Abstract: Aeroengine has complex structure and harsh working conditions, and are fault prone systems. In order to ensure the safe and reliable operation of the engine, an accurate engine model can be established for state monitoring and fault detection. In this paper, the K-means clustering algorithm is used to process the massive test flight data, and the data points with similar and repeated working status are eliminated to improve the capacity of training samples. Then, based on the BP neural network, the air path parameters of the turbofan engine are identified, and the trained model is promoted and applied in the data that has not participated in the training. The calculation results of the model are basically consistent with the flight test data. The fault detection algorithm is studied in the paper. Whether the residual error between the model output and the measured value exceeds the set threshold is used as the basis for fault detection. The application of the model in a section of fault data shows that the fault detection algorithm can detect the fault in advance compared with the engine fault alarm system.