Research on fault diagnosis of condenser via nonlinear principal component analysis and probabilistic neural networks

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

Volumn 25, Issue 18, 2005, Pages 104-108

  Hou, Guo Lian ^a   Sun, Xiao Gang ^a   Zhang, Jian Hua ^a   Jin, Wei Gang ^a  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

Condenser; Fault diagnosis; Principal component analysis neural networks (PCANN); Probabilistic neural networks (PNN); Thermal power engineering

Indexed keywords

NEURAL NETWORKS; NONLINEAR SYSTEMS; PRINCIPAL COMPONENT ANALYSIS;

FAULT DIAGNOSIS; PROBABILISTIC NEURAL NETWORKS; THERMAL POWER ENGINEERING;

STEAM CONDENSERS;

EID: 27944476441     PISSN: 02588013     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (12)

1. Li Yong, Ye Rongxue, Cao Zuqing. Condenser fault diagnostic system based on neural networks[J]. Electric Technology of Northeast China, 1995, 16(5): 5-7.
2. Li Luping. Causes of low-vacuum operation for condenser and its fault fuzzy diagnosis[J]. Turbine Technology, 1993, 35(5): 45-48.
3. Wang Jian. Integrated fault diagnosis system based on parameters for thermal cycle of large power generation units[D]. Beijing: Tsinghua University, 1996.
4. 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.
5. Ma Liangyu, Wang Bingshu, Tong Zhensheng et al. Fuzzy pattern recognition and artificial neural networks used for fault diagnosis of the double channel condenser[J]. Proceedings of the CSEE, 2001, 21(8): 68-73.
6. 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.
7. Mark A Kramer. Nonlinear principal component analysis Using Autoassociative Neural Network[J]. AICHE Journal, 1991, 37(2): 233-243.
8. Malthouse E C, Mah R, Tamhane A. Some theoretical results on nonlinear principal component analysis[C]. Proceedings of American Control Conference, USA, 1995.
9. Raghu P P, Yegnanarayana B. Supervised texture classification using a probabilistic neural network and constraint satisfaction model[J]. IEEE Trans. on Neural Networks, 1998, 9(3): 516-522.
10. Specht D F. Probabilistic neural network[J]. Neural Networks, 1990, 1(3): 109-118.