Abstract
In order to improve the adaptability and effectiveness of anomaly condition recognition for hydropower unit, an adaptive anomaly detection method of hydropower unit vibration is presented based on radial basis function (RBF) neural network. The optimal value of vibration parameters in real-time condition is dynamically computed by using RBF neural network in this method. The relative distance between vibration real data and optimal value is calculated as the anomaly. This index can describe the changes of vibration parameters and identify anomalies of hydropower unit condition. The obtained results of abnormal alarm can meet the actual demands by using the proposed method in vibration monitoring of hydropower unit. This method can well describe the slow process of deterioration for vibration parameters and identify abnormal vibration in a sensitive manner. This method will be practical as to the operation guarantee of hydropower unit.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (grant number 51309258) and the Special Foundation for Excellent Young Scientists of China Institute of Water Re-sources and Hydropower Research (grant number 1421).
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© 2015 Springer International Publishing Switzerland
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An, X. (2015). Self-Adaptive Anomaly Detection Method for Hydropower Unit Vibration Based on Radial Basis Function (RBF) Neural Network. In: Wong, W. (eds) Proceedings of the 4th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 355. Springer, Cham. https://doi.org/10.1007/978-3-319-11104-9_37
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DOI: https://doi.org/10.1007/978-3-319-11104-9_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11103-2
Online ISBN: 978-3-319-11104-9
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