Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection
The subject of this research is the development of a classifier based on machine learning (ML) that is able to recognize defective and healthy ball bearings. For this purpose, vibration measurements were performed on the bearings, on a total of 196 samples. For each recorded vibration signal, a feat...
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| Format: | Article |
| Language: | English |
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Sciendo
2025-04-01
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| Series: | Measurement Science Review |
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| Online Access: | https://doi.org/10.2478/msr-2025-0004 |
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| author | Stepanić Pavle Dučić Nedeljko Vidaković Jelena Baralić Jelena Popović Marko |
| author_facet | Stepanić Pavle Dučić Nedeljko Vidaković Jelena Baralić Jelena Popović Marko |
| author_sort | Stepanić Pavle |
| collection | DOAJ |
| description | The subject of this research is the development of a classifier based on machine learning (ML) that is able to recognize defective and healthy ball bearings. For this purpose, vibration measurements were performed on the bearings, on a total of 196 samples. For each recorded vibration signal, a feature extraction was performed by digital processing in the time domain. The following ML algorithms were used to develop the classifier: K-nearest neighbor (KNN) and support vector machine (SVM) as well as improved versions of the aforementioned algorithms. Improved versions of the mentioned algorithms were obtained by optimizing their hyperparameters. The corresponding models of the KNN and SVM algorithms showed a high percentage of success in classification, 98.5 % and 99.5 %, respectively. By optimizing the hyperparameters, models with a maximum classification success of 100 % were achieved. |
| format | Article |
| id | doaj-art-e9be7dd2217143ef9adb0bdb7936c21e |
| institution | DOAJ |
| issn | 1335-8871 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Measurement Science Review |
| spelling | doaj-art-e9be7dd2217143ef9adb0bdb7936c21e2025-08-20T03:18:38ZengSciendoMeasurement Science Review1335-88712025-04-01251222910.2478/msr-2025-0004Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault DetectionStepanić Pavle0Dučić Nedeljko1Vidaković Jelena2Baralić Jelena3Popović Marko4Research and Development Institute Lola L.t.d., Kneza Viseslava, 70A, 11030, Belgrade, SerbiaFaculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, 32102, Čačak, SerbiaResearch and Development Institute Lola L.t.d., Kneza Viseslava, 70A, 11030, Belgrade, SerbiaFaculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, 32102, Čačak, SerbiaFaculty of Technical Sciences Čačak, University of Kragujevac, Svetog Save 65, 32102, Čačak, SerbiaThe subject of this research is the development of a classifier based on machine learning (ML) that is able to recognize defective and healthy ball bearings. For this purpose, vibration measurements were performed on the bearings, on a total of 196 samples. For each recorded vibration signal, a feature extraction was performed by digital processing in the time domain. The following ML algorithms were used to develop the classifier: K-nearest neighbor (KNN) and support vector machine (SVM) as well as improved versions of the aforementioned algorithms. Improved versions of the mentioned algorithms were obtained by optimizing their hyperparameters. The corresponding models of the KNN and SVM algorithms showed a high percentage of success in classification, 98.5 % and 99.5 %, respectively. By optimizing the hyperparameters, models with a maximum classification success of 100 % were achieved.https://doi.org/10.2478/msr-2025-0004vibration measurementball bearingsmachine learningfault detection |
| spellingShingle | Stepanić Pavle Dučić Nedeljko Vidaković Jelena Baralić Jelena Popović Marko Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection Measurement Science Review vibration measurement ball bearings machine learning fault detection |
| title | Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection |
| title_full | Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection |
| title_fullStr | Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection |
| title_full_unstemmed | Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection |
| title_short | Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection |
| title_sort | implementation and evaluation of machine learning algorithms in ball bearing fault detection |
| topic | vibration measurement ball bearings machine learning fault detection |
| url | https://doi.org/10.2478/msr-2025-0004 |
| work_keys_str_mv | AT stepanicpavle implementationandevaluationofmachinelearningalgorithmsinballbearingfaultdetection AT ducicnedeljko implementationandevaluationofmachinelearningalgorithmsinballbearingfaultdetection AT vidakovicjelena implementationandevaluationofmachinelearningalgorithmsinballbearingfaultdetection AT baralicjelena implementationandevaluationofmachinelearningalgorithmsinballbearingfaultdetection AT popovicmarko implementationandevaluationofmachinelearningalgorithmsinballbearingfaultdetection |