Sensor fault detection and identification using kernel PCA and its fast data reconstruction

2010 Chinese Control and Decision Conference, CCDC 2010

Volumn , Issue , 2010, Pages 3857-3862

  Peng, Hong Xing ^a   Wang, Rui ^a   Hai, Lin Peng ^a  

^a HENAN POLYTECHNIC UNIVERSITY   (China)

Author keywords

Data reconstruction; Distance constraint; Kernel principal component analysis; Sensor fault detection and identification

Indexed keywords

DATA RECONSTRUCTION; DISTANCE CONSTRAINTS; FAULT IDENTIFICATIONS; FAULTY SENSOR; ITERATIVE CALCULATION; KERNEL PCA; KERNEL PRINCIPAL COMPONENT ANALYSIS; ON-LINE APPLICATIONS; SENSOR FAULT; SENSOR FAULT DETECTION; SIMULATION RESULT;

FAULT DETECTION; IDENTIFICATION (CONTROL SYSTEMS); REPAIR; SENSORS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77955352863     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CCDC.2010.5498464     Document Type: Conference Paper

References (15)

1. Dunia R, Qin S J, Edgar T F, et al. Identification of faulty sensors using principal component analysis[J]. AIChE Journal. 1996, 42(10): 2797-2812.
2. Choi S W, Lee C, Lee J M, et al. Fault detection and identification of nonlinear processes based on kernel PCA[J]. Chemometrics and Intelligent Laboratory Systems. 2005, 75(1): 55-67. (Pubitemid 40038191)
3. Lennox J, Rosen C. Adaptive multiscale principal components analysis for online monitoring of wastewater treatment[J]. Water Science and Technology. 2002, 45(4-5): 227-235. (Pubitemid 34256544)
4. Xiao F, Wang S, Xu X, et al. An isolation enhanced PCA method with expert-based multivariate decoupling for sensor FDD in air-conditioning systems[J]. Applied Thermal Engineering. 2009(29): 712-722.
5. Dong D, Mcavoy T J. Nonlinear principal component analysis--Based on principal curves and neural networks[J]. Computers & Chemical Engineering. 1996, 20(1): 65-78. (Pubitemid 126695488)
6. Mark A K. Nonlinear principal component analysis using autoassociative neural networks[J]. AIChE Journal. 1991, 37(2): 233-243.
7. Lee J M, Yoo C K, Choi S W, et al. Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science. 2004, 59(1): 223-234. (Pubitemid 38034007)
8. Scholkopf B, Smola A, Klaus-robertMüller. Nonlinear Component Analysis as a Kernel Eigenvalue Problem[J]. Neural Computation. 1998, 10(5): 1299-1399.
9. Cho I, Lee J, Choi S W, et al. Sensor fault identification based on kernel principal component analysis[C]. Taipei,Taiwan: 2004.
10. Takahashi T, Kurita T. Robust De-noising by Kernel PCA[J]. Lecture Notes in Computer Science. 2002, 2415: 739-744.
11. Kwok J T, Tsang I W. The pre-image problem in kernel methods[J]. IEEE Transactions On Neural Networks. 2004, 15(6): 1517-1525.
12. Christianini N, Shawe-taylor J. An introduction to support vector machines and other kernel-based learning methods[M]. UK: Cambridge University Press, 2000.
13. Mika S, Scholkopf B, Smola A, et al. Kernel PCA and de-noising in feature spaces[C]. MIT Press, 1999.
14. Williams C K I. On a Connection between Kernel PCA and Metric Multidimensional Scaling[J]. Machine Learning. 2002, 46(1-3): 11-19.
15. Gower J C. Adding a point to vector diagrams in multivariate analysis[J]. Biometrika. 1968, 55(3): 582-585.