Energy characteristics prediction of centrifugal pumps based on artificial neural network

被引：0
|
作者：
Tan M. [1 ]
Liu H. [1 ]
Yuan S. [1 ]
Wang Y. [1 ]
Wang K. [1 ]
机构：
[1] Technical and Research Center of Fluid Machinery Engineering, Jiangsu University
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery | 2010年 / 41卷 / 11期
关键词：
Centrifugal pumps; Characteristics prediction; Neural network;
D O I：
10.3969/j.issn.1000-1298.2010.11.010
中图分类号：
学科分类号：
摘要：
The application of the BP and RBF artificial neural networks in energy characteristics prediction of centrifugal pumps was summarized. The structure and characteristics of the two artificial neural networks were introduced in detail. The models of BP and RBF artificial neural network were established respectively to predict the centrifugal pump energy characteristics. The characteristics data of 57 centrifugal pumps were used to train the two models, and the data of the other 6 centrifugal pumps were used to test the two models. The study shows that the prediction results of the two networks are closer and the trends of prediction results are the same for the two networks. The precision of BP network is a little higher than that of RBF network. The head average prediction discrepancy for BP network is 3.85% and the efficiency average discrepancy is 1.39% points. The head average prediction discrepancy for RBF network is 4.79% and the efficiency average discrepancy is 3.43% points. The prediction time of RBF network is only half the time of BP network.
引用
收藏
页码：52 / 56
页数：4
相关论文
共 10 条
  • [1] Tan M., Yuan S., Liu H., The actuality and prospect of characteristics prediction for centrifugal pumps, Pump Technology, 3, pp. 23-25, (2005)
  • [2] Nie S., Guan X., Liu H., The research of characteristic prediction for centrifugal pumps based on artificial neural network, Pump Technology, 5, pp. 16-18, (2002)
  • [3] Cong X., Yuan S., Yuan D., Et al., Performance forecast of centrifugal pump by BP neural network, Transactions of the Chinese Society for Agricultural Machinery, 37, 11, pp. 56-59, (2006)
  • [4] Liu G., Jiang J., Fu X., Predicting complete characteristics of pumps by using BP neural network, Journal of Wuhan University of Hydraulic and Electric Engineering, 33, 2, pp. 37-39, (2000)
  • [5] Golcu M., Neural network analysis of head-flow curves in deep well pumps, Energy Conversion and Management, 47, 7-8, pp. 992-1003, (2006)
  • [6] Garg A., Sastry P.S., Pandey M., Et al., Numerical simulation and artificial neural network modeling of natural circulation boiling water reactor, Nuclear Engineering and Design, 237, 3, pp. 230-239, (2007)
  • [7] (2003)
  • [8] Liu H., Wang Y., Tan M., Et al., Prediction on cavitation performance of centrifugal pumps based on artificial neural network, Drainage and Irrigation Machinery, 26, 3, pp. 15-18, (2008)
  • [9] Panda S.S., Chakraborty D., Pal S.K., Flank wear prediction in drilling using back propagation neural network and radial basis function network, Applied Soft Computing, 8, 2, pp. 858-871, (2008)
  • [10] Yuan S., Shen Y., Zhang J., Et al., Performance predicting of centrifugal pumps with compound impeller based on improved BP neural network, Transactions of the Chinese Society for Agricultural Machinery, 40, 9, (2009)
1