Abstract
In this paper, a new style radial basis function (RBF) neural network is used for fault diagnosis of the high-pressure feed-water heater system of a coal-fired power generating unit. The structure of the RBF network and its training algorithm are given. Another important factor to realize neural network based fault diagnosis, fault symptom fuzzy calculating methods for two different fault symptoms and their integrated calculation, are discussed in detail. The high-pressure feed-water heater system of a 300MW coal-fired power generating unit is taken as a fault diagnosis example. The fault knowledge library of the system is summarized. The fault diagnosis is further realized based on the above RBF neural network. It is shown that good diagnostic results can be acquired with RBF neural network method by using the fault fuzzy knowledge library of the high-pressure heater system.
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