@article{GUO2025, 
author = {Qing GUO and Xiaoyang LIU and Junfeng FAN and Yu FU and Hongfu ZUO},
title = {Adaptive gas path fault diagnosis method of civil aviation engine fusing prior information},
year = {2025},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {46},
number = {4},
keywords = {civil aviation engine, fault diagnosis, component-level modeling, information fusion, nonlinear gas path analysis},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2024.30871},
doi = {10.7527/S1000-6893.2024.30871},
abstract = {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.}
}