ACTA AERONAUTICAET ASTRONAUTICA SINICA >
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)
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.
LU Feng , HUANG Jinquan , LV Yiqiu , QIU Xiaojie . Aircraft Engine Gas-path Components Health Diagnosis Based on Nonlinear Adaptive Filters[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013 , 34(11) : 2529 -2538 . DOI: 10.7527/S1000-6893.2013.0225
[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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