Advertisement

Towards the Identification of Outliers in Satellite Telemetry Data by Using Fourier Coefficients

  • Fabien BouleauEmail author
  • Christoph Schommer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8946)

Abstract

Spacecrafts provide a large set of on-board components information such as their temperature, power and pressure. This information is constantly monitored by engineers, who capture the outliers and determine whether the situation is abnormal or not. However, due to the large quantity of information, only a small part of the data is being processed or used to perform anomaly early detection. A common accepted research concept for anomaly prediction as described in literature yields on using projections, based on probabilities, estimated on learned patterns from the past [6] and data mining methods to enhance the conventional diagnosis approach [14]. Most of them conclude on the need to build a pattern identity chart. We propose an algorithm for efficient outlier detection that builds an identity chart of the patterns using the past data based on their curve fitting information. It detects the functional units of the patterns without apriori knowledge with the intent to learn its structure and to reconstruct the sequence of events described by the signal. On top of statistical elements, each pattern is allotted a characteristics chart. This pattern identity enables fast pattern matching across the data. The extracted features allow classification with regular clustering methods like support vector machines (SVM). The algorithm has been tested and evaluated using real satellite telemetry data. The outcome and performance show promising results for faster anomaly prediction.

Keywords

Data mining Time series Machine learning Pattern identification 

Notes

Acknowledgements

This work has been made within the research project SPACE, which is an interdisciplinary research project between the University Luxembourg, Department of Computer Science and SES Engineering. We thank all the SPACE members as well as all the SES engineers for their kind support. The views expressed herein represent the authors’ views only and do not in any way bind or commit SES Engineering itself.

References

  1. 1.
    Azevedo, D.N.R., Ambrósio, A.M.: Dependability in satellite systems: An architecture for satellite telemetry analysisGoogle Scholar
  2. 2.
    Beringer, J., Hüllermeier, E.: Online clustering of parallel data streams. Data Knowl. Eng. 58(2), 180–204 (2006)CrossRefGoogle Scholar
  3. 3.
    Bouleau, F., Schommer, C.: Outlier identification in spacecraft monitoring data using curve fitting information. In: European Conference on Data Analysis, p. 158 (2013)Google Scholar
  4. 4.
    Das, K., Bhaduri, K., Votava, P.: Distributed anomaly detection using 1-class SVM for vertically partitioned data. Stat. Anal. Data Min. 4(4), 393–406 (2011)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Deng, K., Moore, A., Nechyba, M.: Learning to recognize time series: combining arma models with memory-based learning. In: IEEE International Symposium on Computational Intelligence in Robotics and Automation, vol. 1, pp. 246–250 (1997)Google Scholar
  6. 6.
    Fujimaki, R., Yairi, T., Machida, K.: An anomaly detection method for spacecraft using relevance vector learning. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 785–790. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  7. 7.
    Fukushima, Y.: Telemetry data mining with SVM for satellite monitoringGoogle Scholar
  8. 8.
    Isaksson, C., Dunham, M.H.: A comparative study of outlier detection algorithms. In: Perner, P. (ed.) MLDM 2009. LNCS, vol. 5632, pp. 440–453. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  9. 9.
    Keogh, E.: T5: data mining and machine learning in time series databases (2004)Google Scholar
  10. 10.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally adaptive dimensionality reduction for indexing large time series databases. In: Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, SIGMOD 2001, pp. 151–162. ACM, New York, NY, USA (2001). http://doi.acm.org/10.1145/375663.375680
  11. 11.
    Keogh, E., Lonardi, S., chi’ Chiu, B.Y.: Finding surprising patterns in a time series database in linear time and space. In: proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 550–556. ACM Press (2002)Google Scholar
  12. 12.
    Last, M., Kandel, A., Bunke, H.: Data Mining in Time Series Databases. World scientific, Singapore (2004) CrossRefzbMATHGoogle Scholar
  13. 13.
    Létourneau, S., Famili, F., Matwin, S.: Data mining for prediction of aircraft component replacement. Special Issue on Data Mining (1999)Google Scholar
  14. 14.
    Li, Q., Zhou, X., Lin, P., Li, S.: Anomaly detection and fault diagnosis technology of spacecraft based on telemetry-mining. In: 2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics (ISSCAA), pp. 233–236 (2010)Google Scholar
  15. 15.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD 2003, pp. 2–11. ACM, New York, NY, USA (2003). http://doi.acm.org/10.1145/882082.882086
  16. 16.
    Martínez-Heras, J.A., Donati, A., Sousa, B., Fischer, J.: Drmust-a data mining approach for anomaly investigation (2012)Google Scholar
  17. 17.
    Rebbapragada, U., Protopapas, P., Brodley, C.E., Alcock, C.: Finding anomalous periodic time series. Mach. Learn. 74(3), 281–313 (2009)CrossRefGoogle Scholar
  18. 18.
    Saleh, J., Castet, J.: Spacecraft Reliability and Multi-State Failures: A Statistical Approach. Wiley, Chichester (2011) CrossRefGoogle Scholar
  19. 19.
    Yairi, T., Kawahara, Y., Fujimaki, R., Sato, Y., Machida, K.: Telemetry-mining: a machine learning approach to anomaly detection and fault diagnosis for space systems. In: Proceedings of the 2nd IEEE International Conference on Space Mission Challenges for Information Technology, SMC-IT 2006, pp. 466–476. IEEE Computer Society, Washington, DC, USA (2006). doi: 10.1109/SMC-IT.2006.79Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.SES Engineering, Chateau de BetzdorfBetzdorfLuxembourg
  2. 2.Department of Computer Science and CommunicationUniversity of LuxembourgLuxembourgLuxembourg

Personalised recommendations