Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding
Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding Hundman et al., KDD’18
How do you effectively monitor a spacecraft? That was the question facing NASA’s Jet Propulsion Laboratory as they looked forward towards exponentially increasing telemetry data rates for Earth Science satellites (e.g., around 85 terabytes/day for a Synthetic Aperture Radar satellite).
Spacecraft are exceptionally complex and expensive machines with thousands of telemetry channels detailing aspects such as temperature, radiation, power, instrumentation, and computational activities. Monitoring these channels is an important and necessary component of spacecraft operations given their complexity and cost. In an environment where a failure to detect and respond to potential hazards could result in the full or partial loss of spacecraft, anomaly detection is a critical tool to alert operations engineers of unexpected behavior.
Anomaly detection systems deployed today typically consist of tiered alarms indicating when values fall outside of pre-defined limits. There are also limited instances of expert systems and nearest-neighbour based approaches being tried, but their limitations prevented widespread adoption. A more accurate and scalable approach to anomaly detection that makes better use of limited engineering resources is required.
Any such system needs to work with data that is highly Continue reading

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