Volume 49 Issue 1
Feb.  2023
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Article Navigation >  Journal of Mine Automation  > 2023  >  49(1): 63-72
WANG Houchao, NIU Qiang, CHEN Pengpeng, et al. Fusion denoising algorithm for vibration signal of mine hoist with low signal-to-noise ratio[J]. Journal of Mine Automation，2023，49(1)：63-72.  doi: 10.13272/j.issn.1671-251x.18019
Citation: WANG Houchao, NIU Qiang, CHEN Pengpeng, et al. Fusion denoising algorithm for vibration signal of mine hoist with low signal-to-noise ratio[J]. Journal of Mine Automation，2023，49(1)：63-72.  doi: 10.13272/j.issn.1671-251x.18019
shu

Fusion denoising algorithm for vibration signal of mine hoist with low signal-to-noise ratio

doi: 10.13272/j.issn.1671-251x.18019

  • School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China

  • Received Date: 2022-08-25
  • Rev Recd Date: 2023-01-03
    • Available Online: 2023-01-11
    Abstract
  • Aiming at the nonlinear and low signal-to-noise ratio characteristics of mine hoist vibration signal in complex environments, a mine hoist vibration signal fusion denoising algorithm based on complete EEMD with adaptive noise (CEEMDAN) and adaptive wavelet threshold is proposed. Firstly, the CEEMDAN algorithm is used to decompose the noisy mine hoist vibration signal to obtain the intrinsic mode component (IMF) and the residual. The IMF component is judged for high and low frequency. The t-test method is used to test whether the mean value is significantly different from 0. The IMF component which tends to 0 is the high-frequency component, and the IMF component which is significantly different from 0 is the low-frequency component. Secondly, the appropriate wavelet basis function and decomposition level are selected. The high-frequency IMF component is denoised by using the adaptive wavelet threshold method. Finally, the processed high-frequency IMF components and the unprocessed low-frequency IMF components are reconstructed with the residuals to obtain the de-noised vibration signal from the fusion algorithm. The CEEMDAN denoising method, CEEMD-wavelet threshold combined denoising method, CEEMDAN-wavelet threshold combined denoising method and CEEMDAN-adaptive wavelet threshold fusion denoising method are used to denoise the simulated signal respectively. The results show the following points. ① The signal denoised by the CEEMDAN-adaptive wavelet threshold fusion denoising method is similar to the original signal in local waveform features and signal peak values. Some features of the signal waveform have been restored well. The feature information of the original signal has been well preserved in the process of denoising. ② The composite evaluation index H is used as the objective evaluation standard. The H value of the CEEMDAN-adaptive wavelet threshold fusion denoising method is the smallest. This shows that the denoising effect of the fusion denoising algorithm for the simulation signal is better than that of other denoising methods. The experiment is carried out on the running mine hoist in a mine in Heilongjiang Province. The results show the following points. ① The db4 wavelet basis function is used to decompose the noisy IMF component in four layers. The signal de-noised by CEEMDAN-adaptive wavelet threshold fusion de-noising method is smooth. Some waveform features of the signal have also been restored well. While removing the noise, the feature information of the original signal has been retained to the greatest extent. ② In the actual mine hoist vibration signal denoising process, the CEEMDAN-adaptive wavelet threshold fusion denoising method has the smallest H value and the best denoising effect.

     

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