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Combining Multi-Scale Wavelet Entropy and Kernelized Classification for Bearing Multi-Fault Diagnosis.

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Summary

This study introduces multi-scale wavelet Shannon entropy for improved rolling element bearing fault diagnosis. The proposed methods enhance diagnostic accuracy under varying conditions, outperforming existing techniques.

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Machine Learning

Background:

  • Rolling element bearing failure is critical for rotating machinery reliability.
  • Accurate fault diagnosis under variable conditions remains a challenge.

Purpose of the Study:

  • To propose multi-scale wavelet Shannon entropy for enhanced bearing fault diagnosis.
  • To improve diagnostic accuracy for bearing faults under diverse working conditions.

Main Methods:

  • Integrating stationary wavelet packet transform with dispersion entropy (SWPDE) and permutation entropy (SWPPE).
  • Utilizing kernel extreme learning machine (KELM) classifier for fault diagnosis.
  • Evaluating SWPDE-KELM and SWPPE-KELM against stationary wavelet packet singular value entropy (SWPSVE)-KELM.

Main Results:

  • SWPDE-KELM demonstrated slightly superior diagnostic accuracy compared to SWPPE-KELM.
  • Both SWPDE-KELM and SWPPE-KELM significantly outperformed the SWPSVE-KELM method.
  • The proposed methods show effectiveness in diagnosing bearing failure types and severities.

Conclusions:

  • Multi-scale wavelet Shannon entropy is a promising feature for bearing fault diagnosis.
  • The proposed SWPDE-KELM method offers a robust approach for reliable rotating machinery operation.
  • Enhanced feature extraction significantly improves diagnostic performance in challenging conditions.