To avoid disrepair and over repair

and improve the reliability and availability of gas turbines

this paper proposes a gas turbine gas circuit fault diagnosis method based on model and data hybrid drive. Firstly

the gas turbine thermodynamic model of the object to be diagnosed is constructed based on adaptive thermal modeling strategy. By setting different component health parameters and inlet boundary conditions

a data set of one-to-one correspondence between component health parameter vectors and inlet boundary condition parameters and gas path measurable parameter vectors is obtained through simulation. Secondly

deep learning is used to carry out regression modeling

and train a gas turbine gas path fault diagnosis model. Finally

according to the actual gas turbine inlet boundary condition parameters and gas path measurable parameter vectors

the trained diagnostic model is used to diagnose and output the health parameter vectors of each gas path component in real time. The simulation experiment results show that

accurate and quantified health parameters of each gas path component can be obtained by the proposed method

the overall root mean square error does not exceed 0.033%

and the maximum relative error does not exceed 0.36%

indicating the proposed method has great application potential.