Sparkplug failure detection using Z-freq and machine learning
Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a nov...
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Universitas Ahmad Dahlan
2021
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my.utem.eprints.258472022-04-13T15:53:07Z http://eprints.utem.edu.my/id/eprint/25847/ Sparkplug failure detection using Z-freq and machine learning Ngatiman, Nor Azazi Nuawi, Mohd Zaki Putra, Azma Qamber, Isa S. Tole, Sutikno Jopri, Mohd Hatta Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimental output were proved and endorsed in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. Finally, it confirmed that the proposed technique capably to make a diagnosis: fault detection, fault localization, and fault severity classification. Universitas Ahmad Dahlan 2021-12 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/25847/2/22027-59268-1-PB.PDF Ngatiman, Nor Azazi and Nuawi, Mohd Zaki and Putra, Azma and Qamber, Isa S. and Tole, Sutikno and Jopri, Mohd Hatta (2021) Sparkplug failure detection using Z-freq and machine learning. TELKOMNIKA (Telecommunication Computing Electronics and Control), 19 (6). pp. 2020-2029. ISSN 1693-6930 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/22027/10963 10.12928/TELKOMNIKA.v19i6.22027 |
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Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimental output were proved and endorsed in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. Finally, it confirmed that the proposed technique capably to make a diagnosis: fault detection, fault localization, and fault severity classification. |
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Ngatiman, Nor Azazi Nuawi, Mohd Zaki Putra, Azma Qamber, Isa S. Tole, Sutikno Jopri, Mohd Hatta |
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Ngatiman, Nor Azazi Nuawi, Mohd Zaki Putra, Azma Qamber, Isa S. Tole, Sutikno Jopri, Mohd Hatta Sparkplug failure detection using Z-freq and machine learning |
author_facet |
Ngatiman, Nor Azazi Nuawi, Mohd Zaki Putra, Azma Qamber, Isa S. Tole, Sutikno Jopri, Mohd Hatta |
author_sort |
Ngatiman, Nor Azazi |
title |
Sparkplug failure detection using Z-freq and machine learning |
title_short |
Sparkplug failure detection using Z-freq and machine learning |
title_full |
Sparkplug failure detection using Z-freq and machine learning |
title_fullStr |
Sparkplug failure detection using Z-freq and machine learning |
title_full_unstemmed |
Sparkplug failure detection using Z-freq and machine learning |
title_sort |
sparkplug failure detection using z-freq and machine learning |
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Universitas Ahmad Dahlan |
publishDate |
2021 |
url |
http://eprints.utem.edu.my/id/eprint/25847/2/22027-59268-1-PB.PDF http://eprints.utem.edu.my/id/eprint/25847/ http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/22027/10963 |
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