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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

35 Scopus Citations
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Original languageEnglish
Pages (from-to)17-36
Journal / PublicationEnergy and Buildings
Online published22 Jan 2019
Publication statusPublished - 1 Mar 2019


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.

Research Area(s)

  • Centroid score, Chilled water system, Data mining, Fault detection and diagnosis, k-means clustering, Sensor