To address the problem of difficulty in effective fault diagnosis for civil aviation engines due to insufficient numbers of sensors, an Adaptive Gas Path Analysis Model incorporated with Prior Information (AGPAM-PI) is proposed to enhance the accuracyand efficiency of engine fault diagnosis. The AGPAM-PI combines the prior information of the engine fingerprintand the nonlinear gas path fault diagnosis method. The fault information is firstly supplemented through the fingerprint diagram, the solution range of fault factors is constrained, and then the nonlinear gas path analysis model is used for fault diagnosis. The model is validated using the data from a CFM56-7B engine that experienced a fault during actual operation. The results show that the model accurately locates the fault of the engineand analyzes the engine fault mode through engine fault diagnosis rules, which proves the validity of the model. Compared with traditional gas path fault diagnosis methods, the introduction of prior fault information improves the ability to distinguish unit faultsand enhances diagnostic accuracy.

In order to provide a theoretical basis for the gas path performance degradation of civil aviation engines at the module level, the CFM56-3 engine was taken as the research object. Firstly, the characteristic map scaling method was used to obtain the component characteristic equations, and the selection process of the general fan characteristic map scaling reference point was optimized. A characteristic map surface fitting method was proposed to construct an engine component-level benchmark performance model that conformed to specific speed conditions under steady-state operating mode. Then, by introducing fault factors to generate a fault coefficient matrix, the deviation of engine monitoring parameters with the decrease in component efficiency was calculated and compared with the fingerprint diagram data in the General Electric Company training manual. The results show that the engine steady-state performance model integrating fan characteristic map scaling reference point optimization and characteristic map surface fitting methods has good accuracy and application prospects in gas performance degradation analysis.