Data mining to drastically improve spacecraft telemetry checking: An engineer’s approach

Abstract

The number of telemetry parameters in a typical spacecraft is constantly increasing. At the same time the number of operators allocated to each spacecraft to check those parameters is constantly decreasing. Techniques such as limit checking are well known but they take time and effort to define, enter and manage as the mission evolves. The result is that the vast majority of telemetry parameters are not limit checked in real-time. In 2014, the Advanced Operation Concepts Office at ESA/ESOC decided to see if we could change this by employing Big Data type techniques on the data. The idea was simple, we asked our partner, SATE of Italy, to define future checks for all telemetry parameters given one year’s worth of historical data. No engineering knowledge was provided and the derivation of the checks had to be completely automatic i.e. the checks had to be derived solely on the data itself with no human intervention. The mission we choose was Venus express (VEX) and the learning period ended just before the aero-braking activities started. We then applied these checks to the following three months of data which included interesting activities such as aero-braking preparation and aero-braking itself. This test data was not provided to SATE until after they had submitted their checks to us for validation. This paper describes SATE’s response to this challenge. SATE decided to take a very pragmatic, engineering view of the problem and defined algorithms to search for anything that could be classed as constant in the data. This could be simple features of the data such as average or more exotic features such as harmonic mean, FFT coefficients and features characterizing the sampling rate. 47 features were selected in the end (35 for numerical and 12 for categorical parameters), over different time windows resulting in over 500,000 possible time series. SATE delivered checks for every telemetry parameter of the VEX satellite, extending also the study to telemetry data of the XMM satellite available from a preceding project. This paper then goes on to describe the validation exercise carried out at ESOC in which the delivered checks were run on the new data and the results compared to actual operational events. After some optimisation, which were required to reduce the level of false negatives to reasonable levels the validation team produced some extremely interesting results creating a very accurate and detailed insight into the future operations. ESOC is currently planning to deploy these techniques operationally for flying spacecraft in the near future.