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

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

第二炮兵工程大学动力工程系, 西安 710025

计量
- 文章访问数: 1501
- HTML浏览量: 2
- PDF量: 941
- 被引次数: 0
出版历程
- 收稿日期: 2013-10-10
- 刊出日期: 2014-02-28

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

Department of Power Engineering, the Second Artillery Engineering University, Xi'an 710025, China

摘要

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.
- 涡扇发动机 /
- 高斯过程回归 /
- 无迹卡尔曼滤波 /
- 超球体单形采样 /
- 健康参数
Abstract: An improved spherical simplex square root unscented Kalman filter (ISRUKF) algorithm based on Gaussian process regression (GPR) was investigated to specifically address unknown or inaccurate models problems, resulting in bad or even divergent filtering results in turbofan engine performance monitoring. The transition and measurement GPR models of turbofan engine dynamic systems were established, and used as a substitute for nonlinear model in unscented Kalman filter (UKF). Meantime, the spherical simplex sampling was exploited to decrease the computational complexity, while square root of measurement residuals covariance matrixes was used instead of variance matrixes during recursive arithmetic process to improve the calculation efficiency and numerical stability in ISRUKF algorithm. Using the GPR, the models were firstly trained and verified, thus overcoming dependency on the previous engine models with the abilities of adaptive noise adjustment and high precision. The gradual and rapid deterioration process of different turbofan engine gas path components were then testified and compared by the extended Kalman filter (EKF), SRUKF and ISRUKF. The results show that the precision of ISRUKF has reached 99.9%, demonstrating the effectiveness of the ISRUKF for health parameters monitoring of turbofan engine gas path.
- turbofan engine /
- Gaussian process regression (GPR) /
- unscented Kalman filter (UKF) /
- spherical simplex sampling /
- health parameter

HTML

参考文献(21)

[1]	Rajamani R, Wang J, Jeong K Y.Conditioned based maintenance for aircraft engine[J].ASME Paper GT2004-54127, 2004.
[2]	Luppold R H, Roman J R, Gallops G W, et al.Estimating in flight engine performance variations using Kalman filter concepts[R].AIAA 1989-2584, 1989.
[3]	袁春飞, 姚华.基于卡尔曼滤波器和遗传算法的航空发动机性能诊断[J].推进技术, 2007, 28(1):9-13. YUAN Chunfei, YAO Hua.Development of kalman filter and genetic algorithm for aero-engine performance diagnostics[J].Journal of Propulsion Technology, 2007, 28(1):9-13.(in Chinese)
[4]	Dewallef P, Léonard O.On-line performance monitoring and engine diagnostic using robust Kalman filtering techniques.ASME Paper GT2003-38379, 2003.
[5]	张海波, 陈霆昊, 孙健国, 等.一种新的航空发动机自适应模型设计与仿真[J].推进技术, 2011, 32(4):557-563. ZHANG Haibo, CHEN Tinghao, SUN Jianguo, et al.Design and simulation of a new novel engine adaptive model[J].Journal of Propulsion Technology, 2011, 32(4):557-563.(in Chinese)
[6]	Kobayashi T.Application of a constant gain extended Kalman filter for in-flight estimation of aircraft engine performance parameters[R].NASA/TM 2005-213865, 2005.
[7]	张鹏, 黄金泉.航空发动机气路故障诊断的平方根UKF方法研究[J].航空动力学报, 2008, 23(1):169-173. ZHANG Peng, HUANG Jinquan.SRUKF research on aeroengines for gas path component fault diagnostics[J].Journal of Aerospace Power, 2008, 23(1):169-173.(in Chinese)
[8]	张鹏, 黄金泉.基于双重卡尔曼滤波器的发动机故障诊断[J].航空动力学报, 2008, 23(5):952-956. ZHANG Peng, HUANG Jinquan.Aeroengine fault diagnosis using dual Kalman filtering technique[J].Journal of Aerospace Power, 2008, 23(5):952-956.(in Chinese)
[9]	郑铁军, 王曦, 罗秀芹, 等.建立航空发动机状态空间模型的修正方法[J].推进技术, 2005, 26(1):46-49. ZHENG Tiejun, WANG Xi, LUO Xiuqin, et al.Modified method of establishing the state space model of aeroengine[J].Journal of Propulsion Technology, 2005, 26 (1):46-49.(in Chinese)
[10]	刘小勇, 樊思齐.自适应卡尔曼滤波在航空发动机参数估计中的应用[J].航空动力学报, 1995, 10(3):304-306. LIU Xiaoyong, FAN Siqi.Application of Kalman filtering for an aeroengine parameter estimation[J].Journal of Aerospace Power, 1995, 10(3):304-306.(in Chinese)
[11]	Williams C K I, Rasmussen C E.Gaussian processes for machine learning[M].Cambridge, USA:MIT Press, 2006.
[12]	Ferris B, Hahnel D, Fox D.Gaussian processes for signal strength-based location estimation[C]//Proceedings of Robotics:Science and Systems.Philadelphia, USA:MIT Press, 2006:782-794.
[13]	何志昆, 刘光斌, 赵曦晶, 等.基于GPR 模型的自适应平方根容积卡尔曼滤波算法[J].航空学报, 2013, 34(9):2202-2211. HE Zhikun, LIU Guangbin, ZHAO Xijing, et al.Adaptive square-root cubature Kalman filter algorithm based on Gaussian process regression models[J].Acta Aeronautica et Astronautica Sinica, 2013, 34(9):2202-2211.(in Chinese)
[14]	赵琳, 王小旭, 李亮, 等.非线性系统滤波理论[M].北京:国防工业出版社, 2012.
[15]	Julier S J, Uhlmann J K, Reduced sigma point filters for the propagation of means and covariance through nonlinear transformations[C]//Proceedings of the American Control Conference.Anchorage, USA:American Automatic Control Council, 2002:887-892.
[16]	卫志农, 孙国强, 庞博.无迹卡尔曼滤波及其平方根形式在电力系统动态状态估计中的应用[J].中国电机工程学报, 2011, 31(16):74-80. WEI Zhinong, SUN Guoqiang, PANG Bo.Application of UKF and SRUKF to power system dynamic state estimation[J].Proceedings of the CSEE, 2011, 31(16):74-80.(in Chinese)
[17]	李鹏, 宋申民, 陈兴林.自适应平方根无迹卡尔曼滤波算法[J].控制理论与应用, 2010, 27(2):143-146. LI Peng, SONG Shenming, CHEN Xinglin.Adaptive square-root unscented Kalman filter algorithm[J].Control Theory & Applications, 2010, 27(2):143-146.(in Chinese)
[18]	Borguet S, Léonard O.A generalized likelihood ratio test for adaptive gas turbine performance monitoring[J].Journal of Engineering for Gas Turbines and Power, 2009, 131(1):1-8.
[19]	胡宇, 杨月诚, 张世英, 等.基于改进拟合法的涡扇发动机状态变量模型建立方法[J].推进技术, 2013, 34(3):405-410. HU Yu, YANG Yuecheng, ZHANG Shiying, et al.Establishment of turbofan engine state variable model based on improved fitting method[J].Journal of Propulsion Technology, 2013, 34(3):405-410.(in Chinese)
[20]	Volponi A J, DePold H, Ganguli R, et al.The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics:a comparative study[J].Journal of Engineering for Gas Turbines and Power, 2003, 125(4):917-924.
[21]	Castrejón-Lozano J G, Carrillo L R G, Dzul A, et al.Spherical simplex sigma point Kalman filters:a comparison in the inertial navigation of a terrestrial vehicle[C]//Proceedings of the American Control Conference.Washington, USA:American Automatic Control Council, 2008:3356-3541.