Machine Learning in Operations for the Mars Express Orbiter

Boumghar, Redouane; Lucas, Luke; Donati, Alessandro

doi:10.2514/6.2018-2551

Cited by 7 publications

(4 citation statements)

References 6 publications

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Section: Ii4 Mars Express Power-predicting Algorithmmentioning

confidence: 99%

“…The Mars Express (MEX) spacecraft of the European Space Agency has been exploring the surface of Mars and performing science operations from orbit since 2004 [36], [37], such as finding evidence of the presence of subsurface Martian water using its Mars Radar for Subsurface and Ionospheric Sounding (described in 2.1.3) [18]. The MEX orbiter is powered by electricity generated by its solar panels, stored in batteries to be used when the sun is not present [36], [37]. The autonomous thermal system of MEX, which maintains the temperature of every instrument onboard MEX, uses a majority of the total generated electric power, leaving only a portion of energy for scientific operations [36], [37].…”

Section: Ii4 Mars Express Power-predicting Algorithmmentioning

confidence: 99%

“…The MEX orbiter is powered by electricity generated by its solar panels, stored in batteries to be used when the sun is not present [36], [37]. The autonomous thermal system of MEX, which maintains the temperature of every instrument onboard MEX, uses a majority of the total generated electric power, leaving only a portion of energy for scientific operations [36], [37]. In order to effectively distribute the total energy between the thermal system and planned scientific procedures, the power consumption of the thermal system, which varies depending on factors such as spacecraft heat and instrument heat, must be predicted to allocate the remaining energy towards scientific operations.…”

Section: Ii4 Mars Express Power-predicting Algorithmmentioning

confidence: 99%

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Machine Learning for Mars Exploration

Momennasab¹

2021

Preprint

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Risk to human astronauts and interplanetary distance causing slow and limited communication drives scientists to pursue an autonomous approach to exploring distant planets, such as Mars. A portion of exploration of Mars has been conducted through the autonomous collection and analysis of Martian data by spacecraft such as the Mars rovers and the Mars Express Orbiter. The autonomy used on these Mars exploration spacecraft and on Earth to analyze data collected by these vehicles mainly consist of machine learning, a field of artificial intelligence where algorithms collect data and self-improve with the data. Additional applications of machine learning techniques for Mars exploration have potential to resolve communication limitations and human risks of interplanetary exploration. In addition, analyzing Mars data with machine learning has the potential to provide a greater understanding of Mars in numerous domains such as its climate, atmosphere, and potential future habitation. To explore further utilizations of machine learning techniques for Mars exploration, this paper will first summarize the general features and phenomena of Mars to provide a general overview of the planet, elaborate upon uncertainties of Mars that would be beneficial to explore and understand, summarize every current or previous usage of machine learning techniques in the exploration of Mars, explore implementations of machine learning that will be utilized in future Mars exploration missions, and explore machine learning techniques used in Earthly domains to provide solutions to the previously described uncertainties of Mars.

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Section: Ii4 Mars Express Power-predicting Algorithmmentioning

confidence: 99%

Section: Ii4 Mars Express Power-predicting Algorithmmentioning

confidence: 99%

Section: Ii4 Mars Express Power-predicting Algorithmmentioning

confidence: 99%

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Machine Learning for Mars Exploration

Momennasab¹

2021

Preprint

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“…The time span between the two consecutive entries varies from less than a minute (SAA) to several hours (LTDATA). For a detailed description of the task and the data, we refer the reader to [3], [21].…”

Section: The Datamentioning

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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Machine-learning ready data on the thermal power consumption of the Mars Express Spacecraft

Petković

Lucas²,

Levatić

et al. 2022

Sci Data

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We present six datasets containing telemetry data of the Mars Express Spacecraft (MEX), a spacecraft orbiting Mars operated by the European Space Agency. The data consisting of context data and thermal power consumption measurements, capture the status of the spacecraft over three Martian years, sampled at six different time resolutions that range from 1 min to 60 min. From a data analysis point-of-view, these data are challenging even for the more sophisticated state-of-the-art artificial intelligence methods. In particular, given the heterogeneity, complexity, and magnitude of the data, they can be employed in a variety of scenarios and analyzed through the prism of different machine learning tasks, such as multi-target regression, learning from data streams, anomaly detection, clustering, etc. Analyzing MEX’s telemetry data is critical for aiding very important decisions regarding the spacecraft’s status and operation, extracting novel knowledge, and monitoring the spacecraft’s health, but the data can also be used to benchmark artificial intelligence methods designed for a variety of tasks.

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Machine Learning in Operations for the Mars Express Orbiter

Cited by 7 publications

References 6 publications

Machine Learning for Mars Exploration

Machine Learning for Mars Exploration

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

Machine-learning ready data on the thermal power consumption of the Mars Express Spacecraft

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