Modes

机译:具有紧密间隔模式的模态参数识别中小波变换的参数优化

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In recent years, the identification of modal parameters with closely spaced modes based on wavelet transform has been widely applied in the research area of structural health monitoring. But the parameters of wavelet, i.e. center frequency and its bandwidth have great effect on the results of identification. If the parameters of wavelet are inappropriate, the closely spaced modes could not be identified. In order to optimize the parameters of wavelet, a method based on adaptive genetic algorithm is proposed in this paper. The center frequency and its bandwidth are optimized with the adaptive genetic algorithm, whose objective function is the standard deviation between wavelet-ridge and fitting line. The results of numerical simulation with three closely spaced modes show that, the method proposed in this paper could optimize the wavelet parameters and the closely spaced modes could be identified by the wavelet transform with adaptive genetic algorithm.
机译:近年来,基于小波变换的基于小波变换的紧密间隔模式的模态参数的识别已广泛应用于结构健康监测的研究领域。但是小波的参数,即中心频率及其带宽对识别结果产生了很大的影响。如果小波的参数不合适,则无法识别紧密间隔的模式。为了优化小波的参数,本文提出了一种基于自适应遗传算法的方法。中心频率及其带宽与自适应遗传算法进行了优化,其客观函数是小波脊和配合线之间的标准偏差。用三种紧密间隔模式的数值模拟结果表明,本文提出的方法可以优化小波参数,并且可以通过具有自适应遗传算法的小波变换来识别密集的模式。

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