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A deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition.

Qingjun Song1, Shirong Sun1, Qinghui Song1

  • 1College of Intelligent Equipment, Shandong University of Science and Technology, Taian, 271000, Shandong, China.

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Summary

This study introduces a novel multi-scale convolutional neural network (MCNN-BILSTM) for accurate coal-gangue recognition in noisy mining environments. The method enhances robustness and adaptability for industrial applications.

Keywords:
Coal–gangue recognitionMulti-scale parallel neural networkattention mechanismvibration signal

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Area of Science:

  • Mining Engineering
  • Artificial Intelligence
  • Signal Processing

Background:

  • Coal-gangue recognition is crucial for intelligent mining and coal quality.
  • Existing methods struggle with dust and noise, limiting industrial use.
  • Accurate recognition in harsh environments remains a challenge.

Purpose of the Study:

  • To develop a robust coal-gangue recognition method for noisy industrial settings.
  • To improve the accuracy and stability of coal-gangue identification systems.
  • To enhance the intelligent realization of integrated working faces.

Main Methods:

  • An end-to-end multi-scale feature fusion convolutional neural network (MCNN-BILSTM) was proposed.
  • Vibration signals were analyzed using multi-scale learning and attention mechanisms.
  • Traditional filtering methods were combined with deep learning.

Main Results:

  • The MCNN-BILSTM method demonstrated strong adaptability and robustness.
  • The approach showed significant noise resistance in complex environments.
  • Experimental validation was performed on a coal-gangue impact hydraulic support platform.

Conclusions:

  • The proposed MCNN-BILSTM method is suitable for complex practical industrial sites.
  • The technique effectively overcomes limitations of existing coal-gangue recognition systems.
  • This advancement contributes to safer and more efficient coal mining operations.