Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings
This paper investigated the implementation of machine learning algorithms for health monitoring and fault detection of tapered roller bearings (TRBs) (30205 J2/Q, 30206 J2/Q and 30207 J2/Q). Three defect models were considered: inner race defect, outer race defect and roller defect, in addition to d...
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| Language: | English |
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Balkan Scientific Centre
2025-06-01
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| Series: | Tribology and Materials |
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| Online Access: | https://www.tribomat.net/archive/2025/2025-02/TM-2025-02-05.pdf |
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| author | Harshal Aher Nilesh Ghuge |
| author_facet | Harshal Aher Nilesh Ghuge |
| author_sort | Harshal Aher |
| collection | DOAJ |
| description | This paper investigated the implementation of machine learning algorithms for health monitoring and fault detection of tapered roller bearings (TRBs) (30205 J2/Q, 30206 J2/Q and 30207 J2/Q). Three defect models were considered: inner race defect, outer race defect and roller defect, in addition to data from the healthy bearings condition. An L27 orthogonal array design was used to generate a comprehensive dataset for each defect model, considering various operational parameters such as load, unbalance, defect type, bearing type and speed. Kurtosis was extracted as the sole feature from the vibration signals for fault classification. Several machine learning models, including artificial neural network (ANN), decision tree, support vector machine (SVM), random forest, adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), gradient boosting and categorical boosting (CatBoost), were employed to predict fault severity. The results show that the ANN model accurately predicts faults based on the kurtosis metric. This study illustrates the capability of machine learning, particularly ANN, in enhancing the predictive maintenance strategies for TRBs, thereby enabling early fault detection under varying operational conditions. |
| format | Article |
| id | doaj-art-964bc19ba28b4d5fa8171bae60bf51a8 |
| institution | OA Journals |
| issn | 2812-9717 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Balkan Scientific Centre |
| record_format | Article |
| series | Tribology and Materials |
| spelling | doaj-art-964bc19ba28b4d5fa8171bae60bf51a82025-08-20T02:36:53ZengBalkan Scientific CentreTribology and Materials2812-97172025-06-014210011510.46793/tribomat.2025.009Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearingsHarshal Aher0https://orcid.org/0000-0002-3832-1905Nilesh Ghuge1https://orcid.org/0000-0002-0544-2006Matoshri College of Engineering and Research Center, Eklahare, IndiaMatoshri College of Engineering and Research Center, Eklahare, IndiaThis paper investigated the implementation of machine learning algorithms for health monitoring and fault detection of tapered roller bearings (TRBs) (30205 J2/Q, 30206 J2/Q and 30207 J2/Q). Three defect models were considered: inner race defect, outer race defect and roller defect, in addition to data from the healthy bearings condition. An L27 orthogonal array design was used to generate a comprehensive dataset for each defect model, considering various operational parameters such as load, unbalance, defect type, bearing type and speed. Kurtosis was extracted as the sole feature from the vibration signals for fault classification. Several machine learning models, including artificial neural network (ANN), decision tree, support vector machine (SVM), random forest, adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), gradient boosting and categorical boosting (CatBoost), were employed to predict fault severity. The results show that the ANN model accurately predicts faults based on the kurtosis metric. This study illustrates the capability of machine learning, particularly ANN, in enhancing the predictive maintenance strategies for TRBs, thereby enabling early fault detection under varying operational conditions.https://www.tribomat.net/archive/2025/2025-02/TM-2025-02-05.pdftapered rolling bearingsbearing defectannvibrationcondition monitoring |
| spellingShingle | Harshal Aher Nilesh Ghuge Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings Tribology and Materials tapered rolling bearings bearing defect ann vibration condition monitoring |
| title | Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings |
| title_full | Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings |
| title_fullStr | Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings |
| title_full_unstemmed | Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings |
| title_short | Performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings |
| title_sort | performance comparison of machine learning algorithms for condition monitoring of tapered roller bearings |
| topic | tapered rolling bearings bearing defect ann vibration condition monitoring |
| url | https://www.tribomat.net/archive/2025/2025-02/TM-2025-02-05.pdf |
| work_keys_str_mv | AT harshalaher performancecomparisonofmachinelearningalgorithmsforconditionmonitoringoftaperedrollerbearings AT nileshghuge performancecomparisonofmachinelearningalgorithmsforconditionmonitoringoftaperedrollerbearings |