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Hybrid Diagnostic Framework for Interpretable Bearing Fault Classification Using CNN and Dual-Stage Feature

Mohamed Elhachemi Saouli1,2, Mostefa Mohamed Touba2, Adel Boudiaf3

  • 1LESIA Laboratory of Research, University of Mohamed Khider Biskra, Biskra 07000, Algeria.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid framework for rotary machinery fault diagnosis, combining deep learning with interpretable methods for enhanced accuracy and transparency. The approach achieves 100% classification accuracy on the CWRU bearing dataset, enabling reliable industrial applications.

Keywords:
CNN transfer learningdual-stage feature selectionfault diagnosisinterpretabilityrotary machinerysupervised classification

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Area of Science:

  • Mechanical Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning, especially Convolutional Neural Networks (CNNs), excels in vibration-based fault classification for rotary machinery.
  • Limited interpretability of deep learning models hinders their use in safety-critical industrial settings.
  • Timely fault diagnosis is crucial for system reliability and minimizing downtime.

Purpose of the Study:

  • To develop a hybrid diagnostic framework integrating CNN transfer learning with interpretable supervised classification.
  • To enhance both predictive accuracy and model transparency in fault diagnosis.
  • To provide explainable and reliable fault diagnosis solutions for industrial environments.

Main Methods:

  • A dual-stage feature selection process using Analysis of Variance (ANOVA) and Permutation Feature Importance (PFI) was employed.
  • Deep features were extracted from a pre-trained VGG19 network and refined.
  • SHapley Additive exPlanations (SHAP) were utilized for model interpretability.

Main Results:

  • The proposed framework achieved 100% classification accuracy on the Case Western Reserve University (CWRU) bearing dataset.
  • The dual-stage feature selection effectively reduced dimensionality and improved classification performance.
  • SHAP analysis provided insights into influential features driving fault classification.

Conclusions:

  • The hybrid framework successfully combines high performance with transparent decision-making for fault diagnosis.
  • The approach demonstrates strong potential for explainable and reliable fault diagnosis in industrial settings.
  • Integrating interpretable methods with deep learning enhances the practical applicability of AI in machinery diagnostics.