Machine learning in telemetry data mining of space mission: basics, challenging and future directions

Abstract

The development of an intelligent artificial satellite health monitoring system is a key issue in aerospace engineering that determines satellite health status and failure using telemetry data. The modern design of data mining and machine learning technologies allows the use of satellite telemetry data and the mining of integrated information to produce an advanced health monitoring system. This paper reviews the current status and presents a framework of necessary processes on data mining to solving various problems in telemetry data such as error detection, prediction, summarization, and visualization of large quantities, and help them understand the health status of the satellite and detect the symptoms of anomalies. Machine learning technologies that include neural networks, fuzzy sets, rough sets, support vector machines, Naive Bayesian, swarm optimization, and deep learning are also presented. Also, this paper reviews a wide range of existing satellite health monitoring solutions and discusses them in the framework of remote data mining techniques. In addition, we are discussing the analysis of space debris flow analysis and the prediction of low earth orbit collision based on our orbital Petri nets model. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.