Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network
The use of artificial intelligence-based techniques to solve engineering problems is increasing. One of the most challenging tasks facing industry is the timely diagnosis of failures in electromechanical systems, as they are an essential part of production systems. In this sense, the earlier the det...
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MDPI AG
2024-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/12/928 |
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| author | Emmanuel Resendiz-Ochoa Salvador Calderon-Uribe Luis A. Morales-Hernandez Carlos A. Perez-Ramirez Irving A. Cruz-Albarran |
| author_facet | Emmanuel Resendiz-Ochoa Salvador Calderon-Uribe Luis A. Morales-Hernandez Carlos A. Perez-Ramirez Irving A. Cruz-Albarran |
| author_sort | Emmanuel Resendiz-Ochoa |
| collection | DOAJ |
| description | The use of artificial intelligence-based techniques to solve engineering problems is increasing. One of the most challenging tasks facing industry is the timely diagnosis of failures in electromechanical systems, as they are an essential part of production systems. In this sense, the earlier the detection, the higher the economic loss reduction. For this reason, this work proposes the development of a new methodology based on infrared thermography and an artificial intelligence-based classifier for the detection of multiple faults in an electromechanical system. The proposal combines the intensity profile of the grey-scale image, the use of Fast Fourier Transform and an artificial neural network to perform the detection of twelve states for the state of an electromechanical system: healthy, bearing defect, broken rotor bar, misalignment and gear wear on the gearbox. From the experimental setup, 50 thermographic images were obtained for each state. The method was implemented and tested under different conditions to verify its reliability. The results show that the precision, accuracy, recall and F1-score are higher than 99%. Thus, it can be concluded that it is possible to detect multiple conditions in an electromechanical system using the intensity profile and an artificial neural network, achieving good accuracy and reliability. |
| format | Article |
| id | doaj-art-6c7b0f8d32704f29a95b21c92278331e |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-6c7b0f8d32704f29a95b21c92278331e2024-12-27T14:37:12ZengMDPI AGMachines2075-17022024-12-01121292810.3390/machines12120928Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural NetworkEmmanuel Resendiz-Ochoa0Salvador Calderon-Uribe1Luis A. Morales-Hernandez2Carlos A. Perez-Ramirez3Irving A. Cruz-Albarran4Artificial Intelligence Systems Applied to Biomedical and Mechanical Models, Faculty of Engineering, Autonomus University of Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio 76807, Queretaro, MexicoMechatronics, Faculty of Engineering, Autonomus University of Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio 76807, Queretaro, MexicoMechatronics, Faculty of Engineering, Autonomus University of Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio 76807, Queretaro, MexicoArtificial Intelligence Systems Applied to Biomedical and Mechanical Models, Faculty of Engineering, Autonomus University of Queretaro, Campus Aeropuerto, Carretera a Chichimequillas S/N, Ejido Bolanos, Santiago de Queretaro 76140, Queretaro, MexicoArtificial Intelligence Systems Applied to Biomedical and Mechanical Models, Faculty of Engineering, Autonomus University of Queretaro, Campus San Juan del Rio, Rio Moctezuma 249, Col. San Cayetano, San Juan del Rio 76807, Queretaro, MexicoThe use of artificial intelligence-based techniques to solve engineering problems is increasing. One of the most challenging tasks facing industry is the timely diagnosis of failures in electromechanical systems, as they are an essential part of production systems. In this sense, the earlier the detection, the higher the economic loss reduction. For this reason, this work proposes the development of a new methodology based on infrared thermography and an artificial intelligence-based classifier for the detection of multiple faults in an electromechanical system. The proposal combines the intensity profile of the grey-scale image, the use of Fast Fourier Transform and an artificial neural network to perform the detection of twelve states for the state of an electromechanical system: healthy, bearing defect, broken rotor bar, misalignment and gear wear on the gearbox. From the experimental setup, 50 thermographic images were obtained for each state. The method was implemented and tested under different conditions to verify its reliability. The results show that the precision, accuracy, recall and F1-score are higher than 99%. Thus, it can be concluded that it is possible to detect multiple conditions in an electromechanical system using the intensity profile and an artificial neural network, achieving good accuracy and reliability.https://www.mdpi.com/2075-1702/12/12/928artificial neural networkthermographic imagefast fourier transforminduction motorgearbox |
| spellingShingle | Emmanuel Resendiz-Ochoa Salvador Calderon-Uribe Luis A. Morales-Hernandez Carlos A. Perez-Ramirez Irving A. Cruz-Albarran Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network Machines artificial neural network thermographic image fast fourier transform induction motor gearbox |
| title | Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network |
| title_full | Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network |
| title_fullStr | Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network |
| title_full_unstemmed | Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network |
| title_short | Multiple Electromechanical-Failure Detection in Induction Motor Using Thermographic Intensity Profile and Artificial Neural Network |
| title_sort | multiple electromechanical failure detection in induction motor using thermographic intensity profile and artificial neural network |
| topic | artificial neural network thermographic image fast fourier transform induction motor gearbox |
| url | https://www.mdpi.com/2075-1702/12/12/928 |
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