eprintid: 4222
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/42/22
datestamp: 2023-11-09 16:15:53
lastmod: 2023-11-09 16:15:53
status_changed: 2023-11-09 15:57:56
type: conference_item
metadata_visibility: show
creators_name: Okitsu, J.
creators_name: Khamis, M.F.I.
creators_name: Majid, M.A.A.
creators_name: Naono, K.
creators_name: Sulaiman, S.A.
title: Root cause analysis on changes in chiller performance using linear regression
ispublished: pub
keywords: Absorption cooling; Chemical analysis; Constraint theory; Cooling systems; Corrosion; Linear regression; Maintenance, Chiller; Component failures; Contribution ratios; District cooling; Performance degradation; Root cause analysis; Steam absorption; Theory of constraint, Time series analysis
note: cited By 3; Conference of 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:112912
abstract: Gas District Cooling (GDC) plants, designed to be environmentally efficient, require frequent maintenances, in order to avoid corrosions or leakages from the chemical reactions in Steam Absorption Chillers (SACs) of the plant. However, most of the plant experts face difficulty that the positive and the negative effects from the SAC maintenances are not clear. This is because there are various metrics to indicate GDC SAC performance, but they don't have enough information to describe chiller internal conditions. The paper describes a method to detect the root cause of the GDC SAC performance changes. Specifically, (1) the chiller performance is modeled by linear regression on the performance related sensor data, and (2) the root cause is determined by time series analysis of the sensor contribution ratios to the performance in accordance of the concept of theory of constraints (TOC). Evaluations in Universiti Teknologi Petronas (UTP) GDC plant showed that the method determined the root cause correctly in 3 cases out of 4 problem cases. Because the method determines the root cause only from the plant operation historical data without any inspections, it is generalized to detect component failures and other plant anomalies. © 2014 IEEE.
date: 2014
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938804907&doi=10.1109%2fICCOINS.2014.6868400&partnerID=40&md5=a5071fbc808a2eeb36ed52affcd9af34
id_number: 10.1109/ICCOINS.2014.6868400
full_text_status: none
publication: 2014 International Conference on Computer and Information Sciences, ICCOINS 2014 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2014 - Proceedings
refereed: TRUE
isbn: 9781479943913
citation:   Okitsu, J. and Khamis, M.F.I. and Majid, M.A.A. and Naono, K. and Sulaiman, S.A.  (2014) Root cause analysis on changes in chiller performance using linear regression.  In: UNSPECIFIED.