Process monitoring and fault diagnosis of condenser using KPCA and PSVM

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering

Volumn 27, Issue 14, 2007, Pages 56-61

  Zhang, Xi ^a   Yan, Wei Wu ^a   Liu, Zhen Ya ^b   Shao, Hui He ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b STATE GRID CORPORATION OF CHINA   (China)

Author keywords

Fault diagnosis; Kernel principle component analysis; Process monitoring; Proximal support vector machine

Indexed keywords

FAULT DETECTION; SUPPORT VECTOR MACHINES; TURBOGENERATORS;

FAULT DIAGNOSIS; KERNEL PRINCIPLE COMPONENT ANALYSIS (KPCA); NONLINEAR MAPPING FUNCTION; STATISTICS VALUE;

PROCESS MONITORING;

EID: 34250758256     PISSN: 02588013     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. Hou Guolian, Sun Xiaogang, Zhang Jianhua, et al. Research on fault diagnosis of condenser via nonlinear principal component analysis and probabilistic neural networks [J]. Proceedings of the CSEE, 2005, 25(18): 104-108 (in Chinese).
2. Wang Peihong, Zhu Yuna, Jia Junying, et al. Application of fuzzy pattern recognition to fault diagnosis of condensers [J]. Proceedings of the CSEE, 1999, 19(10): 46-49 (in Chinese).
3. Ma Liangyu, Wang Bingshu, Tong Zhensheng, et al. Fuzzy pattern recognition and artificial neural networks used for fault diagnosis of the doublechannel condenser [J]. Proceedings of the CSEE, 2001, 21(8): 68-73 (in Chinese).
4. Zhang Jianhua, Hou Guolian, Zhang Wei, et al. A research on fuzzy rules and genetic algorithm based method for a condenser fault diagnosis [J]. Proceedings of the CSEE, 2004, 24(4): 205-209 (in Chinese).
5. Yu Zhiwei, Su Baoku, Zeng Ming. Application of wavelet packet in fault diagnosis system of large scale DC motor rotor [J]. Proceedings of the CSEE, 2005, 25(22): 158-162 (in Chinese).
6. Zhang Guoyun, Zhang Jing. Fuzzy SVM-based multilevel binary tree classifier for fault diagnosis of hydro turbine speed regulating system [J]. Proceedings of the CSEE, 2005, 25(8): 100-104 (in Chinese).
7. Jiang Dongxiang, Diao Jinhui, Zhao Gang, et al. Study on methods of vibration fault diagnosis based on time-frequency contour map for turbine generator unit [J]. Proceedings of the CSEE, 2005, 25(6): 146-151 (in Chinese).
8. Zhao Xu, Shao Huihe. A new combination method for on-line process monitoring and fault diagnosis [C]. IEEE ISIE, Dubrovnik Croatia, 2005: 1715-1720.
9. Yan Weiwu, Shao Huihe, Wang Xiaofan. Soft sensing modeling based on support vector machine and Bayesian model selection [J]. Computers and Chemical Engineering, 2004, 28(8): 1489-1498.
10. Mehranbod N, Soroush M, Panjapompon C. A method of sensor fault detection andidentification [J]. Journal of Process Control, 2005, 15(3): 321-339
11. Jia F, Martin E B, Morris A J. Nonlinear principal components analysis with application to processfault detection [J]. International Journal of System Science, 2001, 31(11): 1473-1487.
12. Zhao Shijian, Zhang Jie, Xu Yongmao. Performance monitoring of processes with multiple operating modes through multiple PLS models [J]. Journal of Process Control, 2006, 16(7): 763-772.
13. Jong-Min Lee, ChangKyoo Yoo, Sang Wook Choi, et al. Nonlinear process monitoring using kernel principal component analysis [J]. Chemical Engineering Science, 2004, 59(1): 223-234.
14. Schölkopf B, Smola A, Müller K R. Nonlinear component analysis as a kernel eigenvalue problem [J]. Neural Computation, 1998, 10(5): 1299-1319.
15. Mjuller K R, Mika S, Jatsch R, et al. An introduction to kernel-based learning algorithms [J]. IEEE Transactions of Neural Networks, 2001, 12(2): 181-202.
16. Fung G, Mangasarian O L. Proximal support vector machine classifiers [C]. Proc 7th ACM SIGKDD Intl conf on Knowledge Discovery and Data Mining2001, San Francisco, USA, 2001: 77-86.