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A Rolling Bearing Fault Classification Scheme Based on k-Optimized Adaptive Local Iterative Filtering and Improved

Yi Zhang1,2, Yong Lv1,2, Mao Ge1,2

  • 1Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China.

Entropy (Basel, Switzerland)
|February 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel rolling bearing fault detection technique using k-optimized adaptive local iterative filtering (ALIF) and improved multiscale permutation entropy (MPE) for accurate fault identification in mechanical systems.

Keywords:
BP neural networkfault classificationimproved multiscale permutation entropy (improved MPE)k-optimized adaptive local iterative filtering (ALIF)permutation entropy (PE)

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Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Rolling bearing health is critical for mechanical system operation.
  • Bearing failures often result in nonlinear and non-stationary time series data.
  • Accurate fault detection is essential for predictive maintenance.

Purpose of the Study:

  • To propose a robust rolling bearing fault detection technique.
  • To enhance the accuracy of fault characteristic extraction from complex time series.
  • To enable automatic classification and identification of various fault types and severities.

Main Methods:

  • Adaptive local iterative filtering (ALIF) optimized by permutation entropy (PE) for adaptive layer selection.
  • A completely average coarse-graining method to enhance information extraction.
  • Improved multiscale permutation entropy (improved MPE) for analyzing intrinsic mode functions (IMFs).
  • Backpropagation (BP) neural network for fault identification using extracted features.

Main Results:

  • The improved MPE effectively extracts deep information from time series, yielding stable entropy values.
  • k-optimized ALIF decomposes rolling bearing data into IMFs for analysis.
  • The proposed method accurately extracts fault features and classifies different fault modes and degrees.
  • Simulation and experimental results validate the effectiveness of the technique.

Conclusions:

  • The integrated approach of k-optimized ALIF, improved MPE, and BP neural network offers effective rolling bearing fault detection.
  • This method demonstrates significant potential for real-world applications in bearing fault identification and diagnosis.
  • The technique successfully addresses the challenges posed by nonlinear and non-stationary fault signals.