A Novel Performance Adaptation and Diagnostic Method for Aero-Engines Based on the Aerothermodynamic Inverse Model

Aerospace

Zhongguang Chen¹  Sangwei Lu²  Jinquan Huang²  Wenxiang Zhou²  Feng Lu² 

[1] AECC Shenyang Engine Research Institute, Shenyang 110015, China;Jiangsu Province Key Laboratory of Aerospace Power System, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;

关键词: turbofan engine;    aerothermodynamic inverse model;    model adaptation;    performance diagnosis;    estimation compensation;    nonlinear filter;   

DOI  :  10.3390/aerospace9010016

来源: DOAJ

【 摘 要 】

Aero-engines are faced with severe challenges of availability and reliability in the increasing operation, and traditional gas path filtering diagnostic methods have limitations restricted by various factors such as strong nonlinearity of the system and lack of critical sensor information. A method based on the aerothermodynamic inverse model (AIM) is proposed to improve the adaptation accuracy and fault diagnostic dynamic estimation response speed in this paper. Thermodynamic mechanisms are utilized to develop AIM, and scaling factors are designed to be calculated iteratively in the presence of measurement correction. In addition, the proposed method is implemented in combination with compensation of the nonlinear filter for real-time estimation of health parameters under the hypothesis of estimated dimensionality reduction. Simulations involved experimental datasets revealed that the maximum average simulated error decreased from 13.73% to 0.46% through adaptation. It was also shown that the dynamic estimated convergence time of the improved diagnostic method reached 2.183 s decrease averagely without divergence compared to the traditional diagnostic method. This paper demonstrates the proposed method has the capacity to generalize aero-engine adaptation approaches and to achieve unbiased estimation with fast convergence in performance diagnostic techniques.

【 授权许可】

Unknown