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Research on scraper conveyor load prediction method based on wavelet transform and BP neural network.

Dan Zhang1, Jiafeng Qin2, Weidong Wu1

  • 1School of Mechanical Engineering, Heilongjiang University of Science &Technology, Harbin, 150022, China.

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Summary

Accurate scraper conveyor load prediction is vital for coal mining operations. A new BP neural network model combined with wavelet transform significantly improves prediction accuracy, reducing errors by over 13%.

Keywords:
BP neural networkLoad predictionTime series predictionWavelet transform

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Area of Science:

  • Mining Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Scraper conveyor load prediction is essential for optimizing coal mining operations and enabling cooperative speed regulation between mining machinery.
  • The inherent nonlinearity and non-smoothness of scraper conveyor loads, caused by unpredictable coal fall, present significant prediction challenges.

Purpose of the Study:

  • To develop an accurate prediction model for scraper conveyor load by analyzing motor current.
  • To enhance the prediction capabilities for cooperative speed regulation in coal mining machinery.

Main Methods:

  • A Backpropagation (BP) neural network model was developed to map motor load to current.
  • Wavelet transform was employed for the decomposition and reconstruction of historical scraper conveyor current data.
  • Time series prediction was performed on both original and reconstructed data samples.

Main Results:

  • The BP neural network model integrated with wavelet decomposition demonstrated superior prediction accuracy compared to using original data.
  • Key error metrics were significantly reduced: root mean square error by 13.26%, average absolute error by 14.19%, and percentage error by 17.43%.
  • The enhanced model meets the stringent accuracy requirements for scraper conveyor load prediction.

Conclusions:

  • The proposed wavelet-decomposed BP neural network model offers a robust solution for scraper conveyor load prediction.
  • This improved prediction accuracy provides a crucial theoretical foundation for the cooperative speed regulation of coal mining machines and scraper conveyors.
  • The findings contribute to more efficient and stable underground coal mining operations.