改进阈值小波去噪方法在机床冲孔声音信号处理中的应用

Application of Improved Threshold Wavelet Denoising Method in the Processing of Sound Signal of Machine Tool Punching

机床冲孔产生的声音信号包含了许多有用信息,为了滤除声音信号提取过程中的工厂环境产生的噪声,使用改进阈值小波去噪方法对声音信号进行处理。利用信噪比(SNR)和均方误差(MSE)作为去噪效果指标,使用新的阈值选取规则,提出了一种分段连续的阈值函数,对传统小波去噪方法进行改进。该改进函数通过引入常数a,解决了传统阈值函数中软硬阈值函数不连续和恒定偏差问题。采集机床冲孔声音信号并叠加高斯白噪声作为机床冲孔含噪信号进行验证。通过实验确定了最佳小波函数类型和分解层数,将改进阈值小波去噪算法与传统算法进行对比,结果表明:改进后的算法去噪效果良好,且有效保留了较低的信号能量。

The sound signals generated by the machine tool punching contain a lot of useful information.In order to filter out the noise generated by the factory environment from the extracted sound signals,the improved threshold wavelet denoising method was used to process the sound signal.SNR and MSE were used as denoising indicators,a piecewise continuous threshold function was proposed to improve the traditional wavelet denoising method.The improved function solves the problem of discontinuity and constant deviation of soft and hard threshold function in traditional threshold function by introducing the constant a.By using the collected sound signal of machine tool punching superimposed with Gaussian white noise as the noise signal of machine tool punching,the improved threshold wavelet denoising method was verified.The optimal wavelet function type and decomposition layer number were determined through experiments,and the improved threshold wavelet denoising algorithm was compared with the traditional algorithm.The results show that the improved algorithm has a good denoising effect and can effectively retain the lower signal energy.