Machine Learning for Spacecraft Operations Support - The Mars Express Power Challenge

Lucas, Luke; Boumghar, Redouane

doi:10.1109/smc-it.2017.21

Cited by 18 publications

(17 citation statements)

References 13 publications

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“…One promising avenue of investigation is based on data analytic techniques and machine learning algorithms. These techniques have been applied successfully to several test case including optimising power consumption for currently, or formerly, active European Space Agency satellites, VEX [4], MEX [5][6] & XMM-Newton [7] during their respective eclipse seasons where power generation is severely constrained but significant power load can still exist, particularly to maintain the satellites thermal equilibrium while in the eclipse umbra. Thus far machine learning data analytics have not been actively used operationally for ESA satellites but show great potential for optimisation and automation of certain space activities particularly when large amount of continuous data is available.…”

Section: Van Allan Belts and Radiation Effectsmentioning

confidence: 99%

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Machine Learning Modeling Methods for Radiation Belts Profile Predictions

Finn¹,

Boumghar²,

Martínez³

et al. 2018

2018 SpaceOps Conference

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This paper presents the results of the potential application of machine learning techniques, specifically the Random Forest method, to spacecraft operations optimization. The test subject is ESAs INTEGRAL gamma ray observatory with the goal of demonstrating that AI techniques can reliably model the radiation environment of the satellite as it orbits the Earth and passes through the Earths trapped radiation zones in the Van Allen belts. The results clearly demonstrate that machine learning can approximate predictions of complex and dynamic radiation environment within +/-10% provided that an extensive data set is available and is adequately engineered. The consequences of such accurate data-driven predictions are that comprehensive physical models may be, under certain circumstances, an unnecessarily complicated solution to the optimization of scientific operations of Earth orbiting satellites.

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Section: Van Allan Belts and Radiation Effectsmentioning

confidence: 99%

“…For aging spacecraft they represent a very good complement to theory and manufacturer and engineers' models as depicted by the Mars Express orbiter thermal power consumption prediction method [5,6]. These methods are also well suited for new missions as they can be made to adapt quickly.…”

Section: Machine Learningmentioning

confidence: 99%

Machine Learning Modeling Methods for Radiation Belts Profile Predictions

Finn¹,

Boumghar²,

Martínez³

et al. 2018

2018 SpaceOps Conference

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“…The Mars Express Power Challenge [13] was a machine learning challenge that has opened many perspectives to the development of ML in space operations. The challenge used open data to crowd-source solutions to predict the thermal subsystem power consumptions.…”

Section: Ai and Machine Learning For Space Operationsmentioning

confidence: 99%

“…The challenge used open data to crowd-source solutions to predict the thermal subsystem power consumptions. Flight controllers interacted with a large community of data scientists to unveil the hidden data, bringing two main internal outcomes: (i) a more precise understanding of the chains of influences to the thermal power consumption; (ii) a fast review and prototyping of many different methods: LSTM [14], Recurrent Neural Networks [15], XGBoost [16], random forest [17], and others listed in [13]. The best solution, described in [18], shows how the influence of the features on the target parameters can be inferred from mission operations data when analyzing the model parameters of the random forest tree predictors.…”

Section: Ai and Machine Learning For Space Operationsmentioning

confidence: 99%

Optimization of the battery usage during eclipses using a machine learning approach

Canio

Boumghar²,

Godard³

et al. 2018

2018 SpaceOps Conference

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During eclipses, the spacecraft cannot rely on solar energy and must exploit the power coming from the on-board batteries. Critical for the payload is to maintain its temperature inside a specific predetermined range to avoid degradation and malfunctioning. For XMM-Newton, this is done through the Temperature Closed Loop (TCL) unit, which automatically turns on and off the heaters when the instruments reach the minimum and maximum temperatures, respectively. A possible scenario is the case when more than a single instrument reaches the minimum temperature simultaneously, thus causing a peak in the demand of power from the batteries and threatening their integrity. Such a circumstance may impact the overall mission and a better battery management is therefore required. Here we propose a solution that uses machine learning to optimize the battery consumption during eclipses. We show that after training the algorithm with past data, we are able to predict the temperature profiles of the instruments with good accuracy. These predictions provide the relevant insight to determine the optimal times at which the heaters should be turned on and off, thus improving and optimizing the current eclipse operations. The approach presented here helps overcome the issue of excessively stressing the batteries and, despite being tailored for XMM-Newton, can be extended and applied to other missions as well.

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“…Temperatures fluctuate by up to 28°C due to these two different on/off conditions. Current attempts at modeling and predicting the power consumption, involve manually constructed models that are based on simplified first-principle models, expert knowledge and experience.Given MEX's current condition, this prompts for a more accurate predictive model of the thermal power consumption, which would yield prolonged operating life.This motivated the organization of the first ESA's data mining competition -the Mars Express Power Challenge [3]. The focus of the challenge was the development of specialized approaches for constructing models that are able to accurately estimate and predict the MEX's thermal power consumption (TPC) given only measured telemetry data.…”

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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 for Spacecraft Operations Support - The Mars Express Power Challenge

Cited by 18 publications

References 13 publications

Machine Learning Modeling Methods for Radiation Belts Profile Predictions

Machine Learning Modeling Methods for Radiation Belts Profile Predictions

Optimization of the battery usage during eclipses using a machine learning approach

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

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