A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine...

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Autores principales:	Yonghyeok Ji, Seongyong Jeong, Yeongjin Cho, Howon Seo, Jaesung Bang, Jihwan Kim, Hyeongcheol Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	fault detection anomaly detection hybrid electric vehicle transmission mounted electric drive engine clutch engagement/disengagement machine learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/70700a112af74cde9732947e86560643
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id	oai:doaj.org-article:70700a112af74cde9732947e86560643
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spelling	oai:doaj.org-article:70700a112af74cde9732947e865606432021-11-11T15:14:48ZA Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles10.3390/app1121101872076-3417https://doaj.org/article/70700a112af74cde9732947e865606432021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10187https://doaj.org/toc/2076-3417Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.Yonghyeok JiSeongyong JeongYeongjin ChoHowon SeoJaesung BangJihwan KimHyeongcheol LeeMDPI AGarticlefault detectionanomaly detectionhybrid electric vehicletransmission mounted electric driveengine clutch engagement/disengagementmachine learningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10187, p 10187 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	fault detection anomaly detection hybrid electric vehicle transmission mounted electric drive engine clutch engagement/disengagement machine learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	fault detection anomaly detection hybrid electric vehicle transmission mounted electric drive engine clutch engagement/disengagement machine learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yonghyeok Ji Seongyong Jeong Yeongjin Cho Howon Seo Jaesung Bang Jihwan Kim Hyeongcheol Lee A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles
description	Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.
format	article
author	Yonghyeok Ji Seongyong Jeong Yeongjin Cho Howon Seo Jaesung Bang Jihwan Kim Hyeongcheol Lee
author_facet	Yonghyeok Ji Seongyong Jeong Yeongjin Cho Howon Seo Jaesung Bang Jihwan Kim Hyeongcheol Lee
author_sort	Yonghyeok Ji
title	A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles
title_short	A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles
title_full	A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles
title_fullStr	A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles
title_full_unstemmed	A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles
title_sort	study on the anomaly detection of engine clutch engagement/disengagement using machine learning for transmission mounted electric drive type hybrid electric vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/70700a112af74cde9732947e86560643
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A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

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