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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Faul, A.C., Tipping, M.E.: Analysis of Sparse Bayesian Learning. In: Dietterich, T.G., Becker, S., Ghahramanim, Z. (eds.) Advances in Neural Information Processing Systems, vol.�14, pp. 383–389. MIT Press, Cambridge (2002)
Bishop, C.M., Tipping, M.E.: Variational Relevance Vector Machines. In: Boutilier, C., Goldszmidt, M. (eds.) Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pp. 46–53. Morgan Kaufmann, San Francisco (2000)
Muller, K.-R., Smola, A.J., Ratsch, G., Scholkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting time series with support vector machines. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997)
Tipping, M.E.: Sparse Bayesian Learning and the Relevance Vector Machinem. Journal of Machine Learning Research 1, 211–244 (2001)
Tipping, M.E.: The Relevance Vector Machine. In: Solla, S.A., Leen, T.K., Muller, K.-R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 652–658. MIT Press, Cambridge (2000)
Tipping, M.E., Faul, A.C.: Fast marginal likelihood maximisation for sparse Bayesian models. In: Bishop, C.M., Frey, B.J. (eds.) Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL, January 3-6 (2003)
Nakatsugawa, M., Yairi, T., Ishihama, N., Hori, K., Nakasuka, S.: Supporting Anomaly Detection from Satellite Telemetry Data by Regression Trees. In: The 24th International Symposium on Space Technology and Science (ISTS) (2004)
Yairi, T., Ogasawara, S., Hori, K., Nakasuka, S., Ishihama, N.: Summarization of Spacecrafts Telemetry Data By Extracting Significant Temporal Patterns. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 240–244. Springer, Heidelberg (2004)
Yairi, T., Kato, Y., Hori, K.: Fault Detection by Mining Association Rules from House-keeping Data. In: Proc. of International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2001) (2001)
Muller, U.U., Schick, A., Wefelmeyer, W.: Efficient prediction for linear and nonlinear autoregressive models (2004) (Submitted paper)
Penny, W.D., Roberts, S.J.: Bayesian Methods for Autoregressive Models. In: IEEE Workshop on Neural Networks for Signal Processing, Sydney Australia (December 2000)
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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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