Application of ML to Detection of Anomalies in Spacecraft Health and Status Data
Description
We are collaborating with the Magnetospheric Multiscale (MMS) mission to research Machine Learning (ML) techniques capable of predicting and detecting anomalies in spacecraft health and status data. We are combining historical MMS telemetry data, with known mission events and anomalies, to perform unsupervised ML techniques. We have primarily used Temporal Convolutional Networks (TCN) as they preserve the temporal nature of our data while detecting long term and short term trends in the data.
Detailed example
Detection of anomalies
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
This research provides new insights in telemetry data patterns and may reduce the time it takes to identify anomalies, allowing operators to focus on finding resolutions to ensure spacecraft health and safety.
Controls / human review
ATO: No; PIA: Not published
Data needed
MMS and LRO telemetry data from GSFC's Telemetry as a Service (TaaS) tool