MISFIRE DETECTION IN A MULTI-CYLINDER DIESEL ENGINE: A MACHINE LEARNING APPROACH
Misfire is another type of abnormal combustion. When engine misfires, cylinder (or cylinders) is not producing its normal amount of power. Engine misfire also has negative effects on engine exhaust emissions such as HC, CO, and NOx. Engine misfire should be detected and eliminated. Normal combustion...
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| Format: | Article |
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Taylor's University
2016-02-01
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| Series: | Journal of Engineering Science and Technology |
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| Online Access: | http://jestec.taylors.edu.my/Vol%2011%20issue%202%20February%202016/Volume%20(11)%20Issue%20(2)%20278-%20295.pdf |
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| author | AYYASAMY KRISHNAMOORTHY BABU V. ANTONY AROUL RAJ G. KUMARESAN |
| author_facet | AYYASAMY KRISHNAMOORTHY BABU V. ANTONY AROUL RAJ G. KUMARESAN |
| author_sort | AYYASAMY KRISHNAMOORTHY BABU |
| collection | DOAJ |
| description | Misfire is another type of abnormal combustion. When engine misfires, cylinder (or cylinders) is not producing its normal amount of power. Engine misfire also has negative effects on engine exhaust emissions such as HC, CO, and NOx. Engine misfire should be detected and eliminated. Normal combustion and misfire in the cylinder (if any) generates vibrations in the engine block. The vibration characters due to misfire are unique for a particular cylinder. This can be diagnosed by processing the vibration signals acquired from the engine cylinder block using a piezoelectric accelerometer. The obtained signals were decoded using statistical parameters, like, Kurtosis, standard deviation, mean, median, etc. Misfire identification algorithms such as
AdaBoost, LogitBoost, MultiClass Classifier, and J48 were used as tools for feature selection and classification. The signals were trained and tested by the selected classifiers. The classification accuracy of selected classifiers were compared and presented in this paper. MultiClass Classifier was found to be performing better with selected statistical features compared to other classifiers. |
| first_indexed | 2024-12-22T12:24:29Z |
| format | Article |
| id | doaj.art-ff2a204634d54fec88a08f0c8e4fb02d |
| institution | Directory Open Access Journal |
| issn | 1823-4690 |
| language | English |
| last_indexed | 2024-12-22T12:24:29Z |
| publishDate | 2016-02-01 |
| publisher | Taylor's University |
| record_format | Article |
| series | Journal of Engineering Science and Technology |
| spelling | doaj.art-ff2a204634d54fec88a08f0c8e4fb02d2022-12-21T18:25:52ZengTaylor's UniversityJournal of Engineering Science and Technology1823-46902016-02-01112278295MISFIRE DETECTION IN A MULTI-CYLINDER DIESEL ENGINE: A MACHINE LEARNING APPROACHAYYASAMY KRISHNAMOORTHY BABU0V. ANTONY AROUL RAJ1G. KUMARESAN2Department of Mechanical Engineering, PERI Institute of Technology, Chennai, IndiaDepartment of Mechanical Engineering, Easwari Engineering College, Chennai, India Institute of Energy Studies, Anna University, Chennai, India Misfire is another type of abnormal combustion. When engine misfires, cylinder (or cylinders) is not producing its normal amount of power. Engine misfire also has negative effects on engine exhaust emissions such as HC, CO, and NOx. Engine misfire should be detected and eliminated. Normal combustion and misfire in the cylinder (if any) generates vibrations in the engine block. The vibration characters due to misfire are unique for a particular cylinder. This can be diagnosed by processing the vibration signals acquired from the engine cylinder block using a piezoelectric accelerometer. The obtained signals were decoded using statistical parameters, like, Kurtosis, standard deviation, mean, median, etc. Misfire identification algorithms such as AdaBoost, LogitBoost, MultiClass Classifier, and J48 were used as tools for feature selection and classification. The signals were trained and tested by the selected classifiers. The classification accuracy of selected classifiers were compared and presented in this paper. MultiClass Classifier was found to be performing better with selected statistical features compared to other classifiers.http://jestec.taylors.edu.my/Vol%2011%20issue%202%20February%202016/Volume%20(11)%20Issue%20(2)%20278-%20295.pdfEngine misfireFeature extractionConfusion matrixAdaBoostLogitBoostMultiClass Classifier |
| spellingShingle | AYYASAMY KRISHNAMOORTHY BABU V. ANTONY AROUL RAJ G. KUMARESAN MISFIRE DETECTION IN A MULTI-CYLINDER DIESEL ENGINE: A MACHINE LEARNING APPROACH Journal of Engineering Science and Technology Engine misfire Feature extraction Confusion matrix AdaBoost LogitBoost MultiClass Classifier |
| title | MISFIRE DETECTION IN A MULTI-CYLINDER DIESEL ENGINE: A MACHINE LEARNING APPROACH |
| title_full | MISFIRE DETECTION IN A MULTI-CYLINDER DIESEL ENGINE: A MACHINE LEARNING APPROACH |
| title_fullStr | MISFIRE DETECTION IN A MULTI-CYLINDER DIESEL ENGINE: A MACHINE LEARNING APPROACH |
| title_full_unstemmed | MISFIRE DETECTION IN A MULTI-CYLINDER DIESEL ENGINE: A MACHINE LEARNING APPROACH |
| title_short | MISFIRE DETECTION IN A MULTI-CYLINDER DIESEL ENGINE: A MACHINE LEARNING APPROACH |
| title_sort | misfire detection in a multi cylinder diesel engine a machine learning approach |
| topic | Engine misfire Feature extraction Confusion matrix AdaBoost LogitBoost MultiClass Classifier |
| url | http://jestec.taylors.edu.my/Vol%2011%20issue%202%20February%202016/Volume%20(11)%20Issue%20(2)%20278-%20295.pdf |
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