Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method

• DOI: 10.1016/j.cja.2021.11.005
• Publication Date: 2021-11-18T08:07:58Z
• Authors: Yanhua MA; Xian DU; Ximing SUN
• Keywords: 0203 mechanical engineering; 02 engineering and technology

Abstract An accurate and reliable turbofan engine model which can describe its dynamic behavior within the full flight envelop and lifecycle plays a critical role in performance optimization, controller design and fault diagnosis. However, due to the performance differences caused by the tolerance of engine manufacturing and assembly, and performance degradation during continuously stringent environmental regulations, the model accuracy is severely reduced. In this paper, an adaptive modification method of turbofan engine nonlinear component-level model (CLM) based on long short-term memory neural network (LSTM NN) and hybrid optimization algorithm is pro-posed. First, a dynamic compensator with a combined LSTM NN architecture is constructed to compensate for the initial error between the experimental data and CLM of a turbofan engine on health condition. Then, a sensitivity analysis approach based on based on the entropy coefficient and technique for order preference by similarity to an ideal solution integrated evaluation is developed to choose the unmeasurable health parameters to be adjusted. Finally, a parallel hybrid optimization algorithm is developed to complete the adaptive model modification when the performance degrades. The proposed method is verified on a military low-bypass twin-spool turbofan engine, and experimental results show the effectiveness of the proposed method.