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)...

Full description

Saved in:

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
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

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

Similar Items