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Space Telemetry Anomaly Detection Based on Statistical PCA Algorithm

Authors: M. Mokhtar, B. Nassar, W. Hussein

Abstract:

The critical concern of satellite operations is to ensure the health and safety of satellites. The worst case in this perspective is probably the loss of a mission, but the more common interruption of satellite functionality can result in compromised mission objectives. All the data acquiring from the spacecraft are known as Telemetry (TM), which contains the wealth information related to the health of all its subsystems. Each single item of information is contained in a telemetry parameter, which represents a time-variant property (i.e. a status or a measurement) to be checked. As a consequence, there is a continuous improvement of TM monitoring systems to reduce the time required to respond to changes in a satellite's state of health. A fast conception of the current state of the satellite is thus very important to respond to occurring failures. Statistical multivariate latent techniques are one of the vital learning tools that are used to tackle the problem above coherently. Information extraction from such rich data sources using advanced statistical methodologies is a challenging task due to the massive volume of data. To solve this problem, in this paper, we present a proposed unsupervised learning algorithm based on Principle Component Analysis (PCA) technique. The algorithm is particularly applied on an actual remote sensing spacecraft. Data from the Attitude Determination and Control System (ADCS) was acquired under two operation conditions: normal and faulty states. The models were built and tested under these conditions, and the results show that the algorithm could successfully differentiate between these operations conditions. Furthermore, the algorithm provides competent information in prediction as well as adding more insight and physical interpretation to the ADCS operation.

Keywords: Multivariate analysis, space telemetry monitoring, PCA algorithm, space operations

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109667

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References:


[1] D.L. Iverson, R. Martin, M. Schwabacher, L. Spirkovska, W. Taylor, R. Mackey, and J.P. Castle, “General Purpose Data-Driven System Monitoring for Space Operations,” in Proc. of AIAA Infotech @ Aerospace Conference, Seattle, WA, October 2010.
[2] D.L. Iverson, “Data Mining Applications for Space Mission Operations System Health Monitoring,” NASA Ames Research Center, Moffett Field, California, 94035 Space Operations Conference, 2008.
[3] T. Yairi, M. Inui, A. Yoshiki, Y. Kawahara, and N. Takata, “Spacecraft Telemetry Data Monitoring by Dimensionality Reduction Techniques,” in Proc. SICE Annual Conference, Japan, 2010.
[4] I. Verzola, A.E. Lagny, and J. Biswas, “A Predictive Approach to Failure Estimation and Identification for Space Systems Operations,” in Proc. 13th international conference on space operations, Pasadena, California, USA, May 2014.
[5] J. MacGregora, A. Cinarc, “Monitoring, fault diagnosis, fault-tolerant control, and optimization: Data-driven methods,” Journal of Computers and Chemical Engineering, vol. 47, June 2012.
[6] J. Peng, L. Fan, W. Xiao, and J. Tang, “Anomaly Monitoring Method for Key Components of Satellite,” Scientific World Journal, vol. 2014, Article ID 104052, January 2014.
[7] S. Lindsay, and D. Woodbridge, “Spacecraft State- of-health (SOH) Analysis via Data Mining,” in Proc. 13th international conference on space operations, Pasadena, California, USA, May 2014.
[8] S. Wold, K. Esbensen, and P. Geladi, “Principal component analysis”, Chemometrics and intelligent laboratory systems, vol. 2: 37-52, 1987.
[9] J.E. Jackson, A User’s Guide to Principal Components. Wiley, New York, 1991.
[10] S. Wold, M. Sjostrom, and L. Eriksson, “PLS-regression: a basic tool of Chemometrics”, Chemometrics and intelligent laboratory systems, vol. 58:109-130, 2001.
[11] L. Simar, and W. Hardle, Applied Multivariate Statistical Analysis. Tech method and data technologies. Springer-Verlag, Berlin and Louvain-la- Neuve, 2003.
[12] P.R. Goulding, B. Lennox, D.J. Sandoz, K.J. Smith, and Marjanovic, “Fault detection in continuous processes using multivariate statistical methods,” International journal of systems science, vol. 31(11), pp. 1459-1471, 2000.
[13] T.K. Ralston, G. DePuy, and J.H. Graham, “Computer-based monitoring and fault diagnosis a chemical process case study,” Instrument Society of America Transactions ISA, vol. 40, pp. 85-98. E, USA, 2003.
[14] Y. Zhan, and V.Makis, “A robust diagnostic model for gearboxes subject to vibration monitoring,” Journal of Sound and Vibration, vol. 290, pp. 928–955, 2006.