Abstract
A novel approach for pipeline leak fault detection has been studied, which applies self-organizing fuzzy clustering neural network to identify work status. The proposed method utilized fuzzy neural clustering of DIC method instead of constructing exact mathematical model. After normalizing the sample data, together with prior knowledge, a fuzzy neural network is used to evaluate work status. An adaptive algorithm is developed to diagnose the leak fault. The experiment results have shown the validity and practicability of the method.
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© 2006 Springer-Verlag Berlin Heidelberg
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Feng, J., Zhang, H. (2006). Algorithm of Pipeline Leak Detection Based on Discrete Incremental Clustering Method. In: Huang, DS., Li, K., Irwin, G.W. (eds) Computational Intelligence. ICIC 2006. Lecture Notes in Computer Science(), vol 4114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37275-2_73
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DOI: https://doi.org/10.1007/978-3-540-37275-2_73
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37274-5
Online ISBN: 978-3-540-37275-2
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