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Pattern recognition for HEV engine diagnostic using an improved statistical analysis

Detecting early symptoms of engine failure is a crucial phase in an engine management system to prevent poor driving performance and experience. This paper proposes a Hybrid Electric Vehicle (HEV) engine diagnostics using a low-cost piezo-film sensor, an analysis with improved statistical method and...

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主要な著者: Nor Azizi Ngatiman,, Mohd. Zaki Nuawi,, Mohd Irman Ramli,
フォーマット: 論文
言語:English
出版事項: Penerbit Universiti Kebangsaan Malaysia 2019
オンライン･アクセス:http://journalarticle.ukm.my/14822/1/13.pdf
http://journalarticle.ukm.my/14822/
http://www.ukm.my/jkukm/volume-312-2019/
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spelling my-ukm.journal.148222020-07-10T03:20:03Z http://journalarticle.ukm.my/14822/ Pattern recognition for HEV engine diagnostic using an improved statistical analysis Nor Azizi Ngatiman, Mohd. Zaki Nuawi, Mohd Irman Ramli, Detecting early symptoms of engine failure is a crucial phase in an engine management system to prevent poor driving performance and experience. This paper proposes a Hybrid Electric Vehicle (HEV) engine diagnostics using a low-cost piezo-film sensor, an analysis with improved statistical method and verification by a Support Vector Machine (SVM). The current engine management system is unable to evaluate the performance of each cylinder operation. Eventually, it affects the whole hybrid vehicle system, particularly in the mode of charging and accelerating. This research aims to classify the combustion to monitor the condition of sparking activity of the engine by using the Z-freq statistical method. Piezo-film sensors were mounted on the Internal Combustion Engine (ICE) wall of each hybrid vehicle for vibration signal measurements. The engine runs at different speeds, the vibration signals were then recorded and analysed using the Z-freq technique. A machine learning tool referred to as Support Vector Machine was used to verify the classifications made by the Z-freq technique. A significant correlation was found between the voltage signal and calculated Z-freq coefficient value. Moreover, a good pattern was produced within a consistent value of the engine speed. This technique is useful for the hybrid engine to identify different stages of combustion and enable pattern categorisation of the measured parameters. These improved techniques provide strong evidence based on pattern representation and facilitate the investigator to categorise the measured parameters. Penerbit Universiti Kebangsaan Malaysia 2019-10 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/14822/1/13.pdf Nor Azizi Ngatiman, and Mohd. Zaki Nuawi, and Mohd Irman Ramli, (2019) Pattern recognition for HEV engine diagnostic using an improved statistical analysis. Jurnal Kejuruteraan, 31 (2). pp. 287-294. ISSN 0128-0198 http://www.ukm.my/jkukm/volume-312-2019/
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description Detecting early symptoms of engine failure is a crucial phase in an engine management system to prevent poor driving performance and experience. This paper proposes a Hybrid Electric Vehicle (HEV) engine diagnostics using a low-cost piezo-film sensor, an analysis with improved statistical method and verification by a Support Vector Machine (SVM). The current engine management system is unable to evaluate the performance of each cylinder operation. Eventually, it affects the whole hybrid vehicle system, particularly in the mode of charging and accelerating. This research aims to classify the combustion to monitor the condition of sparking activity of the engine by using the Z-freq statistical method. Piezo-film sensors were mounted on the Internal Combustion Engine (ICE) wall of each hybrid vehicle for vibration signal measurements. The engine runs at different speeds, the vibration signals were then recorded and analysed using the Z-freq technique. A machine learning tool referred to as Support Vector Machine was used to verify the classifications made by the Z-freq technique. A significant correlation was found between the voltage signal and calculated Z-freq coefficient value. Moreover, a good pattern was produced within a consistent value of the engine speed. This technique is useful for the hybrid engine to identify different stages of combustion and enable pattern categorisation of the measured parameters. These improved techniques provide strong evidence based on pattern representation and facilitate the investigator to categorise the measured parameters.
format Article
author Nor Azizi Ngatiman,
Mohd. Zaki Nuawi,
Mohd Irman Ramli,
spellingShingle Nor Azizi Ngatiman,
Mohd. Zaki Nuawi,
Mohd Irman Ramli,
Pattern recognition for HEV engine diagnostic using an improved statistical analysis
author_facet Nor Azizi Ngatiman,
Mohd. Zaki Nuawi,
Mohd Irman Ramli,
author_sort Nor Azizi Ngatiman,
title Pattern recognition for HEV engine diagnostic using an improved statistical analysis
title_short Pattern recognition for HEV engine diagnostic using an improved statistical analysis
title_full Pattern recognition for HEV engine diagnostic using an improved statistical analysis
title_fullStr Pattern recognition for HEV engine diagnostic using an improved statistical analysis
title_full_unstemmed Pattern recognition for HEV engine diagnostic using an improved statistical analysis
title_sort pattern recognition for hev engine diagnostic using an improved statistical analysis
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2019
url http://journalarticle.ukm.my/14822/1/13.pdf
http://journalarticle.ukm.my/14822/
http://www.ukm.my/jkukm/volume-312-2019/
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score 13.251813

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