基于自适应容积卡尔曼滤波方法的涡扇发动机气路部件故障诊断

胡宇; 张世英; 罗雷; 朱杰堂; 马鸿飞

doi:10.13224/j.cnki.jasp.2016.05.030

基于自适应容积卡尔曼滤波方法的涡扇发动机气路部件故障诊断

doi: 10.13224/j.cnki.jasp.2016.05.030

1. 火箭军工程大学动力工程系, 西安 710025;
2. 中国人民解放军 96361部队, 西宁 810000

详细信息

作者简介:
胡宇(1984-),男,湖南湘阴人,讲师,博士,研究领域为涡扇发动机建模与故障诊断.

中图分类号: V233.7
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出版历程
- 收稿日期: 2014-08-21
- 刊出日期: 2016-05-28

Turbofan engine gas path components fault diagnosis based on adaptive cubature Kalman filter

1. Department of Power Engineering, Rocket Force University of Engineering, Xi'an 710025, China;
2. Troops No. 96361 of the Chinese People's Liberation Arrmy, Xining 810000, China

摘要

摘要: 针对涡扇发动机气路部件故障诊断中参数存在不同的噪声统计特性,提出了一种自适应平方根容积卡尔曼滤波(ASRCKF)器的自适应滤波方法.该方法直接利用基于3阶容积积分方法近似发动机的非线性统计特性,用于替代非线性无迹卡尔曼滤波方法的系统模型,避免了滤波过程参数选取的问题;采用移动窗口法对噪声协方差矩阵进行自适应估计,提高了算法对不同统计特性噪声的自适应能力和滤波精度.通过对发动机气路部件健康参数蜕化过程仿真结果表明:ASRCKF方法相比平方根容积卡尔曼滤波(SRCKF)方法,精度提高40%~50%,对不同噪声信号具有更好的适应能力.
- 涡扇发动机 /
- 容积卡尔曼滤波 /
- 移动窗口法 /
- 参数估计 /
- 故障诊断
Abstract: In order to realize better filtering accuracy and adaptability given different statistic characteristics of noises, an adaptive square root cubature Kalman filter (ASRCKF) was proposed for the turbofan engine gas path components fault diagnosis. In the ASRCKF algorithm, the mean and covariance of the engine nonlinear function were calculated by three-order cubature rule-based numerical integration method and used as a substitute of nonlinear model to avoid the parameters selection problem of nonlinear unscented Kalman filter. The latest information of measurement parameters in the recursion and filtering process was used to estimate and self-adjust the noise cross-covariance by removable window method, so as to realize higher filtering accuracy and adaptability of different noises. The comparison of simulation results indicates that the accuracy of the ASRCKF is improved by 40%-50% than the SRCKF, showing a better adaptability to different noises than other algorithms.
- turbofan engine /
- cubature Kalman filter(CKF) /
- removable window method /
- parameter estimation /
- fault diagnosis

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