Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox Wear
Nowadays, induction motors and gearboxes play an important role in the industry due to the fact that they are indispensable tools that allow a large number of machines to operate. In this research, a diagnosis method is proposed for the detection of different faults in an electromechanical system th...
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MDPI AG
2024-12-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/7/6/123 |
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| author | Emmanuel Resendiz-Ochoa Omar Trejo-Chavez Juan J. Saucedo-Dorantes Luis A. Morales-Hernandez Irving A. Cruz-Albarran |
| author_facet | Emmanuel Resendiz-Ochoa Omar Trejo-Chavez Juan J. Saucedo-Dorantes Luis A. Morales-Hernandez Irving A. Cruz-Albarran |
| author_sort | Emmanuel Resendiz-Ochoa |
| collection | DOAJ |
| description | Nowadays, induction motors and gearboxes play an important role in the industry due to the fact that they are indispensable tools that allow a large number of machines to operate. In this research, a diagnosis method is proposed for the detection of different faults in an electromechanical system through infrared thermography and a convolutional neural network (CNN). During the experiment, we tested different conditions in the motor and the gearbox. The induction motor was operated in four conditions, in a healthy state, with one broken bar, a damaged bearing, and misalignment, while the gearbox was operated in three conditions with healthy gears, 50% wear, and 75% wear. The motor failures and gear wear were induced by different machining operations. Data augmentation was then performed using basic transformations such as mirror image and brightness variation. Ablation tests were also carried out, and a convolutional neural network with a basic architecture was proposed; the performance indicators show a precision of 98.53%, accuracy of 98.54%, recall of 98.65%, and F1-Score of 98.55%. The system obtained confirms that through the use of infrared thermography and deep learning, it is possible to identify faults at different points of an electromechanical system. |
| format | Article |
| id | doaj-art-00bc81631fa147b492a8f594741bd6af |
| institution | Kabale University |
| issn | 2571-5577 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Applied System Innovation |
| spelling | doaj-art-00bc81631fa147b492a8f594741bd6af2024-12-27T14:09:35ZengMDPI AGApplied System Innovation2571-55772024-12-017612310.3390/asi7060123Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox WearEmmanuel Resendiz-Ochoa0Omar Trejo-Chavez1Juan J. Saucedo-Dorantes2Luis A. Morales-Hernandez3Irving A. Cruz-Albarran4C. A. Artificial Intelligence Systems Applied to Biomedical and Mechanical Models, Faculty of Engineering, Campus San Juan del Río, Autonomous University of Queretaro, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, MexicoEngineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, MexicoEngineering Faculty, Campus San Juan del Río, Autonomous University of Queretaro, Rio Moctezuma 249, San Juan del Rio 76807, MexicoC. A. Mechatronics, Faculty of Engineering, Campus San Juan del Río, Autonomous University of Queretaro, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, MexicoC. A. Artificial Intelligence Systems Applied to Biomedical and Mechanical Models, Faculty of Engineering, Campus San Juan del Río, Autonomous University of Queretaro, Río Moctezuma 249, Col. San Cayetano, San Juan del Río 76807, MexicoNowadays, induction motors and gearboxes play an important role in the industry due to the fact that they are indispensable tools that allow a large number of machines to operate. In this research, a diagnosis method is proposed for the detection of different faults in an electromechanical system through infrared thermography and a convolutional neural network (CNN). During the experiment, we tested different conditions in the motor and the gearbox. The induction motor was operated in four conditions, in a healthy state, with one broken bar, a damaged bearing, and misalignment, while the gearbox was operated in three conditions with healthy gears, 50% wear, and 75% wear. The motor failures and gear wear were induced by different machining operations. Data augmentation was then performed using basic transformations such as mirror image and brightness variation. Ablation tests were also carried out, and a convolutional neural network with a basic architecture was proposed; the performance indicators show a precision of 98.53%, accuracy of 98.54%, recall of 98.65%, and F1-Score of 98.55%. The system obtained confirms that through the use of infrared thermography and deep learning, it is possible to identify faults at different points of an electromechanical system.https://www.mdpi.com/2571-5577/7/6/123thermographyCNNinduction motor failuresgearbox conditionmulti-fault diagnosis |
| spellingShingle | Emmanuel Resendiz-Ochoa Omar Trejo-Chavez Juan J. Saucedo-Dorantes Luis A. Morales-Hernandez Irving A. Cruz-Albarran Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox Wear Applied System Innovation thermography CNN induction motor failures gearbox condition multi-fault diagnosis |
| title | Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox Wear |
| title_full | Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox Wear |
| title_fullStr | Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox Wear |
| title_full_unstemmed | Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox Wear |
| title_short | Application of Thermography and Convolutional Neural Network to Diagnose Mechanical Faults in Induction Motors and Gearbox Wear |
| title_sort | application of thermography and convolutional neural network to diagnose mechanical faults in induction motors and gearbox wear |
| topic | thermography CNN induction motor failures gearbox condition multi-fault diagnosis |
| url | https://www.mdpi.com/2571-5577/7/6/123 |
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