Abstract
This paper presents a new method that applies fuzzy logic, rough set theory and SOFM neural network to rotating machinery fault diagnosis. In this method, firstly, relationships between the fault causations and fault symptoms are established by fuzzy logics. Then the Rough Set Theory (RST) is applied to obtain a minimal sufficient subset of features, which is helpful to simplify the structure of neural network. Next, the 2-dimension output mapping of the standard fault samples (training samples) is obtained by a self-organizing neural network. Finally, we input some simulation samples (testing samples) and gain the reasonable conclusions by comparison between the two output mappings. Experimental results have demonstrated the effectiveness of this method and its nice prospect of applying to rotating machinery fault diagnosis.
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© 2005 Springer-Verlag Berlin Heidelberg
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Jiang, D., Li, K., Zhao, G., Diao, J. (2005). Application of Fuzzy SOFM Neural Network and Rough Set Theory on Fault Diagnosis for Rotating Machinery. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_90
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DOI: https://doi.org/10.1007/11427469_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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