A STUDY ON A NETWORK TURBO-UNIT SHAFT MONITOR BASED ON THE ADAVANCED DIAGNOSTIC STRATAGEM
Yong Zhang · Proceedings of the CSEE · 2002
It is the inexomble trend for the current power station to adopt the advanced intelligent systematic theory as well as the safety and opening network system to realize the safety and economic operation of the turbo-generator shaft system. This paper introduces a set of shaft-monitoring system based on the improved RBFNN neural network fault diagnosis model, which adopt advanced mixed sub-network structure with multi-input and single-output. This kind of model can easily solve some problems such as the difficulty in determining the number of node and the over-long time of off-line operation. It has been certified that the model has the ability to detect exactly all kinds of typical fault of the turbo-shafting system, and it is a function for detecting the new fault so as to modify the sampling value on time .The framework of this monitor system is suppied with Brower/Server which can on one hand guarantee a safety operation of the equipment, on the other hand, realize the information sharing of the operation parameters among the following levels: equipment management station (MCR of the power station),operation management station and the remote expert station, and so on..