Flue Gas Turbine Condition Trend Prediction Based on Improved Echo State Network

Author

Shaohong, Wang ; Tao, Chen ; Xiaoli, Xu

Author_Institution

Sch. of Mech. Eng., Beijing Inst. of Technol., Beijing, China

Volume

2

fYear

2011

fDate

6-7 Jan. 2011

Firstpage

242

Lastpage

245

Abstract

Fault prediction is the key technology for ensuring safe operation and scientific maintenance of large equipment. As the running of flue gas turbine has nonlinear characteristics, echo state network (ESN) was introduced to predict the condition trend of the turbine. Singular value decomposition was used to improve the linear regression algorithm of ESN, and the prediction workflow was given. Condition trend prediction results showed the effectiveness of the proposed method.

Keywords

condition monitoring; fault location; flue gases; gas turbines; maintenance engineering; mechanical engineering computing; recurrent neural nets; regression analysis; singular value decomposition; condition trend prediction; fault prediction; flue gas turbine; improved echo state network; linear regression algorithm; nonlinear characteristics; operation safety; prediction workflow; scientific maintenance; singular value decomposition; Linear regression; Matrix decomposition; Nonlinear dynamical systems; Prediction algorithms; Singular value decomposition; Training; Turbines; condition trend prediction; echo state network; flue gas turbine;

fLanguage

English

Publisher

ieee

Conference_Titel

Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on

Conference_Location

Shangshai

Print_ISBN

978-1-4244-9010-3

Type

conf

DOI

10.1109/ICMTMA.2011.348

Filename

5721166