Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.number | 1 | - |
dc.citation.startPage | 154 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 18 | - |
dc.contributor.author | Baek, Woosang | - |
dc.contributor.author | Baek, Sujeong | - |
dc.contributor.author | Kim, Duck Young | - |
dc.date.accessioned | 2023-12-21T21:13:43Z | - |
dc.date.available | 2023-12-21T21:13:43Z | - |
dc.date.created | 2018-02-08 | - |
dc.date.issued | 2018-01 | - |
dc.description.abstract | Many fault detection methods have been proposed for monitoring the health of various industrial systems. Characterizing the monitored signals is a prerequisite for selecting an appropriate detection method. However, fault detection methods tend to be decided with user’s subjective knowledge or their familiarity with the method, rather than following a predefined selection rule. This study investigates the performance sensitivity of two detection methods, with respect to status signal characteristics of given systems: abrupt variance, characteristic indicator, discernable frequency, and discernable index. Relation between key characteristics indicators from four different real-world systems and the performance of two fault detection methods using pattern recognition are evaluated. | - |
dc.identifier.bibliographicCitation | SENSORS, v.18, no.1, pp.154 | - |
dc.identifier.doi | 10.3390/s18010154 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.scopusid | 2-s2.0-85040317300 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/23664 | - |
dc.identifier.url | http://www.mdpi.com/1424-8220/18/1/154 | - |
dc.identifier.wosid | 000423286300153 | - |
dc.language | 영어 | - |
dc.publisher | MDPI AG | - |
dc.title | Characterization of system status signals for multivariate time series discretization based on frequency and amplitude variation | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical; Engineering, Electrical & Electronic; Instruments & Instrumentation | - |
dc.relation.journalResearchArea | Chemistry; Engineering; Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | fault detection | - |
dc.subject.keywordAuthor | sensor data | - |
dc.subject.keywordAuthor | frequency domain | - |
dc.subject.keywordPlus | FAULT-DETECTION | - |
dc.subject.keywordPlus | TRANSFORM | - |
dc.subject.keywordPlus | KNOWLEDGE | - |
dc.subject.keywordPlus | MODEL | - |
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