Abstract
In the paper, integration of rough set and neural network for fault is put forward and used in generator fault diagnosis. At first, rough set theory is utilized to reduce attributes of diagnosis system. Set in accordance with the practical needs, optimized decision attribute set acts as the input of artificial neural network used for fault diagnosis, which has been used for Fengman hydroelectric power station and testified the feasibility of integration of rough set and neural network. Given enough data, this method could be popularized to other generators.
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© 2004 Springer-Verlag Berlin Heidelberg
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Su, Wj., Su, Y., Zhao, H., Zhang, Xd. (2004). Integration of Rough Set and Neural Network for Application of Generator Fault Diagnosis. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_66
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DOI: https://doi.org/10.1007/978-3-540-25929-9_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
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