Considered here are nonlinear autoregressive neural networks (NETs) with exogenous inputs (NARX) as a mathematical model of a steam turbine rotor used for the online prediction of turbine temperature and stress. In this paper, the online prediction is presented on the basis of one critical location in a high-pressure (HP) steam turbine rotor. In order to obtain NETs that will correspond to the temperature and stress the critical rotor location, a finite element (FE) rotor model was built. NETs trained using the FE rotor model not only have FEM accuracy but also include all nonlinearities considered in an FE model. Simultaneous NETs are algorithms which can be implemented in turbine controllers. This allows for the application of the NETs to control steam turbine stress in industrial power plants.
Online Prediction of Temperature and Stress in Steam Turbine Components Using Neural Networks
Contributed by the Turbomachinery Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received August 5, 2015; final manuscript received September 6, 2015; published online November 11, 2015. Editor: David Wisler.
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Dominiczak, K., Rządkowski, R., Radulski, W., and Szczepanik, R. (November 11, 2015). "Online Prediction of Temperature and Stress in Steam Turbine Components Using Neural Networks." ASME. J. Eng. Gas Turbines Power. May 2016; 138(5): 052606. https://doi.org/10.1115/1.4031626
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