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Fault Diagnosis Method for Shearer Arm Gear Based on Improved S-Transform and Depthwise Separable Convolution.

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  • 1School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China.

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Summary

This study introduces an improved S-transform and Depthwise Separable Convolutional Neural Network (DSCNN) for diagnosing shearer arm gear faults. The method enhances time-frequency analysis and achieves high accuracy with reduced training time and parameters.

Keywords:
S-transformdepth separable convolutionshearer rocker arm

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Standard Convolutional Neural Networks (CNNs) face challenges in shearer arm gear fault diagnosis due to parameter redundancy and low training efficiency.
  • Effective time-frequency feature representation is crucial for accurately identifying gear faults.

Purpose of the Study:

  • To propose an improved diagnostic method for shearer arm gear faults using an enhanced S-transform and a Depthwise Separable Convolutional Neural Network (DSCNN).
  • To improve the efficiency and accuracy of gear fault diagnosis by optimizing feature representation and network architecture.

Main Methods:

  • Utilized an improved S-transform for time-frequency analysis, converting 1D vibration signals into 2D time-frequency images.
  • Developed a DSCNN model combining standard and depthwise separable convolutions for fault identification.
  • Compared performance against Wavelet Transform, Fourier Transform, S-transform, and Gramian Angular Field (GAF) using various CNN architectures.

Main Results:

  • Frequency-domain representations significantly outperformed raw time-domain signals in fault diagnosis.
  • Grad-CAM visualization confirmed the model's accurate focus on critical fault features.
  • The proposed DSCNN method achieved high classification accuracy with reduced training time and fewer parameters compared to other CNNs.

Conclusions:

  • The improved S-transform and DSCNN offer a robust and efficient solution for shearer arm gear fault diagnosis.
  • Time-frequency image representations are superior to time-domain signals for this application.
  • The method effectively balances diagnostic accuracy with computational efficiency.