Monitoring Spacecraft Telemetry with a Fleet of LSTMs


Spacecraft are exceptionally complex and expensive machines with thousands of telemetry channels detailing temperature, radiation, power, instrumentation, and computational activities. Current alarm-based systems for helping engineers monitor spacecraft health are insufficient, and increasing data rates and volumes exacerbate this problem. Fortunately, improvements in technology and deep learning now provide the tools to build improved anomaly detection systems. This talk will describe the development of our current LSTM-based system from research to deployment.


Kyle Hundman is a data scientist and group lead within the IT Chief Technology and Innovation Office at NASA’s Jet Propulsion Laboratory (JPL) and is also currently enrolled in the Machine Learning track of the Masters of Computer Science program at Columbia University. Prior to joining JPL he graduated from Northwestern University’s Masters of Science in Analytics (MSiA) program in 2014. In addition to developing spacecraft anomaly detection systems, he is working on adapting and extending search, natural language processing, and machine learning research from the DARPA Memex program for use by JPL’s Mission Formulation office.

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Consent to display content from - Youtube
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google