Application of Machine Learning Techniques for Bearing Fault Diagnosis

Document Type : Research Paper

Authors
Sarra Eddai 1 , 2
Nabih Feki 1 , 3
Ahmed Ghorbel 1 , 4
Abdelkhalak El Hami 2
Mohamed Haddar 1
1 Laboratory of Mechanics, Modeling, and Production, National School of Engineering of Sfax, University of Sfax, Sfax, Tunisia
2 Normandie Mechanical Laboratory LMN, National Institute of Applied Sciences of Rouen, University of Rouen, Haute Normandie, France
3 Department of Mechanical Engineering, Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, Sousse, Tunisia
4 Department of Mechanical Engineering, Higher Institute of Applied Sciences and Technology of Kairouan, University of Kairouan, Kairouan, Tunisia
10.22055/jacm.2025.48052.4943
Abstract
Machine learning enhances machine diagnostics through advanced data analysis, pattern recognition, and fault prediction. This study investigates the application of machine learning algorithms for bearing fault detection. The objective is to develop intelligent methodologies for the predictive diagnosis of bearing faults in rotating machinery, emphasizing the significance of timely intervention to prevent critical failures. The methodology employed encompasses a systematic approach, including data preprocessing, feature extraction, and model development. This research employs advanced machine learning techniques, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes algorithms, in conjunction with time-domain and frequency-domain feature extraction methods. The implemented approach substantially enhances fault detection accuracy, achieving an aggregate classification precision of 97.8% across all fault categories. Notably, the SVM algorithm demonstrates exceptional performance, attaining a 99.2% accuracy rate in inner-race fault identification. This investigation provides a comprehensive analysis of the Case Western Reserve University (CWRU) dataset, data preprocessing procedures, feature extraction techniques, and machine learning algorithms utilized for fault detection. The results emphasize the effectiveness of these algorithms in bearing fault diagnosis, offering valuable insights for predictive maintenance strategies in industrial applications. This research also aligns with the objectives of Industry 4.0, which focuses on utilizing intelligent, automated systems to enhance factory efficiency and reliability. The study concludes by proposing future research directions to further advance these technologies and support the transition toward more intelligent, interconnected industries.
Keywords
Bearings faults
intelligent diagnosis
machine learning techniques
features extraction
Artificial Intelligence (AI)
Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Journal of Applied and Computational Mechanics
Volume 11, Issue 4
Autumn 2025
Pages 1183-1195