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

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

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)...

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Bibliographic Details
Main Authors: Khalid Alqunun, Mohammed Bachir Bechiri, Mohamed Naoui, Abderrahmane Khechekhouche, Ismail Marouani, Tawfik Guesmi, Badr M. Alshammari, Amer AlGhadhban, Abderrahim Allal
Format: Article
Language:English
Published: Nature Portfolio 2025-05-01
Series:Scientific Reports
Subjects:
Bearing fault detection
Continuous wavelet transform
Convolutional neural networks models
Machine learning techniques
Tree-structured Parzen estimators
Fault diagnosis
Online Access:https://doi.org/10.1038/s41598-025-02439-4
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