Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26903
Title: Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods
Authors: Xu, X
Xiao, F 
Wang, S 
Keywords: Approximation coefficients
Centrifugal chiller
Fault detection and diagnosis and estimation
Principal component analysis
Sensor fault
Wavelet analysis
Issue Date: 2008
Publisher: Pergamon Press
Source: Applied thermal engineering, 2008, v. 28, no. 2-3, p. 226-237 How to cite?
Journal: Applied thermal engineering 
Abstract: An enhanced sensor fault detection, diagnosis and estimation (FDD&E) strategy is developed for centrifugal chillers using wavelet analysis method and principal component analysis (PCA) method. A number of sensors of concern in chiller system monitoring and control are assigned into a PCA model, which can group these correlated variables and capture the systematic variations of chillers. Raw measurements or simple processing measurements of sensors may deteriorate the performance of sensor FDD&E strategy using PCA because of the embodied noises and dynamics. Wavelet analysis can extract the approximations of sensor measurements by separating noises and dynamics. Using these approximation coefficients for PCA modeling we can improve the capability and reliability of fault detection and diagnosis as well as the accuracy of sensor fault estimation. This wavelet-PCA-based sensor FDD&E strategy was validated using field operation data of an existing centrifugal chiller plant while various sensor faults of different magnitudes were introduced. The results demonstrate that this strategy can produce better performance of sensor FDD&E in terms of fault detection ratio, diagnosis ratio and estimation accuracy by comparing with conventional PCA-based sensor FDD&E strategy using raw or simple processing measurements for PCA modeling.
URI: http://hdl.handle.net/10397/26903
ISSN: 1359-4311
EISSN: 1873-5606
DOI: 10.1016/j.applthermaleng.2007.03.021
Appears in Collections:Journal/Magazine Article

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

SCOPUSTM   
Citations

58
Last Week
0
Last month
3
Citations as of Oct 8, 2017

WEB OF SCIENCETM
Citations

47
Last Week
1
Last month
0
Citations as of Oct 17, 2017

Page view(s)

39
Last Week
0
Last month
Checked on Oct 15, 2017

Google ScholarTM

Check

Altmetric



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