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The research of optimal selection method for wavelet packet basis in compressing the vibration signal of a rolling bearing in fans and pumps

机译:小波包的最优选择方法研究压缩风扇和泵滚动轴承振动信号的基础

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Compressing the vibration signal of a rolling bearing has important significance to wireless monitoring and remote diagnosis of fans and pumps which is widely used in the petrochemical industry. In this paper, according to the characteristics of the vibration signal in a rolling bearing, a compression method based on the optimal selection of wavelet packet basis is proposed. We analyze several main attributes of wavelet packet basis and the effect to the compression of the vibration signal in a rolling bearing using wavelet packet transform in various compression ratios, and proposed a method to precisely select a wavelet packet basis. Through an actual signal, we come to the conclusion that an orthogonal wavelet packet basis with low vanishing moment should be used to compress the vibration signal of a rolling bearing to get an accurate energy proportion between the feature bands in the spectrum of reconstructing the signal. Within these low vanishing moments, orthogonal wavelet packet basis, and 'coif' wavelet packet basis can obtain the best signal-to-noise ratio in the same compression ratio for its best symmetry.
机译:压缩滚动轴承的振动信号对石化工业广泛应用的风扇和泵的无线监测和远程诊断具有重要意义。本文提出了根据滚动轴承中的振动信号的特性,提出了一种基于大波分组的最佳选择的压缩方法。我们分析小波包的几个主要属性,并在各种压缩比中使用小波包变换将振动信号压缩振动信号的效果,并提出了一种精确地选择小波包的方法。通过实际信号,我们得出结论,应使用低消失力矩的正交小波包基础来压缩滚动轴承的振动信号,以在重建信号的光谱中获得特征频带之间的精确能量比例。在这些低消失的矩,正交小波包基础上,“COIF”小波分组基础可以获得与其最佳对称性相同的压缩比中的最佳信噪比。

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