基于自适应小波阈值的皮带机故障声音信号去噪算法研究  

Research on Denoising Algorithm of Sound Signals of Belt Conveyor Faults Based on Adaptive Wavelet Threshold

作　　者：李磊 江帅帅 徐崇杰 沙明璇 LI Lei;JIANG Shuaishuai;XU Chongjie;SHA Mingxuan(College of Engineering,Qufu Normal University,Rizhao 276825,China)

机构地区：[1]曲阜师范大学工学院,山东日照276825

出　　处：《控制工程》2025年第4期699-706,共8页Control Engineering of China

基　　金：山东省自然科学基金面上项目(ZR2020MF092)。

摘　　要：皮带机的声音信号包含了大量的运行状态信息,因此采集声音信号对皮带机的故障诊断至关重要。传统的小波阈值去噪算法无法满足强噪声背景下提取微弱声音信号的要求。因此,对传统的小波阈值去噪算法进行了改进,提出了具有自适应小波阈值的连续型低误差小波阈值函数。自适应小波阈值为关于分解层数的分段函数,与分解层数成反比,能更好地适应噪声系数随小波分解层数的增加而减小的特征。实验结果表明,与硬阈值去噪算法、软阈值去噪算法和小波模极大值去噪算法相比,改进后的算法对皮带机声音信号的去噪能力更强,对原始信号的重构更精确。The sound signals of belt conveyors contain a large amount of operating status information,so the acquisition of sound signals is crucial for the diagnosis of belt conveyor faults.The existing wavelet threshold denoising algorithms can not meet the requirement of extracting weak sound signals in strong noise background.Therefore,the conventional wavelet threshold denoising algorithm is improved,and a continuous low-error wavelet threshold function with adaptive wavelet threshold is proposed.The adaptive wavelet threshold is a segmentation function about the number of decomposition layers,which is inversely proportional to the number of decomposition layers,and can better adapt to the feature that the noise coefficient decreases with the increase of the decomposition layers when wavelet decomposition is performed.The experimental results show that the improved algorithm has stronger denoising ability and more accurate reconstruction of the original signal compared with hard threshold denoising algorithm,soft threshold denoising algorithm and wavelet modulus maximum denoising algorithm.

关 键 词：声音信号 小波去噪 皮带机故障 自适应小波阈值函数 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]