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• W2737268889 startingPage "1040" @default.
• W2737268889 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." @default.
• W2737268889 created "2017-07-31" @default.
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• W2737268889 date "2017-07-20" @default.
• W2737268889 modified "2023-09-27" @default.
• W2737268889 title "Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine" @default.
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• W2737268889 doi "https://doi.org/10.3390/en10071040" @default.
• W2737268889 hasPublicationYear "2017" @default.
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