Abstract
Establishing the condition prediction model of characteristic parameters is one of the key parts in the implementation of condition based maintenance (CBM) of the hydroelectric generating unit (HGU). The performance of radial basis function neural network (RBFNN) in prediction mainly depends on the determination of the number and locations of data centers at the hidden layer. A novel approach inspired from the immune optimization principles is proposed in this paper and used to determine and optimize the structure at the hidden layer. The immune optimized RBFNN has been applied to the vibration condition prediction of the hydroturbine guiding bearing. The prediction results are compared with those by some other intelligent algorithms and the actual values, which shows the effectiveness and the preciseness of the proposed immune optimized RBFNN.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wang, S., Fan, W., Zhong, D.: Condition Maintenance Decision-Making System Based on Condition Monitoring of Main Equipment of Hydroelectric Plant. Automation of Electric Power Systems 16, 45–47 (2001)
Liu, Z., Zhou, J., Zou, M.: Condition Based Maintenance System of Hydroelectric Generating Unit. In: The 2006 IEEE International Conference on Industrial Technology, pp. 80–85. IEEE Press, Bombay (2006)
Hwang, Y., Bang, S.: An Efficient Method to Construct a Radial Basis Function Neural Network Classifier. Neural Network 9, 1495–1503 (1997)
Moody, J.E., Darken, C.: Fast Learning in Networks of Locally Tuned Processing Units. Neural Computation 2, 281–294 (1989)
Dasgupta, D.: Advances in Artificial Immune Systems. IEEE Computational Intelligence Magazine 4, 40–49 (2006)
Timmis, J., Knight, T., de Castro, L.N., Hart, E.: An Overview of Artificial Immune Systems. Natural Computation Series, pp. 51–86. Springer, Heidelberg (2004)
Dasgupta, D.: Artificial Neural Network and Artificial Immune Systems: Similarities and Differences. In: Dasgupta, D. (ed.) the IEEE International Conference on System, Man, and Cybernetics, pp. 873–878. IEEE Press, Orlando (1997)
Wu, S., Chow, T.W.S.: Induction Machine Fault Detection Using SOM-Based RBF Neural Networks. IEEE Trans. Industrial Electronics 51, 183–194 (2004)
de Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Priciple. IEEE Trans. on Evolutionary Computation 3, 239–251 (2002)
Barra, T.V., Bezerra, G.B., de Castro, L.N., Von Zuben, F.J.: An Immunological Density-Preserving Approach to the Synthesis of RBF Neural Networks for Classification. In: International Joint Conference on Neural Networks, pp. 929–935. IEEE Press, Vancouver (2006)
Liu, Z., Zhou, J., Zhang, Y., Zou, M.: Compound Characteristic Extraction Based Fault Diagnosis of Hydroelectric Generating Unit Using RBF Neural Network. Automation of Electric Power Systems 11, 87–91 (2007)
Dai, K.: Forecasting of the Feature-Based Hydroelectric Unit State. Master Dissertation, Huazhong University of Science and Technology, Wuhan (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Z., Zou, S., Liu, S., Jin, F., Lu, X. (2008). Condition Prediction of Hydroelectric Generating Unit Based on Immune Optimized RBFNN. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_95
Download citation
DOI: https://doi.org/10.1007/978-3-540-87732-5_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
Online ISBN: 978-3-540-87732-5
eBook Packages: Computer ScienceComputer Science (R0)