基于卡尔曼滤波器及神经网络的发动机故障诊断

姚华; 单贵平; 孙健国

基于卡尔曼滤波器及神经网络的发动机故障诊断

1.
南京航空航天大学能源与动力学院, 南京 210016
2.
中国航空工业第一集团公司航空动力控制系统研究所, 无锡 214063

基金项目: 航空科学基金(04C52019)

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- 被引次数: 0
出版历程
- 收稿日期: 2007-05-30
- 修回日期: 2007-08-20
- 刊出日期: 2008-06-28

Fault diagnosis for gas turbine engines based on Kalman filter and neural networks

1.
College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2.
Aviation Motor Control System Research Institute, China Aviation Industry Corporation Ⅰ, Wuxi 214063, China

摘要

摘要: 提出了一种基于卡尔曼滤波器及神经网络的航空燃气涡轮发动机气路故障诊断的方法.该方法用卡尔曼滤波器来估计发动机可测参数的变化量,再由神经网络来映射发动机性能参数的变化量,并据此进行发动机气路故障诊断.数字仿真表明,该方法是可行的,有效的.
- 航空、航天推进系统 /
- 航空发动机 /
- 故障诊断 /
- 卡尔曼滤波器 /
- 神经网络
Abstract: A method for gas path fault diagnosis of gas turbine engines based on Kalman filter and neural networks was proposed.For the fault diagnosis,the Kalman filter was used to estimate the variations of measurable parameters,while neural network was applied for mapping the variations of performance parameters of gas turbine engines.Digital simulations show that the proposed method is feasible and effective.
- aerospace propulsion system /
- gas turbine engine /
- fault diagnosis /
- Kalman filter /
- neural networks

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参考文献(8)

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