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)...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-02439-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850242576619143168 |
|---|---|
| author | Khalid Alqunun Mohammed Bachir Bechiri Mohamed Naoui Abderrahmane Khechekhouche Ismail Marouani Tawfik Guesmi Badr M. Alshammari Amer AlGhadhban Abderrahim Allal |
| author_facet | Khalid Alqunun Mohammed Bachir Bechiri Mohamed Naoui Abderrahmane Khechekhouche Ismail Marouani Tawfik Guesmi Badr M. Alshammari Amer AlGhadhban Abderrahim Allal |
| author_sort | Khalid Alqunun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e49f71c38bd34b9d9d26fb657502ffbe |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e49f71c38bd34b9d9d26fb657502ffbe2025-08-20T02:00:14ZengNature PortfolioScientific Reports2045-23222025-05-0115111510.1038/s41598-025-02439-4An efficient bearing fault detection strategy based on a hybrid machine learning techniqueKhalid Alqunun0Mohammed Bachir Bechiri1Mohamed Naoui2Abderrahmane Khechekhouche3Ismail Marouani4Tawfik Guesmi5Badr M. Alshammari6Amer AlGhadhban7Abderrahim Allal8Department of Electrical Engineering, College of Engineering, University of Ha’ilLaboratory of New Technologies and Local Development, University of El OuedResearch Unit of Energy Processes Environment and Electrical Systems, National Engineering School of Gabes, University of GabesFaculty of Technology, University of El OuedDepartment of Electronics Engineering, Applied College, University of Ha’ilDepartment of Electrical Engineering, College of Engineering, University of Ha’ilDepartment of Electrical Engineering, College of Engineering, University of Ha’ilDepartment of Electrical Engineering, College of Engineering, University of Ha’ilDepartment of Electrical Engineering, University of El OuedAbstract 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.https://doi.org/10.1038/s41598-025-02439-4Bearing fault detectionContinuous wavelet transformConvolutional neural networks modelsMachine learning techniquesTree-structured Parzen estimatorsFault diagnosis |
| spellingShingle | Khalid Alqunun Mohammed Bachir Bechiri Mohamed Naoui Abderrahmane Khechekhouche Ismail Marouani Tawfik Guesmi Badr M. Alshammari Amer AlGhadhban Abderrahim Allal An efficient bearing fault detection strategy based on a hybrid machine learning technique Scientific Reports Bearing fault detection Continuous wavelet transform Convolutional neural networks models Machine learning techniques Tree-structured Parzen estimators Fault diagnosis |
| title | An efficient bearing fault detection strategy based on a hybrid machine learning technique |
| title_full | An efficient bearing fault detection strategy based on a hybrid machine learning technique |
| title_fullStr | An efficient bearing fault detection strategy based on a hybrid machine learning technique |
| title_full_unstemmed | An efficient bearing fault detection strategy based on a hybrid machine learning technique |
| title_short | An efficient bearing fault detection strategy based on a hybrid machine learning technique |
| title_sort | efficient bearing fault detection strategy based on a hybrid machine learning technique |
| topic | Bearing fault detection Continuous wavelet transform Convolutional neural networks models Machine learning techniques Tree-structured Parzen estimators Fault diagnosis |
| url | https://doi.org/10.1038/s41598-025-02439-4 |
| work_keys_str_mv | AT khalidalqunun anefficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT mohammedbachirbechiri anefficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT mohamednaoui anefficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT abderrahmanekhechekhouche anefficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT ismailmarouani anefficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT tawfikguesmi anefficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT badrmalshammari anefficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT ameralghadhban anefficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT abderrahimallal anefficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT khalidalqunun efficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT mohammedbachirbechiri efficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT mohamednaoui efficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT abderrahmanekhechekhouche efficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT ismailmarouani efficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT tawfikguesmi efficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT badrmalshammari efficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT ameralghadhban efficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique AT abderrahimallal efficientbearingfaultdetectionstrategybasedonahybridmachinelearningtechnique |