首页> 外国专利> AERO-ENGINE FULL FLIGHT ENVELOPE MODEL ADAPTIVE MODIFICATION METHOD BASED ON DEEP LEARNING ALGORITHM

AERO-ENGINE FULL FLIGHT ENVELOPE MODEL ADAPTIVE MODIFICATION METHOD BASED ON DEEP LEARNING ALGORITHM

机译:基于深度学习算法的航空发动机全飞行包络模型自适应修正方法

摘要

An aero-engine full flight envelope model adaptive modification method based on a deep learning algorithm. A dynamic parallel compensator based on a recursive neural network is adopted to compensate the error of the original nonlinear model within the full flight envelope under the condition without aero-engine performance deterioration. A modifier based on a genetic algorithm is also adopted to conduct adaptive adjustment on correction coefficients of health parameters to be modified in the original nonlinear component-level model. The health parameters to be modified are determined by a multi-attribute decision algorithm based on integrated evaluation. The sum of the modified nonlinear component-level model output and the compensator output is consistent with the aero-engine operation test output data. This provides powerful support for the design of aero-engine control systems and fault diagnosis systems.
机译:一种基于深度学习算法的航空发动机全飞行包络线模型自适应修正方法。在不降低航空发动机性能的条件下,采用基于递归神经网络的动态并行补偿器来补偿原始非线性模型在整个飞行范围内的误差。还采用基于遗传算法的修改器对原始非线性组件级模型中要修改的健康参数校正系数进行自适应调整。通过基于综合评估的多属性决策算法确定要修改的健康参数。修改后的非线性组件级模型输出和补偿器输出的总和与航空发动机运行测试输出数据一致。这为航空发动机控制系统和故障诊断系统的设计提供了有力的支持。

著录项

  • 公开/公告号US2020063665A1

    专利类型

  • 公开/公告日2020-02-27

    原文格式PDF

  • 申请/专利权人 DALIAN UNIVERSITY OF TECHNOLOGY;

    申请/专利号US201816462504

  • 发明设计人 YANHUA MA;XIAN DU;XIMING SUN;

    申请日2018-01-25

  • 分类号F02D28;G07C5;G06N3/08;

  • 国家 US

  • 入库时间 2022-08-21 11:21:12

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