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A Cutting Pattern Recognition Method for Shearers Based on Improved Ensemble Empirical Mode Decomposition and a

Jing Xu1, Zhongbin Wang2, Chao Tan3

  • 1School of Mechatronic Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China. xujingcumt@126.com.

Sensors (Basel, Switzerland)
|November 4, 2015
PubMed
Summary

This study introduces an improved method for recognizing coal shearer cutting patterns using sound analysis. The technique achieves high accuracy, enhancing the stability and automation of coal mining operations.

Keywords:
Improved Ensemble Empirical Mode DecompositionProbabilistic Neural Networkcoal miningcutting pattern recognitionintrinsic mode functionsound signal

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Area of Science:

  • Mining Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Automated coal mining faces require stable shearer operation.
  • Traditional shearer monitoring methods have limitations like size, contact, and low accuracy.
  • Cutting pattern recognition is crucial for optimizing mining processes.

Purpose of the Study:

  • To develop a high-accuracy, high-speed online cutting pattern recognition method for shearers.
  • To improve the stability and automation of coal mining working faces.
  • To overcome limitations of traditional shearer detection methods.

Main Methods:

  • Utilized Improved Ensemble Empirical Mode Decomposition (IEEMD) for sound signal processing.
  • Applied an industrial microphone on the shearer to collect cutting sound data.
  • Implemented end-point continuation and average correlation coefficient for signal refinement.
  • Extracted energy and standard deviation features from intrinsic mode functions (IMFs).
  • Employed Probabilistic Neural Network (PNN) for cutting pattern classification.

Main Results:

  • The IEEMD method effectively processed cutting sound signals, addressing end-point effects and filtering noise.
  • Feature extraction using energy and standard deviation of IMFs proved effective for classification.
  • The proposed IEEMD-PNN method achieved a simulation accuracy of 92.67%.
  • Industrial application validated the method's efficiency and correctness.

Conclusions:

  • The IEEMD-PNN method offers an accurate and fast online solution for shearer cutting pattern recognition.
  • This approach enhances shearer stability and supports the development of automated coal mining.
  • Sound-based recognition provides a non-contact, efficient alternative to traditional detectors.