Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jin, San | - |
dc.contributor.author | Jang, Ahmin | - |
dc.contributor.author | Lee, Donghoon | - |
dc.contributor.author | Kim, Sungjin | - |
dc.contributor.author | Shin, Minjae | - |
dc.contributor.author | Do, Sung Lok | - |
dc.date.accessioned | 2024-04-09T03:00:39Z | - |
dc.date.available | 2024-04-09T03:00:39Z | - |
dc.date.issued | 2024-02 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118472 | - |
dc.description.abstract | Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%). | - |
dc.format.extent | 21 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/app14031113 | - |
dc.identifier.scopusid | 2-s2.0-85192433237 | - |
dc.identifier.wosid | 001160480600001 | - |
dc.identifier.bibliographicCitation | Applied Sciences-basel, v.14, no.3, pp 1 - 21 | - |
dc.citation.title | Applied Sciences-basel | - |
dc.citation.volume | 14 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 21 | - |
dc.type.docType | Article | - |
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 | DIAGNOSIS STRATEGY | - |
dc.subject.keywordPlus | SYSTEMS | - |
dc.subject.keywordAuthor | supply chilled water temperature sensor | - |
dc.subject.keywordAuthor | sensor fault | - |
dc.subject.keywordAuthor | indoor thermal comfort | - |
dc.subject.keywordAuthor | energy consumption | - |
dc.subject.keywordAuthor | fault detection | - |
dc.subject.keywordAuthor | virtual sensor | - |
dc.identifier.url | https://www.mdpi.com/2076-3417/14/3/1113 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG 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.