Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 배석주 | - |
dc.date.accessioned | 2020-03-25T02:00:35Z | - |
dc.date.available | 2020-03-25T02:00:35Z | - |
dc.date.issued | 2019-03 | - |
dc.identifier.citation | 신뢰성 응용연구, v. 19, NO 1, Page. 22-30 | en_US |
dc.identifier.issn | 1738-9895 | - |
dc.identifier.uri | http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07994423&language=ko_KR | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/139438 | - |
dc.description.abstract | Purpose: Condition-based maintenance (CBM) is widely used to decrease the risk of equipment failures. A signal data indicating the health status of equipments is continuously measured in CBM. This article proposes a fault detection and diagnosis approach for smart factory equipments based on the signal processing and feature extraction techniques using a support vector machine (SVM). Methods: We propose a discrete wavelet transform (DWT) as one of signal processing methods. After processing the signal data, we derive the representative energy spectrum through various measures such as mean, median, variance, and interquartile range (IQR). Finally, the SVM is used to classify two classes based on Gaussian radial basis function (RBF) kernel. Results: we applied the proposed method to signal data collected from the equipment. We compared the classification accuracy of the SVM. At window length of , the wavelet spectrum through the variance measure provides the best classification accuracy for the signal data of the equipment. Conclusion: In this article, fault detection and diagnosis methods for smart factory equipments are proposed. | en_US |
dc.description.sponsorship | * 이 논문은 2018년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(No. 2018R1D1A1A09083149). 본 연구는 한국전력공사의 2017년 선정 기초연구개발 과제 연구비에 의해 지원되었음(과제번호: R18XA06-46). | en_US |
dc.language.iso | ko_KR | en_US |
dc.publisher | 한국신뢰성학회 | en_US |
dc.subject | Feature Extraction | en_US |
dc.subject | Smart Factory | en_US |
dc.subject | Signal Processing | en_US |
dc.subject | Support Vector Machine | en_US |
dc.subject | Wavelet Transform | en_US |
dc.title | 웨이블릿 스펙트럼을 이용한스마트 팩토리 설비의 이상감지 및 진단 | en_US |
dc.title.alternative | Fault Detection and Diagnosis of Smart Factory Equipments Using Wavelet Spectrum | en_US |
dc.type | Article | en_US |
dc.relation.no | 1 | - |
dc.relation.volume | 19 | - |
dc.identifier.doi | 10.33162/JAR.2019.03.19.1.22 | - |
dc.relation.page | 22-30 | - |
dc.relation.journal | 신뢰성 응용연구 | - |
dc.contributor.googleauthor | 문병민 | - |
dc.contributor.googleauthor | 임문원 | - |
dc.contributor.googleauthor | 김성준 | - |
dc.contributor.googleauthor | 배석주 | - |
dc.contributor.googleauthor | Mun, Byeong Min | - |
dc.contributor.googleauthor | Lim, Munwon | - |
dc.contributor.googleauthor | Kim, Seong-Joon | - |
dc.contributor.googleauthor | Bae, Suk Joo | - |
dc.relation.code | 2019035073 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF INDUSTRIAL ENGINEERING | - |
dc.identifier.pid | sjbae | - |
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