Machine Learning Modeling Methods for Radiation Belts Profile Predictions

Finn, Timothy; Boumghar, Redouane; Martínez, José Antonio González; Georgiadou, Antigoni

doi:10.2514/6.2018-2639

Cited by 5 publications

(6 citation statements)

References 5 publications

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“…For this task, we consider two data representations -positional and per-revolution. The former, positional representation, is similar to the one proposed by Finn et al [14]. Here, the data is ordered in a series where examples describe the state of the spacecraft using the orbital elements and the IREM counts (or binary indicators whether INTEGRAL is in the belts or not).…”

Section: B Galaxai-integralmentioning

confidence: 99%

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GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data

Kostovska¹,

Petković²,

Stepišnik³

et al. 2021

Preprint

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We present GalaxAI -a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs various machine learning algorithms for multivariate time series analyses, classification, regression, and structured output prediction, capable of handling high-throughput heterogeneous data. These methods allow for the construction of robust and accurate predictive models, that are in turn applied to different tasks of spacecraft monitoring and operations planning. More importantly, besides the accurate building of models, GalaxAI implements a visualisation layer, providing mission specialists and operators with a full, detailed, and interpretable view of the data analysis process. We show the utility and versatility of GalaxAI on two use-cases concerning two different spacecraft: i) analysis and planning of Mars Express thermal power consumption and ii) predicting of INTEGRAL's crossings through Van Allen belts.

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Section: B Galaxai-integralmentioning

confidence: 99%

“…Accurately modeling and predicting the spacecraft's position w.r.t these radiation belts is important. This allows for better control over activation/deactivation of onboard instruments and ultimately leading to optimal scientific output [14].…”

Section: Introductionmentioning

confidence: 99%

GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data

Kostovska¹,

Petković²,

Stepišnik³

et al. 2021

Preprint

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“…Finally, while the proposed methodology focuses on the thermal subsystem of the MEX spacecraft, it can also be readily applied to the other subsystems. Moreover, it can also be extended to other spacecraft such as the XMM Newton [17], Integral [20] and ExoMars as well as rovers (such as Curiosity and ExoMars) exploring Mars.…”

Section: Ensemble Of Ensemblesmentioning

confidence: 99%

Machine Learning for Predicting Thermal Power Consumption of the Mars Express Spacecraft

Petković

Boumghar

Breskvar

et al. 2019

IEEE Aerosp. Electron. Syst. Mag.

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The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its pre-defined operating temperatures range. To plan and optimize the scientific operations of MEX, its operators need to estimate in advance, as accurately as possible, the power consumption of the thermal subsystem. The remaining power can then be allocated for scientific purposes.We present a machine learning pipeline for efficiently constructing accurate predictive models for predicting the power of the thermal subsystem on board MEX. In particular, we employ state-of-the-art feature engineering approaches for transforming raw telemetry data, in turn used for constructing accurate models with different state-of-the-art machine learning methods. We Accepted at IEEE Aerospace and Electronic Systems Magazine 2 show that the proposed pipeline considerably improve our previous (competition-winning) work in terms of time efficiency and predictive performance. Moreover, while achieving superior predictive performance, the constructed models also provide important insight into the spacecraft's behavior, allowing for further analyses and optimal planning of MEX's operation. Index Terms machine learning, Mars Express spacecraft, ensemble learning, predictive modeling, random forest, gradient boosting, feature engineering I. INTRODUCTION M ARS EXPRESS (MEX), a spacecraft operated by the European Space Agency (ESA), is Europe's first spacecraft that orbits Mars. During its science operations, since the beginning of 2004, it has provided evidence of the presence of water above and below the surface of the planet [1], an ample amount of three-dimensional renders of the surface as well as the most complete map of the chemical composition of Mars's atmosphere [2].MEX is powered by electricity generated by its solar arrays and stored in batteries to be used during the eclipse periods. The scientific payload of the MEX consists of seven instruments that provide global coverage of the planet's surface, subsurface and atmosphere. The instruments and on-board equipment have to be kept within their operating temperature ranges, spanning from room temperature for some instruments, to temperatures as low as -180°C for others. In order to maintain these predefined operating temperatures, the spacecraft is equipped with an autonomous thermal system composed of 33 heater lines as well as coolers. The thermal system, together with the platform units, consumes a significant amount of the total generated electric power, leaving a fraction to be used for science operations.Predicting the power consumption of the thermal system is a non-trivial task. However, due to the aging of the spacecraft and the decaying capacity of its batteries, it is a very crucial one for optimal planning and execution of science operations on MEX. The power consumption is a dynamic process that changes through time, depending on various external and internal factors, such as long-term exposure of the spacecraft to the Sun or heat generated by the onboard instruments. F...

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“…The outcome of which was an unprecedented interest in MEX, an incredibly enthusiastic response from the ML community with several models which are an order of magnitude more accurate than the predictive model presently used by the MEX FCT. Demonstrated on Mars Express, similar approaches such as [4,5] are already applied to other spacecraft and subsystems.…”

Section: B Mars Express Thermal Subsystem Use-casementioning

confidence: 99%

Machine Learning in Operations for the Mars Express Orbiter

Boumghar¹,

Lucas²,

Donati³

2018

2018 SpaceOps Conference

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This paper shows how the recent development and integration of a machine learning predictive model provided MEX with a more sensitive and accurate model of the thermal power consumption with points every hour. Machine learning (ML) techniques are highly applicable to multivariate problems with complex relations and constraints, such as spacecraft operations, particularly a context as complex as interplanetary operations. The thermal power consumption is the result of complex and dynamic system. A comprehensive understanding is difficult to achieve, due to these complexities. The approach is thus to understand the link between flight scenarios and the thermal power consumption on the 33 thermal lines identified as main lines of the thermal power subsystem. The insight given by this understanding helps building better models and finding the right points of improvement to make them ready for operations.

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Machine Learning Modeling Methods for Radiation Belts Profile Predictions

Cited by 5 publications

References 5 publications

GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data

GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data

Machine Learning for Predicting Thermal Power Consumption of the Mars Express Spacecraft

Machine Learning in Operations for the Mars Express Orbiter

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