T he most widely extended approach for automatically detecting anomalous behavior in space operations is the use of out-of-limits (OOL) alarms. The OOL approach consists of defining an upper and lower threshold so that when a measurement goes above the upper limit or below the lower one, an alarm is triggered. Then engineers will inspect the parameter that is out of limits and determine whether it is an anomaly or not and decide which action to take (for example, run a procedure). This is the original outof-limits concept.The current OOL concept has evolved to cope with more situations such as distinguishing between soft and hard limits; for example, a soft OOL triggers a warning to pay attention, a hard OOL triggers an error that demands attention. Soft limits are contained within hard limits. In addition OOL thresholds (soft and hard) can be configured so that different thresholds are applicable in different situations (for example, depending on the working mode of a given instrument).
DrMUST is a data mining MUST client that can support flight control engineers in their anomaly investigation tasks. It performs pattern matching and correlation analysis. The pattern matching functionality can be used to find occurrences of a certain behavior (e.g. to know when certain anomaly happened in the past and went unnoticed). The correlation analysis can find which parameters are involved in a certain event of interest (e.g. anomaly). The correlation analysis is based on statistical features and is more robust and efficient than the classical mathematical correlation, allowing to perform correlations in linear time. DrMUST can also be used to characterize how a spacecraft is affected by environmental changes (e.g. solar flares). This work describes the technology behind DrMUST and provides real examples from ESA operated missions.
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