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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© 2006 Springer-Verlag Berlin Heidelberg
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Ma, L., Ma, Y., Ma, J. (2006). Fault Diagnosis for the Feedwater Heater System of a 300MW Coal-Fired Power Generating Unit Based on RBF Neural Network. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_87
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DOI: https://doi.org/10.1007/11739685_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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