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Higher-density dyadic wavelet transform and its application

机译:高密度二进小波变换及其应用

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This paper proposes a higher-density dyadic wavelet transform with two generators, whose corresponding wavelet filters are band-pass and high-pass. The wavelet coefficients at each scale in this case have the same length as the signal. This leads to a new redundant dyadic wavelet transform, which is strictly shift invariant and further increases the sampling in the time dimension. We describe the definition of higher-density dyadic wavelet transform, and discuss the condition of perfect reconstruction of the signal from its wavelet coefficients. The fast implementation algorithm for the proposed transform is given as well. Compared with the higher-density discrete wavelet transform, the proposed transform is shift invariant. Applications into signal denoising indicate that the proposed wavelet transform has better denoising performance than other commonly used wavelet transforms. In the end, various typical wavelet transforms are applied to analyze the vibration signals of two faulty roller bearings, the results show that the proposed wavelet transform can more effectively extract the fault characteristics of the roller bearings than the other wavelet transforms.
机译:提出了一种具有两个发生器的高密度二进小波变换,其对应的小波滤波器为带通和高通。在这种情况下,每个尺度上的小波系数具有与信号相同的长度。这导致了新的冗余二进小波变换,该变换严格地是不变的,并且进一步增加了时间维度上的采样。我们描述了高密度二进小波变换的定义,并从其小波系数讨论了信号完美重构的条件。给出了所提出的变换的快速实现算法。与高密度离散小波变换相比,该变换具有不变性。在信号去噪中的应用表明,提出的小波变换比其他常用的小波变换具有更好的去噪性能。最后,采用各种典型的小波变换对两个故障滚动轴承的振动信号进行分析,结果表明,所提出的小波变换比其他小波变换能更有效地提取滚动轴承的故障特征。

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