Title :
Network turbo unit monitoring system based on advanced diagnostic strategies
Author :
Zhang, Yong ; Wang, Ning-Ling
Author_Institution :
North China Electr. Power Univ., Baoding, China
Abstract :
It is a trend to detect and analyze the faults of turbo-generator unit by means of advanced diagnostic theory and open network structure. A turbo-unit fault diagnosis system for shaft monitoring based on the improved RBF neural network diagnostic model is introduced in this paper, by which the typical faults and some new-type ones are to be diagnosed directly, besides it is of the function of modifying the samples continuously. More importantly, an open Browser/Server network structure is proposed to realize the operating condition information sharing among the levels of equipment managers, production managers and remote experts.
Keywords :
computerised monitoring; fault diagnosis; power engineering computing; radial basis function networks; turbogenerators; RBF neural network diagnostic model; advanced diagnostic strategies; advanced diagnostic theory; fault diagnosis system; network turbo unit monitoring system; open network structure; shaft monitoring; turbo-generator unit; Data acquisition; Databases; Fault diagnosis; Information analysis; Monitoring; Network servers; Power system management; Shafts; Signal analysis; Web server;
Conference_Titel :
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN :
0-7803-8403-2
DOI :
10.1109/ICMLC.2004.1380383
