Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement
Diesel engines find extensive application in various production sectors, including industry and agriculture. Strengthening the condition monitoring and fault diagnosis of diesel engines is of paramount importance in ensuring the smooth operation of production systems. Timely detection and eliminatio...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-12-01
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| Series: | Open Physics |
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| Online Access: | https://doi.org/10.1515/phys-2024-0110 |
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| _version_ | 1841556527070052352 |
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| author | Wang Rongcai Yan Hao Dong Enzhi Cheng Zhonghua Li Yuan Jia Xisheng |
| author_facet | Wang Rongcai Yan Hao Dong Enzhi Cheng Zhonghua Li Yuan Jia Xisheng |
| author_sort | Wang Rongcai |
| collection | DOAJ |
| description | Diesel engines find extensive application in various production sectors, including industry and agriculture. Strengthening the condition monitoring and fault diagnosis of diesel engines is of paramount importance in ensuring the smooth operation of production systems. Timely detection and elimination of defects play a crucial role in maintaining the normal functioning of these systems. Significant temperature fluctuations during the operation of diesel engines are often associated with malfunctions, including ignition failure, abnormal intake, and exhaust processes. Hence, the application of infrared thermography (IRT) for collecting infrared images of diesel engines and conducting quantitative analysis of the temperature distribution in these images has proven to be a faster and more efficient method for recognizing the health status of diesel engines, compared to other fault diagnosis methods. In recent years, there has been a growing interest in deep learning (DL) for fault diagnosis in various industries. This emerging trend has attracted significant attention from researchers. Convolutional neural network (CNN) has garnered significant attention owing to the exceptional capability in extracting image features. Therefore, the article presents a new fault diagnosis method for diesel engines using IRT and CNN. The proposed method involves conducting adaptive histogram equalization for image enhancement, followed by employing Softmax regression for pattern recognition. Finally, two sets of self-made experimental data are used to investigate the impact of temperature variations on fault diagnosis performance and to validate the efficacy of the proposed method in comparison with three DL methods. The findings indicate that this method exhibits superior performance in the realm of diesel engine fault diagnosis. |
| format | Article |
| id | doaj-art-c4cdcde91810445a8a6d8b2307bafbc9 |
| institution | Kabale University |
| issn | 2391-5471 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Physics |
| spelling | doaj-art-c4cdcde91810445a8a6d8b2307bafbc92025-01-07T07:56:16ZengDe GruyterOpen Physics2391-54712024-12-0122115031910.1515/phys-2024-0110Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancementWang Rongcai0Yan Hao1Dong Enzhi2Cheng Zhonghua3Li Yuan4Jia Xisheng5Shijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang, 050003, ChinaShijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang, 050003, ChinaShijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang, 050003, ChinaShijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang, 050003, ChinaShijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang, 050003, ChinaShijiazhuang Campus of Army Engineering University of PLA, Shijiazhuang, 050003, ChinaDiesel engines find extensive application in various production sectors, including industry and agriculture. Strengthening the condition monitoring and fault diagnosis of diesel engines is of paramount importance in ensuring the smooth operation of production systems. Timely detection and elimination of defects play a crucial role in maintaining the normal functioning of these systems. Significant temperature fluctuations during the operation of diesel engines are often associated with malfunctions, including ignition failure, abnormal intake, and exhaust processes. Hence, the application of infrared thermography (IRT) for collecting infrared images of diesel engines and conducting quantitative analysis of the temperature distribution in these images has proven to be a faster and more efficient method for recognizing the health status of diesel engines, compared to other fault diagnosis methods. In recent years, there has been a growing interest in deep learning (DL) for fault diagnosis in various industries. This emerging trend has attracted significant attention from researchers. Convolutional neural network (CNN) has garnered significant attention owing to the exceptional capability in extracting image features. Therefore, the article presents a new fault diagnosis method for diesel engines using IRT and CNN. The proposed method involves conducting adaptive histogram equalization for image enhancement, followed by employing Softmax regression for pattern recognition. Finally, two sets of self-made experimental data are used to investigate the impact of temperature variations on fault diagnosis performance and to validate the efficacy of the proposed method in comparison with three DL methods. The findings indicate that this method exhibits superior performance in the realm of diesel engine fault diagnosis.https://doi.org/10.1515/phys-2024-0110infrared thermographyconvolutional neural networkimage enhancementdiesel enginefault diagnosis |
| spellingShingle | Wang Rongcai Yan Hao Dong Enzhi Cheng Zhonghua Li Yuan Jia Xisheng Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement Open Physics infrared thermography convolutional neural network image enhancement diesel engine fault diagnosis |
| title | Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement |
| title_full | Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement |
| title_fullStr | Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement |
| title_full_unstemmed | Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement |
| title_short | Infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement |
| title_sort | infrared thermography based fault diagnosis of diesel engines using convolutional neural network and image enhancement |
| topic | infrared thermography convolutional neural network image enhancement diesel engine fault diagnosis |
| url | https://doi.org/10.1515/phys-2024-0110 |
| work_keys_str_mv | AT wangrongcai infraredthermographybasedfaultdiagnosisofdieselenginesusingconvolutionalneuralnetworkandimageenhancement AT yanhao infraredthermographybasedfaultdiagnosisofdieselenginesusingconvolutionalneuralnetworkandimageenhancement AT dongenzhi infraredthermographybasedfaultdiagnosisofdieselenginesusingconvolutionalneuralnetworkandimageenhancement AT chengzhonghua infraredthermographybasedfaultdiagnosisofdieselenginesusingconvolutionalneuralnetworkandimageenhancement AT liyuan infraredthermographybasedfaultdiagnosisofdieselenginesusingconvolutionalneuralnetworkandimageenhancement AT jiaxisheng infraredthermographybasedfaultdiagnosisofdieselenginesusingconvolutionalneuralnetworkandimageenhancement |