Application of Machine Learning Techniques for Bearing Fault Diagnosis
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 diagno...
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| Format: | Article |
| Language: | English |
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Shahid Chamran University of Ahvaz
2025-10-01
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| Series: | Journal of Applied and Computational Mechanics |
| Subjects: | |
| Online Access: | https://jacm.scu.ac.ir/article_19459_d1b9641a9bbcf36bbc8274c079a72525.pdf |
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| _version_ | 1839626341340676096 |
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| author | Sarra Eddai Nabih Feki Ahmed Ghorbel Abdelkhalak El Hami Mohamed Haddar |
| author_facet | Sarra Eddai Nabih Feki Ahmed Ghorbel Abdelkhalak El Hami Mohamed Haddar |
| author_sort | Sarra Eddai |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9bd2910e77a144f79a3f858a1c2cac17 |
| institution | Matheson Library |
| issn | 2383-4536 |
| language | English |
| publishDate | 2025-10-01 |
| publisher | Shahid Chamran University of Ahvaz |
| record_format | Article |
| series | Journal of Applied and Computational Mechanics |
| spelling | doaj-art-9bd2910e77a144f79a3f858a1c2cac172025-07-17T16:47:37ZengShahid Chamran University of AhvazJournal of Applied and Computational Mechanics2383-45362025-10-011141183119510.22055/jacm.2025.48052.494319459Application of Machine Learning Techniques for Bearing Fault DiagnosisSarra Eddai0Nabih Feki1Ahmed Ghorbel2Abdelkhalak El Hami3Mohamed Haddar4Laboratory of Mechanics, Modeling, and Production, National School of Engineering of Sfax, University of Sfax, Sfax, TunisiaLaboratory of Mechanics, Modeling, and Production, National School of Engineering of Sfax, University of Sfax, Sfax, TunisiaLaboratory of Mechanics, Modeling, and Production, National School of Engineering of Sfax, University of Sfax, Sfax, TunisiaNormandie Mechanical Laboratory LMN, National Institute of Applied Sciences of Rouen, University of Rouen, Haute Normandie, FranceLaboratory of Mechanics, Modeling, and Production, National School of Engineering of Sfax, University of Sfax, Sfax, TunisiaMachine 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.https://jacm.scu.ac.ir/article_19459_d1b9641a9bbcf36bbc8274c079a72525.pdfbearings faultsintelligent diagnosismachine learning techniquesfeatures extractionartificial intelligence (ai) |
| spellingShingle | Sarra Eddai Nabih Feki Ahmed Ghorbel Abdelkhalak El Hami Mohamed Haddar Application of Machine Learning Techniques for Bearing Fault Diagnosis Journal of Applied and Computational Mechanics bearings faults intelligent diagnosis machine learning techniques features extraction artificial intelligence (ai) |
| title | Application of Machine Learning Techniques for Bearing Fault Diagnosis |
| title_full | Application of Machine Learning Techniques for Bearing Fault Diagnosis |
| title_fullStr | Application of Machine Learning Techniques for Bearing Fault Diagnosis |
| title_full_unstemmed | Application of Machine Learning Techniques for Bearing Fault Diagnosis |
| title_short | Application of Machine Learning Techniques for Bearing Fault Diagnosis |
| title_sort | application of machine learning techniques for bearing fault diagnosis |
| topic | bearings faults intelligent diagnosis machine learning techniques features extraction artificial intelligence (ai) |
| url | https://jacm.scu.ac.ir/article_19459_d1b9641a9bbcf36bbc8274c079a72525.pdf |
| work_keys_str_mv | AT sarraeddai applicationofmachinelearningtechniquesforbearingfaultdiagnosis AT nabihfeki applicationofmachinelearningtechniquesforbearingfaultdiagnosis AT ahmedghorbel applicationofmachinelearningtechniquesforbearingfaultdiagnosis AT abdelkhalakelhami applicationofmachinelearningtechniquesforbearingfaultdiagnosis AT mohamedhaddar applicationofmachinelearningtechniquesforbearingfaultdiagnosis |