An efficient bearing fault detection strategy based on a hybrid machine learning technique.

Journal: Scientific reports
Published Date: May 28, 2025

Abstract

This study introduces an innovative method for addressing the bearing fault detection problem in rotating machinery. The proposed approach integrates multi-feature extraction, advanced feature selection, and state-of-the-art classification techniques using convolutional neural network (CNN) models. Leveraging the comprehensive Fault Bearing Dataset from Case Western Reserve University (CWRU), continuous wavelet transforms (CWT) and CNNs are utilized for feature extraction. The methodology also incorporates machine learning model tuning through Tree-Structured Parzen Estimators (TPE) for optimal hyperparameter adjustment, ensuring high-performance classification. Experimental results, based on the ResNet-50-SVM hybrid model, showed the effectiveness of the proposed approach in achieving an impressive accuracy of 95.51%. This confirms that the proposed methodology represents a significant advancement in bearing fault detection, providing an effective solution for predictive and preventive maintenance in industrial applications.

Authors

  • Khalid Alqunun
    Department of Electrical Engineering, College of Engineering, University of Ha'il, 2240, Ha'il, Saudi Arabia.
  • Mohammed Bachir Bechiri
    Laboratory of New Technologies and Local Development, University of El Oued, 39000, El Oued, Algeria.
  • Mohamed Naoui
    Research Unit of Energy Processes Environment and Electrical Systems, National Engineering School of Gabes, University of Gabes, 6029, Gabes, Tunisia.
  • Abderrahmane Khechekhouche
    Faculty of Technology, University of El Oued, 39000, El Oued, Algeria.
  • Ismail Marouani
    Department of Electronics Engineering, Applied College, University of Ha'il, 2440, Ha'il, Saudi Arabia.
  • Tawfik Guesmi
    Department of Electrical Engineering, College of Engineering, University of Ha'il, 2240, Ha'il, Saudi Arabia. tawfik.guesmi@istmt.rnu.tn.
  • Badr M Alshammari
    Department of Electrical Engineering, College of Engineering, University of Ha'il, 2240, Ha'il, Saudi Arabia.
  • Amer AlGhadhban
    Department of Electrical Engineering, College of Engineering, University of Ha'il, 2240, Ha'il, Saudi Arabia.
  • Abderrahim Allal
    Department of Electrical Engineering, University of El Oued, El Oued, Algeria.

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