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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Fujimaki, R., Yairi, T., & Machida, K. (2005). An anomaly detection method for spacecraft using relevance vector learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3518 LNAI, pp. 785–790). Springer Verlag. https://doi.org/10.1007/11430919_92
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