In order to realize compressor blade fouling and surge faults early warning, a typical fault warning method of gas turbine compressor combining thermodynamic model with artificial neural network was proposed. The simulation model of gas turbine thermodynamic performance was built according to the modularization idea, and the dynamic calibration of the model was completed by using the actual operation data of the gas turbine to form a high-precision gas turbine performance analysis model, and the key indicators such as exhaust flow rate, turbine front temperature and heat consumption can be calculated. Based on the thermal performance simulation model and combined with the compressor typical faults expert experience and professional knowledge, the main characteristic parameters affecting compressor faults were determined, and the compressor blade fouling and surge warning models were abstracted. The historical health data were selected to train the models using the artificial neural network algorithm to obtain the deviation curve, and the early warning of typical compressor faults can be realized by monitoring the deviation changes between the predicted value and the measured value of the early warning model, the example to verify the validity of the measured data of a GE 9F gas turbine compressor was given. The results showed that the method can accurately capture the compressor blade fouling and surge faults, and improve the warning time window compared with the traditional threshold alarm method. The research achievement can be directly deployed in the gas turbine power plant and provide real-time guidance for operation and maintenance personnel to make overhaul and maintenance decisions