Abstract
In this paper the problem of health parameter estimation in an aero-engine is investigated by using an unknown input observer-based methodology, implemented by a second-order sliding mode observer (SOSMO). Unlike the conventional state estimator-based schemes, such as Kalman filters (KF) and sliding mode observers (SMO), the proposed scheme uses a “reconstruction signal” to estimate health parameters modeled as artificial inputs, and is not only applicable to long-time health degradation, but reacts much quicker in handling abrupt fault cases. In view of the inevitable uncertainties in engine dynamics and modeling, a weighting matrix is created to minimize such effect on estimation by using the linear matrix inequalities (LMI). A big step toward uncertainty modeling is taken compared with our previous SMO-based work, in that uncertainties are considered in a more practical form. Moreover, to avoid chattering in sliding modes, the super-twisting algorithm (STA) is employed in observer design. Various simulations are carried out, based on the comparisons between the KF-based scheme, the SMO-based scheme in our earlier research, and the proposed method. The results consistently demonstrate the capabilities and advantages of the proposed approach in health parameter estimation.
Highlights
IntroductionIn aircraft turbofan engine operations, reliability and efficiency are of utmost importance
In aircraft turbofan engine operations, reliability and efficiency are of utmost importance.Subjected to harsh environments, the gas-path performance of aero-engines gradually deteriorates over flights
The high switching chattering is attenuated via the 2-order sliding mode methodology
Summary
In aircraft turbofan engine operations, reliability and efficiency are of utmost importance. The goal of gas path health monitoring (GPHM) is to relate observed changes in measurements to internal changes in health parameters, to provide performance trend monitoring, which will be further used in engine fault diagnostics. Common model-based approaches to estimate health parameters are Weighted Least Squares [7], Generalized Observer [8] and Kalman filter [9,10]. The proposed scheme in [25] performs superior over the KF-based scheme with model mismatches considered, but some problems remain unsettled: one is the health parameters are still modeled without dynamics, like that in KF-based scheme; and another is the harsh restriction on allowed uncertainties, i.e., the uncertainty distribution matrix is forced to be in a certain form, which is hard to meet practically. An approach based on a second-order sliding mode observer is investigated for the estimation of health parameters in a civil aero-engine.
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