Novel Machine Learning Techniques for Classification of Rolling Bearings
Rolling bearing faults frequently cause rotating equipment failure, leading to costly downtime and maintenance expenses. As a result, researchers have focused on developing effective methods for diagnosing these faults. In this paper, we explore the potential of Machine Learning (ML) techniques for...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10604807/ |
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| author | Quynh Nguyen Xuan Phan Tuan Minh Le Hieu Minh Tran Ly van Tran Son Vu Truong Dao |
| author_facet | Quynh Nguyen Xuan Phan Tuan Minh Le Hieu Minh Tran Ly van Tran Son Vu Truong Dao |
| author_sort | Quynh Nguyen Xuan Phan |
| collection | DOAJ |
| description | Rolling bearing faults frequently cause rotating equipment failure, leading to costly downtime and maintenance expenses. As a result, researchers have focused on developing effective methods for diagnosing these faults. In this paper, we explore the potential of Machine Learning (ML) techniques for classifying the health status of bearings. Our approach involves decomposing the signal, extracting statistical features, and using a feature selection employing Binary Grey Wolf Optimization. We then employ four different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF) to diagnose faults based on the reduced set of features. To evaluate the performance of our methods, we utilize several performance indicators. Our results demonstrate that four Machine Learning methods can achieve a high-accuracy fault classification result of 99.85%, better than state-of-the-art methods, highlighting their potential for use in predictive maintenance applications. |
| format | Article |
| id | doaj-art-4f13c72fc8ed423ca59272e3f0f49e14 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4f13c72fc8ed423ca59272e3f0f49e142024-12-04T00:01:35ZengIEEEIEEE Access2169-35362024-01-011217686317687910.1109/ACCESS.2024.343104010604807Novel Machine Learning Techniques for Classification of Rolling BearingsQuynh Nguyen Xuan Phan0https://orcid.org/0009-0009-5540-7020Tuan Minh Le1https://orcid.org/0000-0002-5917-3091Hieu Minh Tran2https://orcid.org/0000-0002-5541-2577Ly van Tran3https://orcid.org/0000-0001-7621-7511Son Vu Truong Dao4https://orcid.org/0000-0001-9281-4869School of Industrial Engineering and Management, International University, Vietnam National University at Ho Chi Minh City, Ho Chi Minh City, VietnamSchool of Electrical Engineering, International University, Vietnam National University at Ho Chi Minh City, Ho Chi Minh City, VietnamSchool of Electrical Engineering, International University, Vietnam National University at Ho Chi Minh City, Ho Chi Minh City, VietnamSchool of Industrial Engineering and Management, International University, Vietnam National University at Ho Chi Minh City, Ho Chi Minh City, VietnamSchool of Industrial Engineering and Management, International University, Vietnam National University at Ho Chi Minh City, Ho Chi Minh City, VietnamRolling bearing faults frequently cause rotating equipment failure, leading to costly downtime and maintenance expenses. As a result, researchers have focused on developing effective methods for diagnosing these faults. In this paper, we explore the potential of Machine Learning (ML) techniques for classifying the health status of bearings. Our approach involves decomposing the signal, extracting statistical features, and using a feature selection employing Binary Grey Wolf Optimization. We then employ four different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF) to diagnose faults based on the reduced set of features. To evaluate the performance of our methods, we utilize several performance indicators. Our results demonstrate that four Machine Learning methods can achieve a high-accuracy fault classification result of 99.85%, better than state-of-the-art methods, highlighting their potential for use in predictive maintenance applications.https://ieeexplore.ieee.org/document/10604807/Fault diagnosisfeature selectiongrey wolf optimizationmachine learningrolling bearing |
| spellingShingle | Quynh Nguyen Xuan Phan Tuan Minh Le Hieu Minh Tran Ly van Tran Son Vu Truong Dao Novel Machine Learning Techniques for Classification of Rolling Bearings IEEE Access Fault diagnosis feature selection grey wolf optimization machine learning rolling bearing |
| title | Novel Machine Learning Techniques for Classification of Rolling Bearings |
| title_full | Novel Machine Learning Techniques for Classification of Rolling Bearings |
| title_fullStr | Novel Machine Learning Techniques for Classification of Rolling Bearings |
| title_full_unstemmed | Novel Machine Learning Techniques for Classification of Rolling Bearings |
| title_short | Novel Machine Learning Techniques for Classification of Rolling Bearings |
| title_sort | novel machine learning techniques for classification of rolling bearings |
| topic | Fault diagnosis feature selection grey wolf optimization machine learning rolling bearing |
| url | https://ieeexplore.ieee.org/document/10604807/ |
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