An Anomaly Detection Method for Spacecraft Using Relevance Vector Learning

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.

• Type: Conference
• Year: 2005
• Where: PAKDD
• Authors: Ryohei Fujimaki, Takehisa Yairi, Kazuo Machida
• Keywords: Anomaly Detection, Data Mining, Massive Telemetry Data, PAKDD 2005, Telemetry Data