Abstract
As the core power for the aviation industry, shipbuilding industry, and power station industry, it is essential to ensure that the gas turbines operate safely, reliably, greenly and efficiently. Learn from the advantages and disadvantages of the thermodynamic model based and data-driven artificial intelligence based gas-path diagnosis methods, a newfangled gas turbine gas-path diagnosis approach on the basis of knowledge data-driven artificial intelligence is proposed. That is a hybrid method of deep learning and gas path analysis. First, gas turbine thermodynamic model of the object to be diagnosed is constructed by adaptation modeling strategy. And the engine thermodynamic model is taken as the basal model to simulate various gas path faults. Secondly, a large number of knowledge data corresponding to component health parameters and gas turbine boundary condition parameters & gas-path measurable parameters are simulated by setting different component health parameter values and different boundary conditions based on this basal model. And next, define the vector composed of the boundary condition parameters & the gas path measurable parameters in the knowledge database as the input vector, and the component health parameter vector as the output vector, and a deep learning model for regression modeling of this knowledge database is designed. At last, along with the gas turbine engine runs, the trained model outputs component health parameters in real time after trained deep learning model is deployed to the corresponding gas turbine power plant. The simulation experiment results show that, accurate and quantified health parameters of each gas path component can be obtained by the proposed method in this paper, and the overall root mean square error does not exceed 0.033%, and the maximum relative error does not exceed 0.36%, which illustrates the proposed method has great application potential.
Highlights
A gas turbine is an internal combustion engine that uses a continuous flow of gas as a working medium to drive the impeller to rotate at high speed and convert the chemical energy of the fuel into useful work
The simulation experiment results show that, accurate and quantified health parameters of each gas path component can be obtained by the proposed method in this paper, and the overall root mean square error does not exceed 0.033%, and the maximum relative error does not exceed 0.36%, which illustrates the proposed method has great application potential
Combined with the advantages and disadvantages of the gas path fault diagnosis method based on thermodynamic model and data driven, this paper proposes a gas turbine gas path fault diagnosis method based on knowledge data driven
Summary
A gas turbine is an internal combustion engine that uses a continuous flow of gas as a working medium to drive the impeller to rotate at high speed and convert the chemical energy of the fuel into useful work. In the period of the gas turbine operating, the temperature, pressure, speed, etc., inside the engine will remain at a certain high intensity level, which is likely to cause damage to the gas turbine. In addition to these harsh working conditions, it may be affected by the surrounding polluted environmental. Regarding the shutdown and maintenance of the gas turbine engine, whether it is planned or unplanned, it always means that a large amount of money will be used for operation and maintenance costs. Gas path analysis (GPA) is a technology that can issue early warning information to a worsening situation that is developing or about to occur [2,3]
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