Denoising of the kinetic parameters of a RV reducer based on an EMD algorithm

被引：0

作者：

Song L. ^{[1
]}

You D. ^{[2
]}

Zheng Z. ^{[1
]}

Zhou Y. ^{[3
]}

Chen L. ^{[1
]}

机构：

[1] School of Mechanical and Electrical Engineering, Guangdong Polytechnic Normal University, Guangzhou

[2] School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou

[3] Institute of Automotive Engineering Research, Guangzhou Automobile Group Co., Ltd., Guangzhou

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 18期

关键词：

consecutive mean square error (CMSE); empirical mode decomposition (EMD); l2-norm; rotate vector (RV) reducer; signal denoising;

D O I：

10.13465/j.cnki.jvs.2022.18.034

中图分类号：

学科分类号：

摘要：

The rotate vector(RV) reducer is one of key transmission mechanisms in modern intelligent equipment. There is usually a great proportion of noise in the measurement signals of its kinetic parameters, which affects the operation accuracy and stability of equipments. A signal-denoising approach based on EMD (empirical mode decomposition) was proposed, which can extract accuratly kinetic parameter signals of the RV reducer effectively. In the approach, the IMFs (intrinsic mode functions) derived from EMD were divided into 3 parts, namely, noise IMFs, noise and information, mixed IMFs and information IMFs, with 2 indexes, CMSE (consecutive mean square error) and l2-norm. Different processing strategies were applied in the 3 parts of IMFs and combining with the PR (part reconstruction) to fulfill the process of denoising. The denoising approach presented has been used in the denoising process of the torque signals of a RV40E reducer. The results show that the SNRs of the denoised signals are improved observably, and the effectiveness of the approach is validated. © 2022 Chinese Vibration Engineering Society. All rights reserved.

引用

下载

页码：266 / 272+290

共 17 条

[11] HUANG N E., A new method for nonlinear and nonstationary time series: empirical mode decomposition and Hilbert spectral analysis [J], Proceedings of SPIE-The International Society for Optical Engineering, 4056, pp. 197-209, (2000)
[12] KOPSINIS Y, MCLAUGHLIN S., Development of EMD-based denoising methods inspired by wavelet thresholding, IEEE Transactions on Signal Processing, 57, 4, pp. 1351-1362, (2009)
[13] YU Teng, HU Wusheng, WU Jie, Et al., Extraction of Runyang Bridge vibration information based on a fusion method of wavelet threshold denoising and EMD decomposition[J], Journal of Vibration and Shock, 38, 12, pp. 264-270, (2019)
[14] BOUDRAA A O, CEXUS J C., EMD-based signal filtering [J], IEEE Transactions on Instrumentation & Measurement, 56, 6, pp. 2196-2202, (2007)
[15] KOMATY A, BOUDRAA A, DARE D., Emd-based filtering using the Hausdorff distance, IEEE International Symposium on Signal Processing & Information Technology, (2013)
[16] PENG Yuanning, YAO Entao, SHI Yu, Et al., Denoising method of audio signal of fan blade based on improved EMD and CMSE [J], Electronic Measurement Technology, 2, pp. 93-100, (2018)
[17] KOMATY A, BOUDRAA A O, AUGIER B, Et al., EMD-based filtering using similarity measure between probability density functions of IMFs [J], IEEE Transactions on Instrumentation and Measurement, 63, 1, pp. 27-34, (2014)

← 1 2 →