An anomaly detection method for spacecraft using relevance vector learning

Authors:
Ryohei Fujimaki;Takehisa Yairi;Kazuo Machida
Affiliations:
The Univ. of Tokyo, Aero. and Astronautics;The Univ. of Tokyo, RCAST;The Univ. of Tokyo, RCAST
Venue:
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Year:
2005

Citing 3
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Abstract

This paper proposes a novel anomaly detection system for spacecrafts based on data mining techniques. It constructs a nonlinear probabilistic model w.r.t. behavior of a spacecraft by applying the relevance vector regression and autoregression to massive telemetry data, and then monitors the on-line telemetry data using the model and detects anomalies. A major advantage over conventional anomaly detection methods is that this approach requires little a priori knowledge on the system.