The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its predefined 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 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.
Spacecraft housekeeping telemetry is monitored at flight control centers by the operations engineers using tools that can perform limit checking or simple trend analysis. Recent developments in machine learning techniques for anomaly detection enables the implementation of more sophisticated systems that aim to augment current state-of-the-art mission tools to provide valuable decision support for the spacecraft operators, assisting in anomaly detection and potentially saving console time for the engineers. We will show some results of the implementation of an anomaly detection tool for the NASA Mars Science Laboratory mission. We introduce the challenge of using machine learning effectively in space applications and motivate the domain for future researchers. Machine learning can be used to enable greater autonomy to improve the duration, reliability, cost-effectiveness, and science return of space missions. In addition to the challenges provided by the nature of space itself, the requirements of a space mission severely limit the use of many current machine learning approaches, and we encourage researchers to explore new ways to address these challenges. A machine learning approach for the operational situational awareness (OSA) in flight operations is presented. The spacecraft health and safety telemetry are generally time dependent and periodical. The machine learning algorithms, such as neural networks, are used to capture the time dependent trend of the telemetry datasets characterized by their data patterns and noise level, which provides a direct insights into the health and safety status of telemetry datasets. As the time dependent trends are highly sensitive to changes above the noise level, the potential anomalies can be detected at much early stage, which leads to a more proactive flight operations and a more resilient system. The challenges for the machine-learning approach in the spacecraft ground system are to develop a systematic, accurate, adaptive and efficient data training strategy, and a representation to meet persistent requirement. The focus of this paper is to present a machine-learning approach for the time dependent trending of spacecraft datasets with arbitrary scales. The data training algorithms have been developed and implemented in neural networks, which are shown to generate highly accurate time dependent trend. The OSA tool, ASRC Intelligent Monitoring System (AIMS), is presented, which implements the machine-learning algorithm. The extensions of machine-learning algorithms in developing capabilities to improve the mission efficiency and enable more autonomous operations are discussed. For any space mission, safety and reliability are the most important issues. To tackle this problem, we have studied anomaly detection and fault diagnosis methods for spacecraft systems based on machine learning (ML) and data mining (DM) technology. In these methods, the knowledge or model which is necessary for monitoring a spacecraft system is (semi-)automatically acquired from the spacecraft telemetry data. In this paper, we first overview the anomaly detection diagnosis problem in the spacecraft systems and conventional techniques such as limit-check, expert systems and model-based diagnosis. Then we explain the concept of ML DM-based approach to this problem, and introduce several anomaly detection diagnosis methods which have been developed by us.