您好,欢迎来到中国测试科技资讯平台!

首页> 数字期刊群 >本期导读>基于峭度原则的VMD-SVD微型电机声音信号降噪方法

基于峭度原则的VMD-SVD微型电机声音信号降噪方法

595    2023-04-20

免费

全文售价

作者:李伟光, 兰钦泓, 马贤武

作者单位:华南理工大学机械与汽车工程学院,广东 广州 510641


关键词:微型电机;声音信号降噪;变分模态分解(VMD);奇异值分解(SVD)


摘要:

微型电机运转时的声音信号包含丰富的状态信息,可用于生产线上电机的快速检测,但由于待测电机体积小、声音能量低,采集过程中声音信号易与环境噪声耦合,导致声音信号提取和检测不准确。该文通过研究电机组成结构,分析声音信号频率成分与成因,得到该文研究电机的声音信号3倍频谐波特点,提出一种基于峭度原则的VMD-SVD算法对电机声音信号进行提纯降噪,该算法采用VMD分段原理,对各分段信号进行SVD分解,提取谐波特征,利用峭度原则优化VMD参数选取。首先通过仿真信号对比实验,验证了该文算法具有更好的降噪效果和降噪性能指标。而后,将该方法应用于微型电机实测声音信号,测试结果表明提出的基于峭度原则VMD-SVD算法具有良好降噪效果,能够显著提高原始信号信噪比,更利于后续特征提取和故障检测工作。


A VMD-SVD micro-motor sound signal noise reduction method based on the kurtosis principle
LI Weiguang, LAN Qinhong, MA Xianwu
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China
Abstract: The sound signal of micro motor is rich in state information, which can be used for the rapid detection of motor on the production line. However, due to the small volume and low sound energy of the motor to be tested, the sound signal is easy to be coupled with the environmental noise in the process of acquisition, which leads to the inaccurate extraction and detection of sound signal. This paper studies the structure of motor, analyzes the frequency components and causes of sound signal, obtains the 3 times harmonic characteristics of the sound signal of the motor, a VMD-SVD algorithm based on the kurtosis principle is proposed to purify and reduce the noise of the motor sound signal. The algorithm uses the VMD segmentation principle to decompose each segmented signal by SVD, extract the harmonic characteristics, and use the kurtosis principle to optimize the selection of VMD parameters. Firstly, the simulation results show that the algorithm has better noise reduction effect and performance index. Then, the method is applied to the sound signal measured by micro motor. The test results show that the VMD-SVD algorithm based on the kurtosis principle proposed in this paper has a good noise reduction effect can significantly improve the signal-to-noise ratio of original signal, and is more conducive to the subsequent feature extraction and fault detection.
Keywords: micro motor;noise reduction of sound signal;variational modal decomposition (VMD);singular value decomposition (SVD)
2023, 49(1):111-118  收稿日期: 2021-03-23;收到修改稿日期: 2021-06-04
基金项目: 国家自然科学基金项目(51875216);广东省自然科学基金项目(2017A050501004);广东省自然资源厅项目(2020030);广东省重点领域研发计划( 2019B090918003)
作者简介: 李伟光(1958-),男,江西永丰县人,教授,博士,研究方向为智能制造、信号处理、故障诊断
参考文献
[1] 易子馗, 谭建平, 刘思思. 基于改进谱减法和MFCC的电机异常噪声识别方法[J]. 微特电机, 2017, 45(2): 31-38
[2] 徐锋, 刘云飞, 宋军. 基于中值滤波-SVD和EMD的声发射信号特征提取[J]. 仪器仪表学报, 2011, 32(12): 2712-2719
[3] 李伟, 姜智通, 张璐莹, 等. 碳纤维复合材料损伤声发射信号模式识别方法[J]. 中国测试, 2020, 46(6): 121-128
[4] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544
[5] 马增强, 张俊甲, 张安, 等. 基于VMD-SVD联合降噪和频率切片小波变换的滚动轴承故障特征提取[J]. 振动与冲击, 2018, 37(17): 210-217
[6] 程珩, 励文艳, 权龙, 等. 基于VMD-MDE和ELM的柱塞泵微弱故障诊断[J]. 振动. 测试与诊断, 2020, 40(4): 635-642+818
[7] 卢莉蓉, 王鉴, 牛晓东. 基于VMD和小波阈值的ECG肌电干扰去噪处理[J]. 传感技术学报, 2020, 33(6): 867-873
[8] 赵学智, 叶邦彦. SVD和小波变换的信号处理效果相似性及其机理分析[J]. 电子学报, 2008, 36(8): 1582-1589
[9] 张景润, 李伟光, 李振, 等. 基于奇异值差分谱理论的大型转子轴心轨迹提纯[J]. 振动与冲击, 2019, 38(4): 199-205
[10] 江志农, 魏东海, 张进杰, 等. 基于VMD和SVD的柴油机气门间隙异常特征提取研究[J]. 振动与冲击, 2020, 39(16): 23-30
[11] 苏燕辰, 王筱野, 靳行. 基于SVD差分谱去噪法分析地铁调车测试噪声[J]. 中国测试, 2019, 45(7): 42-45,65
[12] HESTENES M R. Multiplier and gradient methods[J]. Journal of Optimization Theory & Applications, 1969, 4(5): 303-320
[13] 赵学智, 叶邦彦, 陈统坚. 奇异值差分谱理论及其在车床主轴箱故障诊断中的应用[J]. 机械工程学报, 2010, 46(1): 100-108