Journal of Propulsion Technology ›› 2020, Vol. 41 ›› Issue (10): 2358-2366.DOI: 10.13675/j.cnki.tjjs.190586

• Test, Experiment and Control • Previous Articles     Next Articles

Turbofan Engine Model Identification of Acceleration Process Based on Particle Swarm Optimization Kernel Extreme Learning Machine

  

  1. Aviation Foundation College,Naval Aeronautical University,Yantai 264001,China
  • Published:2021-08-15

基于粒子群核极限学习机的涡扇发动机加速过程模型辨识

赵姝帆,李本威,钱仁军,朱飞翔   

  1. 海军航空大学 航空基础学院,山东 烟台 264001
  • 基金资助:
    国家自然科学基金(51505492);山东省自然科学基金(ZR2016FQ19);泰山学者建设工程专项经费。

Abstract: In order to solve the problem of low accuracy and real-time in establishing the model of turbofan engine acceleration process by analytic method, a data-driven method on identifying the turbofan engine acceleration process model based on kernel extreme learning machine (KELM) optimized by particle swarm optimization (PSO) was proposed. Firstly, a turbofan engine acceleration process model was constructed. Then, PSO-KELM was adopted to identify the model using engine acceleration process test data. The results show that identification results of the low-pressure rotor speed, the high-pressure rotor speed and the low-pressure turbine outlet gas temperature are all close to measured data, the mean maximum relative errors are 1.013%, 0.355% and 1.055%, respectively, and the mean computing time is 0.04ms. The precision and real-time are better than back propagation (BP) neural network method and PSO-support vector regression (SVR). This method can be used for engine condition monitoring and performance optimization control.

Key words: Turbofan engine;Acceleration process;Kernel extreme learning machine;Data-driven;Model identification

摘要: 针对解析法建立涡扇发动机加速过程模型精度和实时性不高的问题,提出了一种基于粒子群核极值学习机(PSO-KELM)的涡扇发动机加速过程模型数据驱动辨识方法,构建涡扇发动机加速过程模型,结合加速过程试车数据,利用PSO-KELM方法对该加速模型进行辨识。试验结果表明:低压转子转速、高压转子转速和低压涡轮出口燃气总温都较好地逼近了试车数据,最大相对误差均值分别为1.013%,0.355%和1.055%,平均计算时间为0.04ms。精度和实时性均优于反向传播神经网络和粒子群支持向量回归方法,可用于发动机状态监控和性能优化控制。

关键词: 涡扇发动机;加速过程;核极限学习机;数据驱动;模型辨识