摘要
针对发动机气路突变故障诊断精度不高以及算法工程化验证周期长的问题,提出了线性自适应卡尔曼滤波算法,且将其扩展至非线性系统,并在快速原型试验平台上实现算法快速验证。在非线性滤波算法的状态方程中引入状态突变因子,采用统计意义的广义似然比检验方法,通过测量残差对气路部件健康参数的突变与否进行检验,解决了发动机气路健康参数突变的准确估计,搭建基于NI CRIO的航空发动机气路性能分析快速原型试验平台,实现了非线性自适应滤波算法在快速原型验证平台的部署及快速验证。以某型大涵道比涡扇发动机为对象,通过数字仿真与快速原型平台验证了非线性自适应滤波算法相比于常规扩展卡尔曼滤波(EKF)具有更好的突变诊断能力,同时具有较高的渐变诊断能力。
To deal with the issue of poor accuracy of gas-path abrupt fault diagnosis and the long term required for algorithm validation, a detailed algorithm of linear adaptive Kalman filter is presented and extended to the nonlinear system, and then validated on a rapid prototyping platform. A tuning factor is introduced to the state equation of the nonlinear filter, and a gen- eralized likelihood ratio test is used to detect and estimate an abrupt fault by monitoring the residuals. Gas-path abrupt faults can be diagnosed by the shift of the tuning factor in the nonlinear filter algorithm. Then the proposed algorithm is validated on the NI CRIO test of aircraft engine gas-path analysis, and it is realized by the rapid prototyping with arrangement and down- loads. Tests on a high bypass ratio turbofan engine through digital simulation and rapid prototyping platform show that the adaptive filter algorithm can obtain estimates of both abrupt and gradual deteriorations more accurately than the conventional extended Kalman filter （EKF） algorithm.
出处
《航空学报》
EI
CAS
CSCD
北大核心
2013年第11期2529-2538,共10页
Acta Aeronautica et Astronautica Sinica
基金
国家自然科学基金(51276087)
中国博士后科学基金(2013M530256)
江苏省自然科学基金(BK20130802)
江苏省博士后科学基金(201202063)
关键词
航空发动机
气路分析
扩展卡尔曼滤波
自适应滤波
快速原型
aircraft engine
gas-path analysis
extended Kalman filter （EKF）
adaptive filter
rapid prototyping
