基于DA-VMD联合小波阈值的排种器田间振动信号去噪方法

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

摘要:

播种机作业过程中排种器会受到非平稳随机振动，对排种性能产生较大的影响，对振动信号采集和分析具有重要意义，而田间振动信号采集过程中受到诸多噪声干扰，难以从中获取有效的信息。本文提出了一种结合蜻蜓算法（Dragonfly algorithm，DA）、变分模态分解（Variational mode decomposition，VMD）和小波阈值的去噪方法，可通过不断迭代更新蜻蜓个体位置和速度，寻找使得VMD分解效果最优的参数组合。以仿真得到的时域随机路面信号为初始信号，分别采用DA-VMD联合小波阈值、小波阈值去噪、经验模态分解（Empirical mode decomposition，EMD）、VMD和小波联合EMD方法对其进行去噪处理，结果表明本文所提出的去噪方法对非平稳随机振动信号去噪效果更优，去噪后信噪比、均方根和相关系数分别为21.570、0.094和0.833。采集田间播种过程不同地表和作业速度下排种器振动信号，并采用DA-VMD联合小波阈值去噪方法对其进行去噪处理，以平滑度、信号能量比和噪声模为评价指标分析去噪效果，结果表明本文所提出方法去噪后信号更加平滑，信号能量比和噪声模更高，且在不同工况条件下均具有较好的去噪效果。

Abstract:

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.