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Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system

Luo, X. J.; Fong, K. F.; Sun, Y. J.; Leung, M. K.H.

Authors

Xiaojun Luo Xiaojun.Luo@uwe.ac.uk
Research Fellow - Evolutionary Computing and Optimisation

K. F. Fong

Y. J. Sun

M. K.H. Leung



Abstract

This paper presents a new clustering-based sensor fault detection and diagnosis (SFDD) strategy for chilled water system. For data clustering, k-means algorithm was used and the optimal quantity of clusters was determined by Davis-Bouldin value. With the cluster centroid dataset, the featuring centroid score (CS) was determined for the fault-free sensor reading dataset thus the threshold for fault detection could be set. The database for sensor fault detection was then formed. By characterizing the CS patterns of different types of sensor fault, the database for sensor fault diagnosis was generated accordingly. Various sensor fault types could be handled, including bias, drift, precision degradation and complete failure. In this study, the developed SFDD strategy was applied to the sensor of primary chilled water return temperature in a water-cooled chilled water system. With the databases of sensor fault detection and diagnosis, the real-time measured sensor readings can be examined. Once sensor fault is detected, the fault type can be confirmed within a day at soonest or 2 days at most. The smallest detectable absolute bias value, absolute drifting rate and precision degradation error could be down to 0.25 °C, 0.025 °C/h and 0.1 °C respectively, demonstrating robustness of the proposed SFDD strategy.

Citation

Luo, X. J., Fong, K. F., Sun, Y. J., & Leung, M. K. (2019). Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system. Energy and Buildings, 186, 17-36. https://doi.org/10.1016/j.enbuild.2019.01.006

Journal Article Type Article
Acceptance Date Jan 1, 2019
Online Publication Date Jan 22, 2019
Publication Date Mar 1, 2019
Publicly Available Date Jan 23, 2020
Journal Energy and Buildings
Print ISSN 0378-7788
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 186
Pages 17-36
DOI https://doi.org/10.1016/j.enbuild.2019.01.006
Public URL https://uwe-repository.worktribe.com/output/851186
Publisher URL http://doi.org/10.1016/j.enbuild.2019.01.006
Additional Information Additional Information : This is the author's accepted manuscript. The final published version is available here: http://doi.org/10.1016/j.enbuild.2019.01.006.