Denoising Method for Seed Feeder Field Vibration Signal Based on Combination of DA - VMD and Wavelet Thresholding

被引:0
作者
Liu, Zhengdao [1 ]
Ma, Zhuanghong [1 ]
Zhang, Junchang [1 ]
Yan, Xiaoli [1 ]
Huang, Yuxiang [1 ]
Zhang, Zhiqiang [1 ]
机构
[1] College of Mechanical and Electronic Engineering, Northwest A&F University, Shaanxi, Yangling
来源
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2024年 / 55卷 / 11期
关键词
DA-VMD; denoising; seeding device; vibration; wavelet threshold;
D O I
10.6041/j.issn.1000-1298.2024.11.027
中图分类号
学科分类号
摘要
During the operation of the seeder, the discharge device will experience non-stationary random vibration, which significantly affects seed discharge performance and holds great importance for acquiring and analyzing vibration signals. A denoising method was proposed that combined dragonfly algorithm (DA), variational mode decomposition (VMD), and wavelet threshold to continuously update the location and speed of dragonfly individuals through iterative processes. The optimal parameter combination for VMD decomposition effect was determined. A simulated random road signal in the time domain served as the initial signal and underwent denoising by using DA-VMD combined wavelet threshold, wavelet threshold denoising, empirical mode decomposition (EMD), VMD, and wavelet combined EMD methods respectively. The results demonstrated that the proposed method achieved superior denoising effects on non-stationary random vibration signals with post-denoising signal-to-noise ratio, root-mean-square value, and correlation number measuring 21.570, 0.094, and 0.833, respectively. Furthermore, vibration signals from seeders under different surface conditions and operating speeds during field seeding were collected and subjected to denoising by using the DA-VMD combined wavelet threshold denoising method. The effectiveness of denoising was evaluated based on smoothness index, signal energy ratio, and noise mode indices. The results indicated smoother signals with higher signal energy ratios after denoising across various working conditions. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.
引用
收藏
页码:262 / 272
页数:10
相关论文
共 31 条
[1]  
SHAO Xuedong, YANG Zihan, SONG Zhenghe, Et al., Analysis of influence of tractor rotary tillage load on power take-off driveline [J], Transactions of the Chinese Society for Agricultural Machinery, 53, pp. 332-339, (2022)
[2]  
ZHENG Juan, LIAO Yitao, QI Tianxiang, Effect of vibration on performance of pneumatic rapeseed precision metering device [J], Journal of Huazhong Agricultural University, 42, 2, pp. 233-242, (2023)
[3]  
CHEN Xing, HE Zhitao, JI Jiangtao, Et al., Influence of low frequency vibration signal on maize seeding monitoring system[J], Journal of Agricultural Mechanization Research, 44, 11, pp. 197-200, (2022)
[4]  
WANG Xiaoyong, YU Zhi, NI Dejiang, Optimization design and experiment of reciprocating tea vibrating-sifting machine, Transactions of the Chinese Society for Agricultural Machinery, 54, 9, pp. 143-153, (2023)
[5]  
SHANG Shuqi, LI Ghengpeng, HE Xiaoning, Et al., Design and experiment of high-acid apple vibrating picker [J], Transactions of the Chinese Society for Agricultural Machinery, 54, 3, pp. 115-125, (2023)
[6]  
DING Hantao, CHEN Shuren, ZHOU Weiwei, Et al., Mechanism analysis of combine harvester's vibration characteristics under feeding interference [J], Transactions of the Chinese Society for Agricultural Machinery, 53, 2, pp. 20-27, (2022)
[7]  
ZHANG Dongxing, YU Tiancheng, YANG Li, Et al., Design and experiment of embryo side orientation device of maize seed based on vibration sorting[J], Transactions of the Chinese Society for Agricultural Machinery, 53, 7, pp. 122-131, (2022)
[8]  
ZHOU X, WANG X, WANG H, Et al., Method for denoising the vibration signal of rotating machinery through VMD and MODWPT, Sensors, 23, 15, (2023)
[9]  
ZHOU T, ZHANG G, LU N, Et al., A rotating machinery fault feature extraction approach based on an adaptive wavelet denoising method and synthetic detection index[J], Measurement Science and Technology, 34, 7, (2023)
[10]  
OLALERE I, OLANREWAJU 0 A., Tool and workpiece condition classification using empirical mode decomposition (EMD) with Hilbert-Huang Transform (HHT) of vibration signals and machine learning models [J], Applied Sciences, 13, 4, (2023)