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A Lightweight and Small Sample Bearing Fault Diagnosis Algorithm Based on Probabilistic Decoupling Knowledge

Hao Luo1, Tongli Ren1, Ying Zhang1

  • 1College of Information, Liaoning University, Shenyang 110036, China.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
Summary

This study introduces a lightweight bearing fault diagnosis algorithm using probabilistic decoupling knowledge distillation and meta-learning. It effectively diagnoses faults with minimal data, addressing limitations of traditional deep learning methods.

Keywords:
fault diagnosisknowledge distillationlightweightmeta-learningsmall sample

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Rolling bearings are critical in industrial machinery, and their failures cause significant disruptions.
  • Traditional deep learning fault diagnosis requires extensive data and computational resources, hindering deployment in constrained environments.
  • Developing efficient, small-sample fault diagnosis algorithms is essential for industrial applications.

Purpose of the Study:

  • To propose a lightweight and deployable bearing fault diagnosis algorithm suitable for small sample scenarios.
  • To address the computational and data limitations of existing deep learning-based fault diagnosis methods.
  • To enhance the performance and rapid convergence of diagnostic models with limited data.

Main Methods:

  • Utilized Model-Agnostic Meta-Learning (MAML) for efficient teacher model parameter initialization and training.
  • Employed a novel probability-based decoupled knowledge distillation to transfer knowledge from a teacher to a student model.
  • Leveraged meta-training on the Paderborn University dataset and validation on Case Western Reserve University and laboratory datasets.

Main Results:

  • The proposed MIX-MPDKD algorithm demonstrated effective performance in small sample bearing fault diagnosis.
  • The lightweight student model achieved rapid convergence and satisfactory accuracy, outperforming traditional methods in data-scarce conditions.
  • The approach successfully addressed the challenge of deploying complex deep learning models in resource-constrained settings.

Conclusions:

  • The MIX-MPDKD algorithm offers a viable solution for efficient and accurate bearing fault diagnosis with limited data.
  • This method significantly reduces computational requirements, making it suitable for real-world industrial deployments.
  • The integration of meta-learning and knowledge distillation provides a robust framework for developing adaptable and performant diagnostic systems.