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.
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Fujimaki, R., Yairi, T., Machida, K. (2005). An Anomaly Detection Method for Spacecraft Using Relevance Vector Learning. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_92
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DOI: https://doi.org/10.1007/11430919_92
Publisher Name: Springer, Berlin, Heidelberg
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