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基于改进平方根无迹卡尔曼滤波方法的涡扇发动机气路状态监控

胡宇 杨月诚 张世英 孙振生 朱杰堂

胡宇, 杨月诚, 张世英, 孙振生, 朱杰堂. 基于改进平方根无迹卡尔曼滤波方法的涡扇发动机气路状态监控[J]. 航空动力学报, 2014, 29(2): 441-450. doi: 10.13224/j.cnki.jasp.2014.02.025
引用本文: 胡宇, 杨月诚, 张世英, 孙振生, 朱杰堂. 基于改进平方根无迹卡尔曼滤波方法的涡扇发动机气路状态监控[J]. 航空动力学报, 2014, 29(2): 441-450. doi: 10.13224/j.cnki.jasp.2014.02.025
HU Yu, YANG Yue-cheng, ZHANG Shi-ying, SUN Zhen-sheng, ZHU Jie-tang. Turbofan engine gas path performance monitoring based on improved square root unscented Kalman filter[J]. Journal of Aerospace Power, 2014, 29(2): 441-450. doi: 10.13224/j.cnki.jasp.2014.02.025
Citation: HU Yu, YANG Yue-cheng, ZHANG Shi-ying, SUN Zhen-sheng, ZHU Jie-tang. Turbofan engine gas path performance monitoring based on improved square root unscented Kalman filter[J]. Journal of Aerospace Power, 2014, 29(2): 441-450. doi: 10.13224/j.cnki.jasp.2014.02.025

基于改进平方根无迹卡尔曼滤波方法的涡扇发动机气路状态监控

doi: 10.13224/j.cnki.jasp.2014.02.025

Turbofan engine gas path performance monitoring based on improved square root unscented Kalman filter

  • 摘要: 针对涡扇发动机气路状态监控存在模型未知或不准确导致滤波效果下降甚至发散的问题,研究了一种融入高斯过程回归(GPR)的改进平方根无迹卡尔曼滤波(UKF)方法.该方法利用GPR对训练数据进行学习,建立发动机气路部件状态监控的GPR模型,替代UKF方法中的非线性系统模型;采用超球体单形采样和平方根滤波方法来提高滤波的计算效率和数值稳定性.仿真结果表明:训练的GPR模型解决了UKF方法对发动机原系统模型和噪声协方差矩阵依赖性的问题;与扩展卡尔曼滤波(EKF)和平方根UKF方法相比较,改进平方根UKF方法精度更高,对健康参数的估计精度达到99.9%,实现了对涡扇发动机单个和多个气路部件健系参数的有效跟踪.

     

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出版历程
  • 收稿日期:  2013-10-10
  • 刊出日期:  2014-02-28

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