Fault diagnosis for refrigeration systems based on principal component analysis and support vector machine

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University

Volumn 45, Issue 9, 2011, Pages

  Han, Hua ^a   Gu, Bo ^a   Ren, Neng ^b  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b Asia Engineering Center of Building Efficiency Business   (China)

Author keywords

Fault diagnosis (FD); Principal component analysis (PCA); Refrigeration system; Support vector machine (SVM)

Indexed keywords

BP NEURAL NETWORKS; DATA POOL; DESIGNED EXPERIMENTS; FAULT DETECTION AND DIAGNOSIS; HISTORICAL DATA; MODEL TRAINING; MULTICLASS CLASSIFICATION PROBLEMS; PRINCIPAL COMPONENT ANALYSIS (PCA); REFRIGERATION SYSTEM; SMALL SAMPLES; SUPPORT VECTOR; SVM MODEL; SVM(SUPPORT VECTOR MACHINE); TRAINING TIME;

ALGORITHMS; FAILURE ANALYSIS; FAULT DETECTION; FEATURE EXTRACTION; NEURAL NETWORKS; REFRIGERATION; REFRIGERATORS; SUPPORT VECTOR MACHINES;

PRINCIPAL COMPONENT ANALYSIS;

EID: 81355133067     PISSN: 10062467     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (17)

1. Qin S J, Yue H Y, Dunia R. Self-validating inferential sensors with application to air emission monitoring [J]. Ind Eng Chen Res, 1997, 36(5): 1675-1685.
2. QIU Tian, DING Yan-jun, WU Zhan-song. Sensor fault detection statistics based on principal component analysis [J]. Journal of Shanghai Jiaotong University, 2006, 40(8): 1447-1450.
3. 2003.
4. Vapnik V N. 2004: 293-323.
5. Choi K, Namburu S M, Azam M, et al. Fault diagnosis in HVAC chillers: Adaptability of a data-driven fault detection and isolation approach [J]. IEEE Instrum Meas Mag, 2005, 8(3): 24-32.
6. Cherkassky V, Ma Y Q. Practical selection of SVM parameters and noise estimation for SVM regression [J]. Neural Networks, 2004, 17(1): 113-126.
7. Lee J M, Yoo C, Choi S W, et al. Nonlinear process monitoring using kernel principle component analysis [J]. Chemical Engineering Science, 2004, 59(1): 223-234.
8. Valle S, Li W, Qin S J. Selection of the number of principal components: The variance of the reconstruction error criterion with a comparison to other methods [J]. Industrial and Engineering Chemistry Research, 1999, 38(11): 4389-4401.
9. Cui J T. A robust fault detection and diagnosis strategy for centrifugal chillers [D]. Hong Kong: Department of Building Services Engineering, Hong Kong Polytechnic University, 2005.
10. Fletcher R. Practical methods of optimization [M]. New York: John Wiley and Sons, 1987.
11. Hsu C W, Lin C J. A comparison of methods for multi-class support vector machines [J]. IEEE Transaction on Neural Networks, 2002, 13(2): 415-425.
12. Liu Y G, You Z S, Cao L P. A novel and quick SVM-based multi-class classifier [J]. Pattern Recognition, 2006, 39(11): 2258-2264.
13. Weston J, Watkins C. Support vector machines for multi-class pattern recognition [C]//Proc ESANN'99. Brussels: Facto Press, 1999: 219-224.
14. Platt J C, Cristianini N, Shawe T J. Large margin DAGs for multiclass classification [C]//Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2000: 547-553.
15. Moreira M, Mayoraz E. Improved pairwise coupling classification with correcting classifiers [C]//Euro Conf Machine Learning. London: Springer-Verlag, 1998: 160-171.
16. Yuan S F, Chu F L. Support vector machines-based fault diagnosis for turbo-pump rotor [J]. Mechanical Systems and Signal Processing, 2006, 20(4): 939-952.
17. Lin H T, Lin C J. A practical guide to support vector classification [EB/OL]. [2009-10-30]. http://www.csie.ntu.edu.tw/~cjlin/papers/tanh.pdf.