Root cause analysis on changes in chiller performance using linear regression
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...
| Main Authors: | Okitsu, J., Khamis, M.F.I., Majid, M.A.A., Naono, K., Sulaiman, S.A. |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.31191 / |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2014
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| Online Access: |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938804907&doi=10.1109%2fICCOINS.2014.6868400&partnerID=40&md5=a5071fbc808a2eeb36ed52affcd9af34 http://eprints.utp.edu.my/31191/ |
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utp-eprints.311912022-03-25T09:02:27Z Root cause analysis on changes in chiller performance using linear regression Okitsu, J. Khamis, M.F.I. Majid, M.A.A. Naono, K. Sulaiman, S.A. 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. Institute of Electrical and Electronics Engineers Inc. 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938804907&doi=10.1109%2fICCOINS.2014.6868400&partnerID=40&md5=a5071fbc808a2eeb36ed52affcd9af34 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. http://eprints.utp.edu.my/31191/ |
| institution |
Universiti Teknologi Petronas |
| collection |
UTP Institutional Repository |
| description |
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. |
| format |
Conference or Workshop Item |
| author |
Okitsu, J. Khamis, M.F.I. Majid, M.A.A. Naono, K. Sulaiman, S.A. |
| spellingShingle |
Okitsu, J. Khamis, M.F.I. Majid, M.A.A. Naono, K. Sulaiman, S.A. Root cause analysis on changes in chiller performance using linear regression |
| author_sort |
Okitsu, J. |
| title |
Root cause analysis on changes in chiller performance using linear regression |
| title_short |
Root cause analysis on changes in chiller performance using linear regression |
| title_full |
Root cause analysis on changes in chiller performance using linear regression |
| title_fullStr |
Root cause analysis on changes in chiller performance using linear regression |
| title_full_unstemmed |
Root cause analysis on changes in chiller performance using linear regression |
| title_sort |
root cause analysis on changes in chiller performance using linear regression |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2014 |
| url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938804907&doi=10.1109%2fICCOINS.2014.6868400&partnerID=40&md5=a5071fbc808a2eeb36ed52affcd9af34 http://eprints.utp.edu.my/31191/ |
| _version_ |
1741197533079666688 |
| score |
11.62408 |