CASTeC - Context Aware Spacecraft Telemetry Checking

CASTeC - Context Aware Spacecraft Telemetry Checking

Satellite Health monitoring by means of Machine Learning

CASTeC is a software tool intended to ease the labour-intensive task of spacecraft telemetry checking, by automating the telemetry signals trend analysis and the detection of anomalous behaviours and novelties.

It provides a predictive and proactive monitoring based on data mining and autonomous machine learning techniques, so allows to relieve the Flight Control Engineers from manually setting alarm and warning thresholds over the thousands of parameters of housekeeping telemetry shaping satellite’s health status.
CASTeC learns from the nominal system behaviour, derived either from models and simulations, or from real telemetry data labelled by operators during routine operation.

The telemetry checking is performed by evaluating a large number of statistical features over distributed time intervals, identifying the significant ones and comparing them with reference values.
When a feature deviates from these references values, autonomously defined, the tool highlights the novelty or the trend anomaly in the parameters, raising alarms and warnings based on smarter criteria than usual simple signal range check, and, in addition, with thresholds that are autonomously tailored by the software tool.

CASTeC aims to be a support tool for flight control engineers, complementing their “sensibility” that is deeply based on system knowledge and experience and so to help where systems and models alone were hardly able to be effective.

In this sense, a further great advantage provided by CASTeC is that it works “context aware” and so it is able to distinguish several different nominal behaviors, related to satellite position in the orbit track (e.g. Earth/ orbiting body distance, sunlight or umbra…) and subsystems’ status. Knowledge of the operational conditions increase the effectiveness of AI algorithms, allowing to drastically reduce the number of false alarms.


  • Autonomous mining of monitoring key features
  • Autonomous thresholds learning
  • Autonomous anomalies detection
  • Contexts management
  • Synoptic views of the health status of the spacecraft
  • Housekeeping drill down and cross-comparison
  • 3D models and videos for advanced investigation
  • Collaboration tools for analyses sharing and discussion
  • Report generation

Its graphical interface provides a toolset intended to a quick and friendly browsing and drill-down of the (huge) telemetry time series; provides navigable views like heatmaps (also animated as video sequences) and 3D satellite  models highlighting sub-systems according their detected health status.
It also integrates dashboard and team collaboration tools for sharing the results of each flight engineer custom analysis.

CASTeC allows to integrate AI algorithms, configure and execute them in a Spark environment.
The graphical user interfaces exploit Web Technologies, REST interfaces are adopted at system’s components level and JSON structure format for data exchange.

System is based on Docker containers that collect each component of the system so to make it very modular and ease the installation on different environments.

CASTeC has been developed by S.A.T.E. and Planetek in the frame of a contract with the Advanced Operations Technology group at ESOC.

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