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15 July 2016 Data mining spacecraft telemetry: towards generic solutions to automatic health monitoring and status characterisation
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We present the first results of a study aimed at finding new and efficient ways to automatically process spacecraft telemetry for automatic health monitoring. The goal is to reduce the load on the flight control team while extending the "checkability" to the entire telemetry database, and provide efficient, robust and more accurate detection of anomalies in near real time. We present a set of effective methods to (a) detect outliers in the telemetry or in its statistical properties, (b) uncover and visualise special properties of the telemetry and (c) detect new behavior. Our results are structured around two main families of solutions. For parameters visiting a restricted set of signal values, i.e. all status parameters and about one third of all the others, we focus on a transition analysis, exploiting properties of Poincare plots. For parameters with an arbitrarily high number of possible signal values, we describe the statistical properties of the signal via its Kernel Density Estimate. We demonstrate that this allows for a generic and dynamic approach of the soft-limit definition. Thanks to a much more accurate description of the signal and of its time evolution, we are more sensitive and more responsive to outliers than the traditional checks against hard limits. Our methods were validated on two years of Venus Express telemetry. They are generic for assisting in health monitoring of any complex system with large amounts of diagnostic sensor data. Not only spacecraft systems but also present-day astronomical observatories can benefit from them.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
P. Royer, J. De Ridder, B. Vandenbussche, S. Regibo, R. Huygen, W. De Meester, D. J. Evans, J. Martinez, and M. Korte-Stapff "Data mining spacecraft telemetry: towards generic solutions to automatic health monitoring and status characterisation", Proc. SPIE 9910, Observatory Operations: Strategies, Processes, and Systems VI, 99101I (15 July 2016);


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