Application of Adaptive Wavelet Thresholding Algorithm in Mould Friction Signal Denoising

Hong-Jun Wang, Xiang-fei Si, Zhuo-qun Zhao
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引用次数: 1

Abstract

The noise always affects the accuracy result of the abrupt breakout signal, during the analysis of mould friction signal of continuous slab casting. The traditional signal de-noising method makes the signal's edges information ambiguous as reduce the noise, and which leads to signal's partly distortion. In this paper, based on wavelet threshold de-noising, proposed by Dohono, using an adaptive wavelet threshold to the separation of signal and noise, experiments show that the adaptive wavelet threshold method can well remove the noise, the new method can improve signal-to-noise ratio. Furthermore, the integrity of the detail information still well guaranteed at the same time.
自适应小波阈值算法在模具摩擦信号去噪中的应用
在对连铸结晶器摩擦信号进行分析的过程中,噪声的存在往往会影响信号的准确性。传统的信号去噪方法在降低噪声的同时,使信号的边缘信息模糊不清,从而导致信号的部分失真。本文在Dohono提出的小波阈值降噪的基础上,利用自适应小波阈值对信号和噪声进行分离,实验表明,自适应小波阈值法能很好地去除噪声,新方法能提高信噪比。同时,还能很好地保证细节信息的完整性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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