State Prediction of Huge Gas Turbine Machine Based on Multi-Level Recursive Method and Experimental Study

Jin Ling Xu; Jie Ma; Xiao Li Xu; Shao Hong Wang

doi:10.4028/www.scientific.net/AMM.20-23.414

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 20-23State Prediction of Huge Gas Turbine Machine Based...

State Prediction of Huge Gas Turbine Machine Based on Multi-Level Recursive Method and Experimental Study

Abstract:

Gas turbine machine,Multi-level recursive,Prediction,Experimental study Abstract. Take the huge gas turbine set of Beijing Yanshan Petrochemical Refinery as the research object. The most important feature of machine is the vibration characteristic,and vibration displacement is the most important parameter of the machine,based on the historical vibration displacement datas,use multi-level recursive method to predict the state of the machine.With this method,fault prediction is divided into two parts:forecast of time-varying parameters and forecast based on the former forecast,the prediction precision is improved. Through the analysis of gas turbine failure mechanism,use Bently rotor bench,effectively simulate the mechanical failure of the rotor imbalance.

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Periodical:

Applied Mechanics and Materials (Volumes 20-23)

Pages:

414-420

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.20-23.414

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Online since:

January 2010

Authors:

Jin Ling Xu, Jie Ma, Xiao Li Xu, Shao Hong Wang

Keywords:

Experimental Study, Gas Turbine Machine, Multi-Level Recursive, Prediction

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References

[1] Xu Xiaoli，Xu Yong，Wang Xinyi. Research for trend indicating technology of machine working state [J] , INDUSTRIAL INSTRUMENTATION & AUTOMATION, 1998, (3): 9~12.

Google Scholar

[2] G.K. Singh and Sad Ahmed Saleh. AI Kazzaz. Induction machine drive condition monitoring and diagnostic research-a survey. Electric Power Systems Research, 2003, 64(2): 145~158.

DOI: 10.1016/s0378-7796(02)00172-4

Google Scholar

[3] China Vibration Engineering Society. 2006 Vibration Engineering and Academic conferences Essays, Journal of Vibration and Shock，(2006).

Google Scholar

[4] Xu Yuxiu, Zhang Jian, Hou Rongtao. Mechanical system dynamic fractal characteristics and Fault diagnosis. Beijing: National Defense Industry Press, (2006).

Google Scholar

[5] Han Zhigang. The Progress of Theory and Application of Multi-level Recursive Method.

Google Scholar

[6] Norden E Huang. A New method for nonlinear and non-stationary time series analysis and its application for civil infrastructure health monitoring. NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA.

Google Scholar

[7] WANG Hongqiao. The Application of Multi-level Recursive Method Oil Field[J] Journal of Natural Science of Heilongjiang University. 1994, 11(1): 39-45.

Google Scholar