JSEA  Vol.6 No.3 B , March 2013
The Research on the Methods of Diagnosing the Steam Turbine Based on the Elman Neural Network
Author(s) Junru Gao*, Yuqing Wang
This paper introduces a kind of diagnosis principle and learning algorithm of steam turbine fault diagnosis which based on Elman neural network. Comparing the results of the Elman neural network and the traditional BP neural network diagnosis, the results shows that Elman neural network is an effective way to improve the learning speed , effectively suppress the minimum defects that the traditional neural network easily trapped in, and shorten the autonomous learning time. All these proves that the Elman neural network is an effective way to diagnose the steam turbine.

Cite this paper
J. Gao and Y. Wang, "The Research on the Methods of Diagnosing the Steam Turbine Based on the Elman Neural Network," Journal of Software Engineering and Applications, Vol. 6 No. 3, 2013, pp. 87-90. doi: 10.4236/jsea.2013.63B019.
[1]   Sichuan Province Electric Power Industry Bureau, Sichuan Province Electric Power Education Association. Vibration of steam turbine generator units[M]. Beijing: China Electric Power Press, 1998.

[2]   Jianhua Zhang, Guolian Hou, Xiaogang Sun, Guili Yuan, Using a probabilistic neural network fault diagnosis method [J], Power engineering, 2005, 25 (5):698~701

[3]   Zhihong Yao. Kohonen Network in the steam turbine vibration fault diagnosis using [J]. Turbine technology, 2004, 46 (1):67~68

[4]   Heji Yu, Changzheng Chen, etc. Intelligent diagnosis based on neural network [M]. Beijing : Metallurgical Industry Press, 2000

[5]   Akbari A A, et al. Induction motor identification using Elman neural network[J].WSEAS Transactions on Systems,2008,5:766~770

[6]   WangL, ShiX, LiangY.An improved Elman neural network with profit factors and its applications[A]. International Con-ferenceon Intelligent Computing, CIC2006[C], Aug16-19 2006, Springer Verlag, Heidelberg, D-69121, Germany, Kun-ming, China, 2007, 317~322

[7]   Huang Jinying, Pan Hongxia.The Research on Condition Detection and Fault Characteristic in one Vehicle Wheel Box. Proceedings of the 5th International Symposium on Test and Measurement,2009.