There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 40 | - |
dc.citation.startPage | 27 | - |
dc.citation.title | RELIABILITY ENGINEERING & SYSTEM SAFETY | - |
dc.citation.volume | 184 | - |
dc.contributor.author | Jeong, Haedong | - |
dc.contributor.author | Park, Bumsoo | - |
dc.contributor.author | Park, Seungtae | - |
dc.contributor.author | Min, Hyungcheol | - |
dc.contributor.author | Lee, Seungchul | - |
dc.date.accessioned | 2023-12-21T19:16:08Z | - |
dc.date.available | 2023-12-21T19:16:08Z | - |
dc.date.created | 2019-02-28 | - |
dc.date.issued | 2019-04 | - |
dc.description.abstract | Manufacturing machinery is becoming increasingly complicated, and machinery breakdowns not only reduce efficiency, but also pose safety hazards. Due to the needs for maintaining high reliability within facility operation, various methods for condition monitoring are suggested as the importance of maintenance has increased. Among the various prognostics and health management (PHM) techniques, this paper introduces a model-based fault detection and isolation (FDI) technique for the diagnosis of machine health conditions. The proposed approach identifies faults by extracting fault signal information such as the magnitude or shape of the fault based on a defined relationship between a fault signal and observer theory. To validate the proposed method, a numerical simulation is conducted to demonstrate its fault detection and identification capabilities in various situations. The proposed method and data-driven methods are then compared with regard to their fault diagnosis performance. (C) 2018 Elsevier Ltd. All rights reserved. | - |
dc.identifier.bibliographicCitation | RELIABILITY ENGINEERING & SYSTEM SAFETY, v.184, pp.27 - 40 | - |
dc.identifier.doi | 10.1016/j.ress.2018.02.007 | - |
dc.identifier.issn | 0951-8320 | - |
dc.identifier.scopusid | 2-s2.0-85042361966 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/26161 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0951832017310165?via%3Dihub | - |
dc.identifier.wosid | 000458590200005 | - |
dc.language | 영어 | - |
dc.publisher | ELSEVIER SCI LTD | - |
dc.title | Fault detection and identification method using observer-based residuals | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial; Operations Research & Management Science | - |
dc.relation.journalResearchArea | Engineering; Operations Research & Management Science | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | SYSTEMS | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.