Data Driven based Modeling and Fault Detection for the MATLAB/Simulink Turbofan Engine: An ARX Model Approach

Peng, Chao-Chung; Cheng, Yi-Ho

doi:10.1109/ccta49430.2022.9966098

Cited by 3 publications

References 8 publications

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Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

Salighe,

Söffker

2024

Proc Appl Math and Mech

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This paper discusses a strategy for optimizing the complexity of time‐varying data models as used in model‐free adaptive control (MFAC). Here the dynamic linearization in compact form (CFDL), partial form (PFDL), and full form (FFDL) are considered as data models used to describe input/output (I/O) data sets. These data models are built only for control purpose and can have various degrees of complexity depending on the size of the considered time‐window as well as the underlying algorithms. The methodology is to compare the performance of the data models according to an evaluation criterion, to analyze the order of different data models based on bias‐variance trade‐off, and to select the best‐performing model. The complexity of the selected model is compared with the reduced linear time‐invariant (LTI) model obtained by applying a combination of the eigensystem realization algorithm (ERA) and observer/Kalman filter identification (OKID) on the same I/O dataset. The I/O data are obtained from applying the MFAC controllers on a nonlinear three‐tank system (3TS) to track various desired references. The results indicate that the model complexity optimization can also be used for selecting an appropriate MFAC data model with optimal order to reduce the complexity and computational burden of the MFAC control algorithms.

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Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

Salighe,

Söffker

2024

Proc Appl Math and Mech

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Modeling of a Motor-driven Propeller Dynamics System by Neural Ordinary Differential Equation

Peng,

Chen

2023

2023 Sixth International Symposium on Computer, Consumer and Control (IS3C)

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Moving Horizon Fault Estimation of a Hybrid System using the Switched ARX-Laguerre Model

Bengharat,

Mbarek

2024

Wseas Transactions on Electronics

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Using moving horizon Fault estimation, we propose in this article a fault estimation of a linear hybrid system using a reduced complexity SARX-Laguerre model. The linear hybrid systems are approximated by a SARX-Laguerre model (Switched ARX-Laguerre).This latter is obtained by expanding a discrete time SARX model parameters on Laguerre orthonormal bases. The resulting model ensures an efficient complexity reduction with respect to the classical SARX model. This parametric complexity reduction is still subject to an optimal choice of the Laguerre poles defining Laguerre bases. Therefore, we propose in this paper to identify the parameters of the SARX-Laguerre model by using a recursive algorithm to identify the Fourier coefficients and a metaheuristic algorithm to identify the poles. The proposed model is built from the system input / output observations and is used to develop a fault estimation scheme. The scheme of the fault estimation based on the Moving Horizon fault Estimation (MHE). The proposed fault estimation using moving horizon procedure is tested on numerical simulation.

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Data Driven based Modeling and Fault Detection for the MATLAB/Simulink Turbofan Engine: An ARX Model Approach

Cited by 3 publications

References 8 publications

Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

Model complexity optimization of equivalent dynamical linearization data models used in model‐free adaptive control

Modeling of a Motor-driven Propeller Dynamics System by Neural Ordinary Differential Equation

Moving Horizon Fault Estimation of a Hybrid System using the Switched ARX-Laguerre Model

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