基于非线性自适应滤波的发动机气路部件健康诊断方法

doi:10.7527/S1000-6893.2013.0225

Abstract

Abstract:

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.

Key words: aircraft engine, gas-path analysis, extended Kalman filter (EKF), adaptive filter, rapid prototyping

CLC Number:

V233.7

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.

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Aircraft Engine Gas-path Components Health Diagnosis Based on Nonlinear Adaptive Filters

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