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Detecting the weak damped oscillation signal in the agricultural machinery working environment by vibrational resonance in the duffing system

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Abstract

The working environment for agricultural machinery is complex and variable. Some weak characteristic damped oscillation signals are extremely difficult to extract and analyze because of their long-term operation in a strong noise environment. The vibration resonance (VR) phenomenon of a second-order Duffing bistable system driven by a weak characteristic damped oscillation signal and a high-frequency harmonic signal was studied. The results indicate that the cooperation between the Duffing damping ratio and attenuation coefficient can induce the VR occurrence of a small-parameter damped oscillation signal. As a result, the energy of the weak characteristic signal becomes stronger, and the VR numerical processing method of the high-frequency weak characteristic damped oscillation signal is provided. On this basis, aiming at the strong noise of agricultural machinery working, taking the weighted kurtosis index as the objective function and supplemented by variational mode decomposition (VMD) technology, a VR-VMD adaptive method based on quantum particle swarm optimization (QPSO) was proposed to extract the weak characteristic damped oscillation signal. Numerical simulation analysis and experiments show that the proposed VR-VMD method is effective in a strong noise environment for agricultural machinery.

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Abbreviations

VR :

Vibration resonance

VMD :

Variational mode decomposition

f 0 :

Frequency of weak signal

λ :

Attenuation coefficient of weak signal

γ :

Damping ratio of the duffing oscillator

H and f h :

Amplitude and frequency of high-frequency excitation periodic signal

a 0 and b 0 :

Two parameters of the low-frequency system

a and b :

Two parameters of the high-frequency system

d :

Compound influence factor

QPSO :

Quantum particle swarm optimization

Kc :

Weighted kurtosis index

k :

Number of VMD decomposition

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Acknowledgments

This work was supported in part by the Science and Technology Innovation Project of the Chinese Academy of Agricultural Sciences (2015-2021), Sichuan Province Key R&D Plan (2022YFG0079) and Jiangsu Agricultural Science, Technology Innovation & Promotion Fund Project (No.2020-16).

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Authors and Affiliations

  1. Nanjing University of Science & Technology, Nanjing, 210094, China

    Suzhen Wang & Baochun Lu

  2. Nanjing Research Institute for Agriculture Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing, 210014, China

    Suzhen Wang

Authors
  1. Suzhen Wang
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  2. Baochun Lu
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Corresponding author

Correspondence to Suzhen Wang.

Additional information

Suzhen Wang is a doctor of the Nanjing University of Science & Technology, China. She is also a researcher from Nanjing Research Institute for Agriculture Mechanization, Ministry of Agriculture and Rural Affairs, China. Her research interests include vibration signal analysis, driver-less agricultural equipment and agricultural Internet of things technology.

Baochun Lu is a Professor at the Nanjing University of Science & Technology, China. His research interests include intelligent manufacturing and mechanical fault diagnosis.

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Wang, S., Lu, B. Detecting the weak damped oscillation signal in the agricultural machinery working environment by vibrational resonance in the duffing system. J Mech Sci Technol 36, 5925–5937 (2022). https://doi.org/10.1007/s12206-022-1109-3

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  • DOI: https://doi.org/10.1007/s12206-022-1109-3

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