Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27090
Title: A statistical fault detection and diagnosis method for centrifugal chillers based on exponentially-weighted moving average control charts and support vector regression
Authors: Zhao, Y
Wang, S 
Xiao, F 
Keywords: Centrifugal chiller
EWMA control chart
Fault detection
Fault diagnosis
Support vector regression
Issue Date: 2013
Publisher: Pergamon Press
Source: Applied thermal engineering, 2013, v. 51, no. 1-2, p. 560-572 How to cite?
Journal: Applied thermal engineering 
Abstract: This paper presents a new fault detection and diagnosis (FDD) method for centrifugal chillers of building air-conditioning systems. Firstly, the Support Vector Regression (SVR) is adopted to develop the reference PI models. A new PI, namely the heat transfer efficiency of the sub-cooling section (εsc), is proposed to improve the FDD performance. Secondly, the Exponentially-Weighted Moving Average (EWMA) control charts are introduced to detect faults in a statistical way to improve the ratios of correctly detected points. Thirdly, when faults are detected, diagnosis follows which is based on a proposed FDD rule table. Six typical chiller component faults are concerned in this paper. This method is validated using the real-time experimental data from the ASHRAE RP-1043. Test results show that the combined use of SVR and EWMA can achieve the best performance. Results also show that significant improvements are achieved compared with a commonly used method using Multiple Linear Regression (MLR) and t-statistic.
URI: http://hdl.handle.net/10397/27090
ISSN: 1359-4311
EISSN: 1873-5606
DOI: 10.1016/j.applthermaleng.2012.09.030
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

32
Last Week
0
Last month
1
Citations as of Aug 18, 2017

WEB OF SCIENCETM
Citations

22
Last Week
0
Last month
0
Citations as of Aug 15, 2017

Page view(s)

31
Last Week
3
Last month
Checked on Aug 21, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.