基于改进的EWT+小波阈值的齿轮振动信号去噪研究  

Based on Improved EWT andWavelet Threshold

作　　者：张鹏镇 王琳 刘雨轩 徐鑫 尹晓伟 陈冀驰 ZHANG Pengzhen;WANG lin;LIU Yuxuan;XU Xin;YIN Xiaowei;CHEN Jichi(School of Energy and Power Engineering,Shenyang Institute of Engineering,Shenyang 110136;College of Machinery,Shenyang Institute of Engineering,Shenyang 110136;School of Mechanical Engineering,Shenyang University of Technology,Shenyang 110870,Liaoning Province)

机构地区：[1]沈阳工程学院能源与动力学院,辽宁沈阳110136 [2]沈阳工程学院机械学院,辽宁沈阳110136 [3]沈阳工业大学机械工程学院,辽宁沈阳110870

出　　处：《沈阳工程学院学报（自然科学版）》2024年第3期39-44,共6页Journal of Shenyang Institute of Engineering:Natural Science

基　　金：国家自然科学基金(62001312,62101355);辽宁省科学技术计划项目(2021-MS-269)。

摘　　要：针对采集到的齿轮振动信号含有较多噪声,严重影响后续齿轮故障诊断准确性的问题,提出一种改进的经验小波变换(EWT)结合小波阈值的去噪方法。该方法对传统的EWT频谱分割方法进行了改进,首先使用寻峰算法对频谱寻峰,然后采用平滑算法对峰值曲线进行平滑处理,取平滑曲线的极小值点作为频谱分割边界,使得划分出的滤波器组更精确。模拟信号和实验信号的去噪结果表明:本文使用的方法的信噪比达到16.27,均方根误差达到7.54e-07,相较于小波阈值、经验模态分解和传统EWT具有更好的去噪效果和更高的鲁棒性。It is inevitable for the collected gear vibration signals to contain some noises that can seriously affect the accuracy of the gear fault diagnosis.In the present work,an improved empirical wavelet transform(EWT)combined with wavelet threshold denoisingmethod was proposed.This method improved the traditional EWTfrequency spectrum segmentation method.First,the peak seeking algorithm was used to find the peak of the frequency spectrum,and then the smoothing algorithm was used to smooth the peak curve.The minimum points of the smooth curve were taken as the frequency spectrum segmentation boundaries,so that the divided filter banks were more accurate.The denoising results of simulated signals and experimental signals showed that the signal-to-noise ratio(SNR)of this method reaches 16.27,and the root mean square error(RMSE)reaches 7.54e-07.The proposed method had better denoising effectivity and higher robustness than the wavelet threshold,empirical mode decomposition(EMD)and traditional EWT.

关 键 词：改进的EWT 小波阈值 EMD 齿轮振动信号去噪 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]