Abstract
This paper proposes a novel neural network called Extension Neural Network-Type 1 (ENN1) for vibration fault recognition according to generator vibration characteristic spectra. The proposed ENN1 has a very simple structure and permits fast adaptive processes for new training data. Moreover, the learning speed of the proposed ENN1 is shown to be faster than the previous approaches. The proposed method has been tested on practical diagnostic records in China with rather encouraging results.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, Mh. (2006). Vibration Fault Diagnosis of Large Generator Sets Using Extension Neural Network-Type 1. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_201
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DOI: https://doi.org/10.1007/11760023_201
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
eBook Packages: Computer ScienceComputer Science (R0)