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Frame vibration states identification for corn harvester based on joint improved empirical mode decomposition -

Jun Fu1,2,3, Chao Chen1,2, Rongqiang Zhao1,2

  • 1College of Biological and Agricultural Engineering, Jilin University, Changchun, China.

Frontiers in Plant Science
|March 31, 2023
PubMed
Summary

This study introduces a novel method to identify corn harvester frame vibration states. The improved Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) model achieved 99.21% accuracy, enhancing machinery reliability.

Keywords:
decrease noiseframe vibrationlow order vibrationreliability of harvestervibration frequency

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Area of Science:

  • Agricultural Engineering
  • Mechanical Vibrations
  • Signal Processing

Background:

  • Corn harvester frames experience significant vibration, leading to bending and torsional deformation.
  • Field road conditions and operational fluctuations challenge machinery reliability.

Purpose of the Study:

  • To develop and validate a method for identifying corn harvester frame vibration states.
  • To enhance the understanding of vibration mechanisms affecting machinery performance.

Main Methods:

  • Utilized an improved Empirical Mode Decomposition (EMD) algorithm to denoise non-stationary vibration signals.
  • Employed a Support Vector Machine (SVM) model for classifying vibration states under various working conditions.

Main Results:

  • The improved EMD algorithm effectively reduced noise and preserved essential signal information.
  • The integrated EMD-SVM method achieved a high accuracy of 99.21% in identifying frame vibration states.
  • Observed that corn ears absorb high-order vibrations, while being less sensitive to low-order ones.

Conclusions:

  • The proposed EMD-SVM method offers a robust solution for accurate vibration state identification in corn harvesters.
  • This approach has the potential to significantly improve frame safety and overall machinery reliability.