A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ji, Yonghyeok | - |
dc.contributor.author | Jeong, Seongyong | - |
dc.contributor.author | Cho, Yeongjin | - |
dc.contributor.author | Seo, Howon | - |
dc.contributor.author | Bang, Jaesung | - |
dc.contributor.author | Kim, Jihwan | - |
dc.contributor.author | Lee, Hyeongcheol | - |
dc.date.accessioned | 2022-07-06T11:42:03Z | - |
dc.date.available | 2022-07-06T11:42:03Z | - |
dc.date.created | 2021-12-08 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140529 | - |
dc.description.abstract | Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV & LRARR;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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Hyeongcheol | - |
dc.identifier.doi | 10.3390/app112110187 | - |
dc.identifier.scopusid | 2-s2.0-85118411789 | - |
dc.identifier.wosid | 000721183200001 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.11, no.21, pp.1 - 21 | - |
dc.relation.isPartOf | APPLIED SCIENCES-BASEL | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 11 | - |
dc.citation.number | 21 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 21 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordPlus | FAULT-DETECTION | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | MANAGEMENT | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | BEHAVIOR | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordAuthor | fault detection | - |
dc.subject.keywordAuthor | anomaly detection | - |
dc.subject.keywordAuthor | hybrid electric vehicle | - |
dc.subject.keywordAuthor | transmission mounted electric drive | - |
dc.subject.keywordAuthor | engine clutch engagement/disengagement | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | multi-layer perceptron (MLP) | - |
dc.subject.keywordAuthor | long short-term memory (LSTM) | - |
dc.subject.keywordAuthor | convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | one-class SVM | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.