A Belgian university research group frequently collaborating with ESA/NASA with a large body of knowledge in data processing algorithms, classification algorithms has developed advanced data mining techniques for automatic spacecraft status characterization.
The tools were developed for novelty detection in large sets of telemetry parameters and derived parameters to be used in environments where automatic checks and computer-aided data analysis are needed to help operators and operation engineers keep an overview.
These sets of data mining techniques have the possibility to assist in:
- Detecting anomalies/sudden change in behavior
- Detecting trends or change in trends
- Finding correlations based on Command and Effect Analysis.
The development of novelty detection tools is a result of extensive research in data mining techniques that are useful for the health monitoring of spacecraft.
The underlying principle is the identification of anomalies that are defined as telemetry behaviour that is so different from its historical behaviour that it raises suspicion that it is caused by a different mechanism.
The developed algorithms can be used in environments where a large number of housekeeping parameters are monitored and while providing a wealth of diagnostic information, these vast numbers exceed the possibilities of the human brain to oversee all telemetry measures. Automatic checks and computer-aided data analysis are needed to help operators and operation engineers keep the overview.
Potential applications are to be sought in the monitoring of complex systems with large amounts of sensor data, e.g. automated cockpits (aviation), industrial equipment, Windmills, etc).