Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine

Chang, Xiaodong; Huang, Jinquan; Lü, Feng

doi:10.3390/en10071040

Cited by 13 publications

(13 citation statements)

References 31 publications

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“…A. Volponi et al applied the Kalman filter [19], and this approach is followed and developed in many papers [20][21][22][23][24], whose authors improved stability of the algorithm and its applicability to a non-linear engine model. X. Chang et al applied an alternative method based on the non-linear filtration (sliding mode observer) [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

et al. 2020

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Zero-dimensional models based on the description of the thermo-gas-dynamic process are widely used in the design of engines and their control and diagnostic systems. The models are subjected to an identification procedure to bring their outputs as close as possible to experimental data and assess engine health. This paper aims to improve the stability of engine model identification when the number of measured parameters is small, and their measurement error is not negligible. The proposed method for the estimation of engine components’ parameters, based on multi-criteria identification, provides stable estimations and their confidence intervals within known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularized identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Synthesis of objective functions and subsequent scalar convolutions of these functions are used to estimate gas-path components’ parameters. A comparison with traditional methods showed that the main advantage of the proposed approach is the high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions that do not correspond to a priori assumptions and also helps to implement the gas path analysis with a limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, gas path analysis and adapting dynamic models to the needs of the engine control system.

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Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

et al. 2020

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“…A generic FDI development in terms of reconstruction of faults using sliding mode observers is given by Tan and Edwards [10], and they extended the work in [8] for robust reconstruction of sensor and actuator faults by minimizing the effect of uncertainty on the reconstruction in an L2 sense. Our previous work in [11,12] exploited the application of SMO in robust health estimation and sensor fault diagnosis for aircraft engines. The work in [13] described a sensor FTC scheme based on the SMO in [10].…”

Section: Introductionmentioning

confidence: 99%

Sensor Fault Tolerant Control for Aircraft Engines Using Sliding Mode Observer

2019

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This paper investigated the problem of fault estimation and fault-tolerant control (FTC) against sensor faults for aircraft engines. By applying a second order sliding mode observer (SOSMO) to the engine on-board model, estimations of the system states and sensor faults could be obtained simultaneously, and the result of state estimation was unaffected when using the reduced-order sliding mode system. This result gave rise to the idea to use the estimated states instead of physical sensor signal in the engine close-loop feedback control. Unlike those using passive FTC concepts, the tradeoff between control performance and robustness was inherently unnecessary. Meanwhile, compared to active FTC approaches, because any classical state/output feedback method can be directly applied to the proposed scheme without any controller reconfiguration, extra undesired dynamic responses caused by parameter reconfiguring were avoided. In this paper, the proposed FTC scheme was tested on the nonlinear model of a civil aircraft turbofan engine, and numerical simulation results showed satisfactory sensor FTC performance.

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“…A. Volponi et al applied the Kalman filter [17] and this approach is followed and developed in many papers, for example [18][19][20][21][22], which authors improved stability of the algorithm and its applicability to a non-linear engine model. In publications [23][24][25], X. Chang et. al.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

Khustochka¹,

Yepifanov²,

Zelenskyi³

et al. 2019

Preprint

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Gas Path Analysis and matching turbine engine models to experimental data are inverse problems of mathematical modelling which are characterized by parametric uncertainty. This results from the fact that the number of measured parameters is significantly lower than the number of components’ performance parameters needed to describe the real engine. In these conditions, even small measurement errors can result in a high variation of results, and obtained efficiency, loss factors etc. can appear out of the physical range. The current methods of engine model identification have developed considerably to provide stable, precise and physically adequate solutions. Presented in this work is an estimation method of engine components’ parameters based on multi-criteria identification which provides stable estimations of parameters and their confidence intervals with the known measurement errors. A priori information about the engine, its parameters and performance is used directly in the regularised identification procedure. The mathematical basis for this approach is the fuzzy sets theory. Forming objective functions and scalar convolutions synthesis of these functions is used to estimate gas-path components’ parameters. A comparison of the proposed approach with traditional methods showed that its main advantage is high stability of estimation in the parametric uncertainty conditions. Regularization reduces scattering, excludes incorrect solutions which do not correspond to a priori assumptions, and also helps to implement the Gas Path Analysis at the limited number of measured parameters. The method can be used for matching thermodynamic models to experimental data, Gas Path Analysis and also adapting dynamic models for the needs of the engine control system.

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Health Parameter Estimation with Second-Order Sliding Mode Observer for a Turbofan Engine

Cited by 13 publications

References 31 publications

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

Sensor Fault Tolerant Control for Aircraft Engines Using Sliding Mode Observer

Estimation of Performance Parameters of Turbine Engine Components Using Experimental Data in Parametric Uncertainty Conditions

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