Touchdown Point Detection for Operational Flight Data Using Quality Measures and a Model Based Approach

2018 ◽  
Author(s):  
Phillip Koppitz ◽  
Joachim Siegel ◽  
Nikolaus Romanow ◽  
Lukas Höhndorf ◽  
Florian Holzapfel
Keyword(s):  
Quality Measures ◽  
Flight Data ◽  
Model Based ◽  
Touchdown Point ◽  
Point Detection

On-Board Model Based Fault Diagnosis for CubeSat Attitude Control Subsystem: Flight Data Results

2021 ◽  
Author(s):  
Ryan Mackey ◽  
Allen Nikora ◽  
Cornelia Altenbuchner ◽  
Robert Bocchino ◽  
Michael Sievers ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Attitude Control ◽  
Flight Data ◽  
Control Subsystem ◽  
Model Based

On applying AI-driven flight data analysis for operational spacecraft model-based diagnostics

2020 ◽  
Vol 49 ◽  
pp. 197-211
Author(s):  
Massimo Tipaldi ◽  
Lorenzo Feruglio ◽  
Pierre Denis ◽  
Gianni D’Angelo
Keyword(s):  
Data Analysis ◽  
Flight Data ◽  
Model Based

scholarly journals Model-based branching point detection in single-cell data by K-branches clustering

2017 ◽  
Vol 33 (20) ◽  
pp. 3211-3219 ◽  
Author(s):  
Nikolaos K Chlis ◽  
F Alexander Wolf ◽  
Fabian J Theis
Keyword(s):  
Single Cell ◽  
Model Based ◽  
Branching Point ◽  
Point Detection ◽  
Cell Data

scholarly journals Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4634
Author(s):  
Xiaolong Wang ◽  
Lukas Beller ◽  
Claudia Czado ◽  
Florian Holzapfel
Keyword(s):  
Spectral Representation ◽  
Expansion Method ◽  
Statistical Characteristics ◽  
Wind Model ◽  
Flight Data ◽  
Model Based ◽  
Dimensional Dependence ◽  
Turbulence Data ◽  
Wind Characteristics ◽  
Vine Copula

Wind has a significant influence on the operational flight safety. To quantify the influence of the wind characteristics, a wind series generator is required in simulations. This paper presents a method to model the stochastic wind based on operational flight data using the Karhunen–Loève expansion. The proposed wind model allows us to generate new realizations of wind series, which follow the original statistical characteristics. To improve the accuracy of this wind model, a vine copula is used in this paper to capture the high dimensional dependence among the random variables in the expansions. Besides, the proposed stochastic model based on the Karhunen–Loève expansion is compared with the well-known von Karman turbulence model based on the spectral representation in this paper. Modeling results of turbulence data validate that the Karhunen–Loève expansion and the spectral representation coincide in the stationary process. Furthermore, construction results of the non-stationary wind process from operational flights show that the generated wind series have a good match in the statistical characteristics with the raw data. The proposed stochastic wind model allows us to integrate the new wind series into the Monte Carlo Simulation for quantitative assessments.

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