Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms
Abstract Deep Groove Ball Bearings (DGBBs) serve multipurpose and are used for the propeller shaft movement and applications based on revolving. They have great applications in industry related to axial and radial loads. The major risk factors are faults in bearings. Data analyzed for faults in the...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97410-8 |
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| author | Vedant Jaiswal Narendiranath Babu T Pandiyan Murugan Rama Prabha D |
| author_facet | Vedant Jaiswal Narendiranath Babu T Pandiyan Murugan Rama Prabha D |
| author_sort | Vedant Jaiswal |
| collection | DOAJ |
| description | Abstract Deep Groove Ball Bearings (DGBBs) serve multipurpose and are used for the propeller shaft movement and applications based on revolving. They have great applications in industry related to axial and radial loads. The major risk factors are faults in bearings. Data analyzed for faults in the DGBBs help us conclude that there are 4 types of bearing faults. For instance, Excluding HB- Healthy Bearing, there are CF- Case Fault, BF- Ball Fault, IRF- Inner Ring Fault, and ORF- Outer Ring Fault. The input parameters are represented by using 14 features in the evaluation. Next, a feature ranking method is established to classify the bearing fault and contribution of each of the features is used as input conditions. It displays the involvement value for each of the 14 parameters. Automatic fault classification has been done by Artificial Neural Networks (ANN). Training on various algorithms is performed, noting and storing the probability of correct prediction for comparison. The probability of correct predictions decreases as the number of samples representing faults increases. A high efficiency of around 97.9% has been achieved for the Resnet50 algorithm. The classifier learner achieved an accuracy of 97% using the neural network, followed by the decision tree and discriminant analysis. |
| format | Article |
| id | doaj-art-807fc6e58cff4367b3f67d571801597c |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-807fc6e58cff4367b3f67d571801597c2025-08-20T02:27:52ZengNature PortfolioScientific Reports2045-23222025-04-0115112210.1038/s41598-025-97410-8Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithmsVedant Jaiswal0Narendiranath Babu T1Pandiyan Murugan2Rama Prabha D3School of Mechanical Engineering, Vellore Institute of Technology (VIT)School of Mechanical Engineering, Vellore Institute of Technology (VIT)School of Mechanical Engineering, Vellore Institute of Technology (VIT)School of Electrical Engineering, Vellore Institute of Technology (VIT)Abstract Deep Groove Ball Bearings (DGBBs) serve multipurpose and are used for the propeller shaft movement and applications based on revolving. They have great applications in industry related to axial and radial loads. The major risk factors are faults in bearings. Data analyzed for faults in the DGBBs help us conclude that there are 4 types of bearing faults. For instance, Excluding HB- Healthy Bearing, there are CF- Case Fault, BF- Ball Fault, IRF- Inner Ring Fault, and ORF- Outer Ring Fault. The input parameters are represented by using 14 features in the evaluation. Next, a feature ranking method is established to classify the bearing fault and contribution of each of the features is used as input conditions. It displays the involvement value for each of the 14 parameters. Automatic fault classification has been done by Artificial Neural Networks (ANN). Training on various algorithms is performed, noting and storing the probability of correct prediction for comparison. The probability of correct predictions decreases as the number of samples representing faults increases. A high efficiency of around 97.9% has been achieved for the Resnet50 algorithm. The classifier learner achieved an accuracy of 97% using the neural network, followed by the decision tree and discriminant analysis.https://doi.org/10.1038/s41598-025-97410-8Ball bearingArtificial neural networkFault classificationMachine learningDeep neural networkSupport vector machine |
| spellingShingle | Vedant Jaiswal Narendiranath Babu T Pandiyan Murugan Rama Prabha D Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms Scientific Reports Ball bearing Artificial neural network Fault classification Machine learning Deep neural network Support vector machine |
| title | Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms |
| title_full | Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms |
| title_fullStr | Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms |
| title_full_unstemmed | Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms |
| title_short | Fault analysis on deep groove ball bearing using ResNet50 and AlexNet50 algorithms |
| title_sort | fault analysis on deep groove ball bearing using resnet50 and alexnet50 algorithms |
| topic | Ball bearing Artificial neural network Fault classification Machine learning Deep neural network Support vector machine |
| url | https://doi.org/10.1038/s41598-025-97410-8 |
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