Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정구익 | - |
dc.contributor.author | 하현수 | - |
dc.contributor.author | 이상준 | - |
dc.date.accessioned | 2022-03-10T08:40:05Z | - |
dc.date.available | 2022-03-10T08:40:05Z | - |
dc.date.created | 2022-01-26 | - |
dc.date.issued | 2021-12 | - |
dc.identifier.issn | 1976-913X | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/41921 | - |
dc.description.abstract | Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factoryproviding effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국정보처리학회 | - |
dc.relation.isPartOf | JIPS(Journal of Information Processing Systems) | - |
dc.title | Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes | - |
dc.title.alternative | Anomaly Detection of Facilities and Non-disruptive Operation of Smart Factory Using Kubernetes | - |
dc.type | Article | - |
dc.identifier.doi | 10.3745/JIPS.01.0083 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | JIPS(Journal of Information Processing Systems), v.17, no.9, pp.1071 - 1082 | - |
dc.identifier.kciid | ART002799332 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000741646100004 | - |
dc.identifier.scopusid | 2-s2.0-85123455204 | - |
dc.citation.endPage | 1082 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 1071 | - |
dc.citation.title | JIPS(Journal of Information Processing Systems) | - |
dc.citation.volume | 17 | - |
dc.contributor.affiliatedAuthor | 이상준 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Anormal Detection | - |
dc.subject.keywordAuthor | Continuously Learning | - |
dc.subject.keywordAuthor | Kubernetes | - |
dc.subject.keywordAuthor | Non-disruptive Operation | - |
dc.subject.keywordAuthor | Smart Factory | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Soongsil University Library 369 Sangdo-Ro, Dongjak-Gu, Seoul, Korea (06978)02-820-0733
COPYRIGHT ⓒ SOONGSIL UNIVERSITY, ALL RIGHTS RESERVED.
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.