Determination of Model Structure from Flight Test with Generalized Additive Models

Monstein, Raphael; Capone, Pierluigi; Dettling, Marcel; Vrdoljak, Milan

doi:10.2514/1.c035171

Cited by 4 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, the authors of [24] compared a GAM with linear regression in order to predict aviation weather parameters. In [25], GAMs were explored because of their ability to provide information on the significance of each individual feature. The data employed in this study are flight test data, with the objective of having a physical interpretation of an aerodynamic model.…”

Section: Applications Of the Gam And The State Of The Artmentioning

confidence: 99%

AI-Based Exhaust Gas Temperature Prediction for Trustworthy Safety-Critical Applications

2022

View full text Add to dashboard Cite

Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.

show abstract

Section: Applications Of the Gam And The State Of The Artmentioning

confidence: 99%

AI-Based Exhaust Gas Temperature Prediction for Trustworthy Safety-Critical Applications

2022

View full text Add to dashboard Cite

show abstract

“…System identification in the time-domain can be implemented using several methods such as the equation error method (EEM) [3,8,9,[14][15][16][17], the output error method [8,9,14], the filter error method [8,18], and artificial neural networks [3,19,20]. Computational software tools such as FVSysID [8] and SIDPAC [9], running under MATLAB, are available for system identification [21].…”

Section: Introductionmentioning

confidence: 99%

Development of Aerodynamic and Propulsion Models Using the Iterative Equation Error Method

Millidere,

Akgül,

Leblebicioğlu

et al. 2023

Aerospace

View full text Add to dashboard Cite

For developing high-fidelity flight simulations, an accurate and complete representation of the aerodynamic characteristics of the aircraft is necessary. To obtain a realistic aerodynamic database, system identification methods can be used to describe the applied forces and moments acting on the aircraft. This study is based on simulated flight test data from a nonlinear simulation of the F-16 aircraft. It is demonstrated that the complete set of aerodynamic coefficients can be reconstructed from the flight test data. Thrust forces and moments are obtained from ground tests. A practical system identification methodology based on the iterative equation error method to determine the nonlinear aerodynamic and engine thrust models in the absence of engine manufacturer data is developed. The estimated values obtained using the method are compared with the actual parameter values. A mathematical engine model that can be used to estimate the thrust force for any flight condition is also developed. The findings demonstrate that the proposed method yields accurate results. The developed methodology is well-suited for the identification of isolated aerodynamic drag and lift coefficients and the thrust model.

show abstract

Development of High-Fidelity Saab 340B Aerodynamic Model

Millidere

Alam

Place

et al. 2023

AIAA SCITECH 2023 Forum

View full text Add to dashboard Cite

Determination of Model Structure from Flight Test with Generalized Additive Models

Cited by 4 publications

References 28 publications

AI-Based Exhaust Gas Temperature Prediction for Trustworthy Safety-Critical Applications

AI-Based Exhaust Gas Temperature Prediction for Trustworthy Safety-Critical Applications

Development of Aerodynamic and Propulsion Models Using the Iterative Equation Error Method

Development of High-Fidelity Saab 340B Aerodynamic Model

Contact Info

Product

Resources

About