基于非线性自适应滤波的发动机气路部件健康诊断方法
收稿日期: 2013-01-16
修回日期: 2013-04-26
网络出版日期: 2013-06-09
基金资助
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）
Aircraft Engine Gas-path Components Health Diagnosis Based on Nonlinear Adaptive Filters
Received date: 2013-01-16
Revised date: 2013-04-26
Online published: 2013-06-09
Supported by
National Natural Science Foundation of China (51276087);China Postdoctoral Science Foundation (2013M530256);Natural Science Foundation of Jiangsu Province (BK20130802);Postdoctoral Science Foundation of Jiangsu Province (201202063)
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。
鲁峰 , 黄金泉 , 吕怡秋 , 仇小杰 . 基于非线性自适应滤波的发动机气路部件健康诊断方法[J]. 航空学报, 2013 , 34(11) : 2529 -2538 . DOI: 10.7527/S1000-6893.2013.0225
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 generalized 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 downloads. 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.
[1] Garg S.Controls and health management technologies for intelligent aerospace propulsion systems.NASA/TM-2004-212915,2004.
[2] Hans R D,Jason S.Using diagnostics and prognostics to minimize the cost of ownership of gas turbine.ASME Paper,GT-2006-91183,2006.
[3] James A T,Litt J S.A foreign object damage event detector data fusion system for turbofan engines.NASA/TM-2004-213192,2004.
[4] Hao Y,Sun J G,Bai J.State-of-the-art and prospect of aircraft engine fault diagnosis using gas path parameters.Journal of Aerospace Power,2003,18(6): 753-760.(in Chinese) 郝英,孙健国,白杰.航空燃气涡轮发动机气路故障诊断现状与展望.航空动力学报,2003,18(6): 753-760.
[5] Luppold R H,Roman J R,Gallops G W,et al.Estimating in-flight engine performance variations using Kalman filter concepts.AIAA-1989-2584,1989.
[6] Volponi A.Enhanced self tuning on-board real-time model (eSTORM) for aircraft engine performance health tracking.NASA/CR-2008-215272,2008.
[7] Simon D,Simon D L.Constrained Kalman filtering via density function truncation for turbofan engine health estimation.NASA/TM-2006-214129,2006.
[8] Ogaji S O T,Marinai S,Sampath S,et al.Gas-turbine fault diagnostics: a fuzzy-logic approach.Applied Energy,2005,82(1): 81-89.
[9] Volponi A,DePold H,Ganguli R,et al.The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study.Journal of Engineering for Gas Turbine and Power,2003,125(4): 917-924.
[10] Lu F,Huang J Q.Engine component performance prognostics based on decision fusion.Acta Aeronautica et Astronautica Sinica,2009,30(10): 1795-1800.(in Chinese) 鲁峰,黄金泉.航空发动机部件性能参数融合预测.航空学报,2009,30(10): 1795-1800.
[11] Kobayashi T,Simon D L.Application of a bank of Kalman filters for aircraft engine fault diagnostics.NASA/TM-2003-212526,2003.
[12] Simon D L,Armstrong J B,Garg S.Application of an optimal tuner selection approach for on-board self-tuning engine models.NASA/TM-2012-217278,2012.
[13] Simon D.A comparison of filtering approaches for aircraft engine health estimation.Aerospace Science and Technology,2008,12(4): 276-284.
[14] Kobayashi T,Simon D L,Litt J S.Application of a constant gain extended Kalman filter for in-flight estimation of aircraft engine performance parameters.NASA/TM-2005-213865,2005.
[15] Borguet S,Léonard O.Comparison of adaptive filters for gas turbine performance monitoring.Journal of Computational and Applied Mathematics,2010,234(7): 2202-2212.
[16] Borguet S,Léonard O.A generalized likelihood ratio test for adaptive gas turbine performance monitoring.ASME Journal of Engineering for Gas Turbines and Power,2009,131(1): 011601-1: 9.
[17] Tang L,Decastro J A,Zhang X,et al.A unified nonlinear adaptive approach for detection and isolation of engine faults.NASA/TM-2010-216360,2010.
[18] Naderi E,Meskin N,Khorasani K.Nonlinear fault diagnosis of jet engines by using a multiple model-based approach.Journal of Engineering for Gas Turbines and Power,2012,134(1): 1-10.
[19] Zhang C Y.Approach to adaptive filtering algorithm.Acta Aeronautica et Astronautica Sinica,1988,19(7S): 96-99.(in Chinese) 张常云.自适应滤波方法研究.航空学报,1988,19(7S): 96-99.
[20] Willsky A,Jones H.A generalized likelihood ratio approach to state estimation in linear systems subject to abrupt changes.1974 IEEE Conference on Decision and Control including the 13th Symposium on Adaptive Processes,1974: 846-853.
[21] Lu J,Guo Y Q,Wang H Q.Digital electronic engine control real-time simulation platform based on rapid prototyping.Computer Measurement & Control,2009,17(6): 1098-1101.(in Chinese) 陆军,郭迎清,王海泉.基于快速原型化的数控系统实时仿真平台研制.计算机测量与控制,2009,17(6): 1098-1101.
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