导航

Acta Aeronautica et Astronautica Sinica ›› 2025, Vol. 46 ›› Issue (4): 230871.doi: 10.7527/S1000-6893.2024.30871

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles    

Adaptive gas path fault diagnosis method of civil aviation engine fusing prior information

Qing GUO1(), Xiaoyang LIU1, Junfeng FAN2, Yu FU1, Hongfu ZUO3   

  1. 1.College of Aeronautical Engineering,Civil Aviation University of China,Tianjin 300300,China
    2.Power Plant Department,Southern Airlines Engineering Technology Branch,Guangzhou 510890,China
    3.College of Civil Aviation,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
  • Received:2024-06-26 Revised:2024-07-15 Accepted:2024-08-05 Online:2024-08-21 Published:2024-08-20
  • Contact: Qing GUO E-mail:qguocauc@sina.com
  • Supported by:
    National Natural Science Foundation of China(U2133202);Civil Aviation University of China Central University Basic Research Project(3122022046)

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 accuracy and efficiency of engine fault diagnosis. The AGPAM-PI combines the prior information of the engine fingerprint and 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 engine and 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 faults and enhances diagnostic accuracy.

Key words: civil aviation engine, fault diagnosis, component-level modeling, information fusion, nonlinear gas path analysis

CLC Number: