The main purpose of the present study is to enhance high-level noisy data by a wavelet-based iterative filtering algorithm for identification of natural frequencies during ambient wind vibrational tests on a petrochemical process tower. Most of denoising methods fail to filter such noise properly. Both the signal-to-noise ratio and the peak signal-to-noise ratio are small. Multiresolution-based one-step and variational-based filtering methods fail to denoise properly with thresholds obtained by theoretical or empirical method. Due to the fact that it is impossible to completely denoise such high-level noisy data, the enhancing approach is used to improve the data quality, which is the main novelty from the application point of view here. For this iterative method, a simple computational approach is proposed to estimate the dynamic threshold values. Hence, different thresholds can be obtained for different recorded signals in one ambient test. This is in contrast to commonly used approaches recommending one global threshold estimated mainly by an empirical method. After the enhancements, modal frequencies are directly detected by the cross wavelet transform (XWT), the spectral power density and autocorrelation of wavelet coefficients. Estimated frequencies are then compared with those of an undamaged-model, simulated by the finite element method.

中文翻译：

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基于小波的迭代数据增强，可用于模式频率的净化，以实现设拉子-伊朗极端嘈杂的环境振动测试

本研究的主要目的是通过基于小波的迭代滤波算法增强高噪声数据，以识别石化工艺塔在环境风振动测试过程中的固有频率。大多数降噪方法无法正确滤除此类噪声。信噪比和峰值信噪比均很小。基于多分辨率的单步和基于变分的滤波方法无法通过理论或经验方法获得的阈值进行适当的降噪。由于不可能完全去噪这种高级别的噪声数据，因此使用增强方法来改善数据质量，这是从应用角度来看的主要新颖之处。对于这种迭代方法，提出了一种简单的计算方法来估计动态阈值。因此，在一个环境测试中，对于不同的记录信号可以获得不同的阈值。这与推荐一种主要通过经验方法估计的全局阈值的常用方法相反。增强后，通过交叉小波变换（XWT），谱功率密度和小波系数的自相关直接检测模态频率。然后将估计的频率与通过有限元方法模拟的未损坏模型的频率进行比较。谱功率密度和小波系数的自相关。然后将估计的频率与通过有限元方法模拟的未损坏模型的频率进行比较。谱功率密度和小波系数的自相关。然后将估计的频率与通过有限元方法模拟的未损坏模型的频率进行比较。