Study on a hybrid SVM model for chiller FDD applications

Applied Thermal Engineering

Volumn 31, Issue 4, 2011, Pages 582-592

  Han, H ^a   Gu, B ^a   Kang, J ^a   Li, Z R ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b TONGJI UNIVERSITY   (China)

Author keywords

Chiller; Fault indicative feature; FDD; Refrigeration; Sensor; Support vector machine

Indexed keywords

CHILLER; CHILLER SYSTEM; COMPARISON ANALYSIS; COMPUTATIONAL TIME; EVALUATION METHOD; FALSE ALARM RATE; FAULT DETECTION AND DIAGNOSIS; FAULT INDICATIVE FEATURE; FDD; FEATURE SELECTION; FEATURE SETS; FEATURE SUBSET; HIT RATE; HYBRID MODEL; INDIVIDUAL PERFORMANCE; PARAMETER-TUNING; POTENTIAL FEATURES; SVM MODEL;

COOLING SYSTEMS; FAULT DETECTION; GENETIC ALGORITHMS; REFRIGERANTS; REFRIGERATION; SENSORS; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 78649964371     PISSN: 13594311     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.applthermaleng.2010.10.021     Document Type: Article

References (33)

1. M.C. Comstock, J.E. Braun, and E.A. Groll The sensitivity of chiller performance to common faults International Journal of HVAC&R Research 7 3 2001 263 279
2. M.B. Bailey. The design and viability of a probabilistic fault detection and diagnosis method for vapor compression cycle equipment. Ph.D. Thesis. School of Civil Engineering, University of Colorado; 1998.
3. H. Grimmelius, J. Woud, and G. Been On-line failure diagnosis for compression refrigeration plants International Journal of Refrigeration 18 1 1995 31 41
4. H. Peitsman, and V. Bakker Application of black-box models to HVAC systems for fault detection ASHRAE Transactions 102 1 1996 628 640
5. T.M. Rossi, and J.E. Braun A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners International Journal of HVAC&R Research 3 1 1997 19 37
6. J. Cui, and S.W. Wang A model-based online fault detection and diagnosis strategy for centrifugal chiller systems International Journal of Thermal Science 44 10 2005 986 999
7. Y.Z. Jia, and T. Reddy Characteristic physical parameter approach to modeling chillers suitable for fault detection, diagnosis, and evaluation Journal of Solar Energy Engineering, Transactions of the ASME 125 3 2003 258 265
8. Derk J. Swider A comparison of empirically based steady-state models for vapor-compression liquid chillers Applied Thermal Engineering 23 2003 539 556
9. D.J. Swider, M.W. Browne, P.K. Bansal, and V. Kecman Modelling of vapour-compression liquid chillers with neural networks Applied Thermal Engineering 21 2001 311 329
10. I.N. Grace, D. Datta, and S.A. Tassou Sensitivity of refrigeration system performance to charge levels and parameters for on-line leak detection Applied Thermal Engineering 25 2005 557 566
11. M. Kim, W.V. Payne, Piotr A. Domanski, Seok Ho Yoon, and Christian J.L. Hermes Performance of a residential heat pump operating in the cooling mode with single faults imposed Applied Thermal Engineering 29 2009 770 778
12. X.H. Xu, F. Xiao, and S.W. Wang Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods Applied Thermal Engineering 28 2008 226 237
13. F. Xiao, S.W. Wang, X.H. Xu, and G.M. Ge An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems Applied Thermal Engineering 29 2009 712 722
14. H. Han, Z.K. Cao, B. Gu, and N. Ren PCA-SVM-based automated fault detection and diagnosis (AFDD) for vapor-compression refrigeration systems International Journal of HVAC&R Research 16 3 2010 295 313
15. M.C. Comstock, and J.E. Braun Development of analysis tools for the evaluation of fault detection and diagnostics for chillers ASHRAE Research Project 1043 1999 HL 99-20, Report # 4036-3
16. M.C. Comstock, and J.E. Braun Experimental data from fault detection and diagnostic studies on a centrifugal chiller ASHRAE Research Project 1043 1999 HL 99-18, Report # 4036-1
17. N. Cristianini, and J. Shawe-Taylor An Introduction to Support Vector Machines and Other Kernel-based Learning Methods 2000 Cambridge University Press
18. C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Mining and Knowledge Discovery 2 2 1998 1 47
19. H.T. Lin, C.J. Lin, A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods, Technical report, Department of Computer Science, National Taiwan University, 2003. http://www.csie.ntu.edu.tw/ ∼cjlin/papers/tanh.pdf.
20. C.W. Hsu, and C.J. Lin A comparison of methods for multi-class support vector machines IEEE Transactions on Neural Networks 13 2002 415 425
21. J.C. Platt, N. Cristianini, and J.S. Taylor Large Margin DAGs for Multiclass Classification 2000 MIT Press http://research.microsoft.com/pubs/ 68541/dagsvm.pdf
22. R. Rifkin, and A. Klautau In defense of one-vs.-all classification Journal of Machine Learning Research 5 2004 101 141
23. Y.G. Liu, Z.S. You, and L.P. Cao A novel and quick SVM-based multi-class classifier Pattern Recognition 39 2006 2258 2264
24. H.T. Lin, C.J. Lin, A practical guide to support vector classification. Technical reports, Department of Computer Science and Information Engineering, National Taiwan University, 2004. Available at: http://www.csie.ntu.edu.tw/ ∼cjlin/papers/guide/guide.pdf.
25. D.E. Goldberg Genetic Algorithms in Search, Optimization & Machine Learning 1989 Addison-Wesley
26. A.S. Glass, P. Gruber, M. Roos, and J. Todtli Qualitative model based fault detection in air-handling units IEEE Control Systems Magazine 15 4 1995 11 22
27. Ian H. Witten, and Eibe Frank Data Mining: Practical Machine Learning Tools and Techniques second ed. 2005 Morgan Kaufmann Publishers
28. T. Fawcett ROC Graphs: Notes and Practical Considerations for Researchers 2004 Kluwer Academic Publishers Netherlands
29. I. Ye'lamos, M. Graells, and L. Puigjaner Simultaneous fault diagnosis in chemical plants using a MultiLabel approach AIChE Journal 53 11 2007 2871 2884
30. C.C. Chang, and C.J. Lin LIBSVM: A Library for Support Vector Machines 2001 Software available at: http://www.csie.ntu.edu.tw/∼cjlin/libsvm/
31. T.F. Wu, C.J. Lin, and R.C. Weng Probability estimates for multi-class classi-cation by pairwise coupling Journal of Machine Learning Research 5 2004 975 1005 Available at: http://www.csie.ntu.edu.tw/∼cjlin/papers/svmprob/ svmprob.pdf
32. Ross Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann Publishers San Mateo, CA
33. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations 2005 China Machine Press Software available at: www.cs.waikato.ac.nz/ml/weka