A data-driven approach for mode and anomaly detection from Spacecraft Telemetry Streams

Gautam Biswas, Hamed Khorasgani, Gerald Stanje, Abhishek Dubey, Somnath Deb, and Sudipto Ghoshal
Publication Target: 
Publication Issue: 
Special Issue Big Data and Analytics
Submission Type: 
Full Paper
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NASA STTR grant # NNX15CA11C
ijphm_16_033.pdf1.78 MBDecember 7, 2016 - 6:29pm

This paper discusses a mixed method that combines unsupervised learning methods and human expert input for analyzing telemetry data from long-duration robotic space missions. Our goal is to develop more automated methods for detecting anomalies in time series data. Once anomalies are identified using unsupervised learning methods we use feature selection methods followed by expert input to derive the knowledge required for building on-line detectors. These detectors
can be used in later phases of the current mission and in future missions for improving operations and overall safety of the mission. Whereas the primary focus in this paper is on developing data-driven anomaly detection methods, we also
present a computational platform for data mining and analytics that can operate on historical data offline, as well as incoming telemetry data on-line.

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Submission Keywords: 
anomaly detection; unsupervised learning; data driven methods; time series data
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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