A Research of MM-SVM Technique for Fault Diagnosis

Abstract:

With the satellite development of our country, higher accuracy and stability are requires, which makes the control systems becoming more complex and requiring more telemetry parameters. Data mining techniques do not consider the physical relationship between the various components, but use of satellite telemetry parameters of the satellite states the purpose of fault identification. In this paper, we give a model based on multiple support vector machines (MM-SVM) technology satellite fault diagnosis method. The experiment shows that our method is effective in satellite equipment fault diagnosis

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 2633-2637
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.2633
• Online since: May 2014
• Authors: Hong Yin*, Shu Qiang Yang, Guo Ming Li, Ping Yin, Song Chang Jin
• Keywords: Data Mining (DM), Fault Diagnosis, Support Vector Machine (SVM)

References

[1] Vapnik V. The nature of statistical learning theory[M]. springer, (2000).

[2] Mao Y, Zhou X, Pi D, et al. Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection[J]. Journal of Biomedicine and Biotechnology, 2005, 2005(2): 160-171.

DOI: 10.1155/jbb.2005.160

[3] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural processing letters, 1999, 9(3): 293-300.

[4] Monroy I, Benitez R, Escudero G, et al. A semi-supervised approach to fault diagnosis for chemical processes[J]. Computers & Chemical Engineering, 2010, 34(5): 631-642.

DOI: 10.1016/j.compchemeng.2009.12.008

[5] Freund Y, Schapire R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of computer and system sciences, 1997, 55(1): 119-139.

DOI: 10.1006/jcss.1997.1504

[6] KLEMA J, NOVAKOVA L, KAREL F, STEPANKOVA O. Sequential data mining: A comparative case study in development of atherosclerosis risk factors, IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., 2008, vol. 38, no. 1: 3-15.

DOI: 10.1109/tsmcc.2007.906055

[7] BUDALAKOTI S, SRIVASTAVA A, AKELLA R. Discovering atypical flights in sequences of discrete flight parameters, in Proc. 2006, IEEE Aerospace. Conf., pp: 1-8.

DOI: 10.1109/aero.2006.1656109

[8] FINK E, PRATT K. B, GANDHI H.S. Indexing of Time Series by Major Minima and Maxima. Proc of the IEEE Int Conf on Systems, Man, and Cybernetics. Washington. DC: IEEE, 2003: 2332-2335.

DOI: 10.1109/icsmc.2003.1244232

[9] DAVID L. Inductive System Health Monitoring. Proceedings of the International Conference on Artificial Intelligence, IC-AI 04, Volume 2 & Proceedings of the International Conference on Machine Learning; Models, Technologies & Applications, MLMTA , 04, June 21-24, 2004, Las Vegas, Nevada, USA.

[10] Berndt Donald J, Clifford James. Using dynamic time warping to find patterns in time series[C]. In Proceedings of the KDD Workshop, Seattle, WA. 1994: 359-370.

[11] SCHWABACHER M. Machine Learning for Rocket Propulsion Health Monitoring. Proceedings of the SAE World Aerospace Congress, Dallas, TX, (2005).

[12] DAS K, SCHNEIDER J. Detecting anomalous records in categorical datasets. In KDD'07: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007, pp: 220-229.

DOI: 10.1145/1281192.1281219

[13] DAVID L. I. Data Mining Applications for Space Mission Operations System Health Monitoring, NASA Ames Research Center, Moffett Field, California, 94035, (2008).

[14] PARK H, MACKEY R, JAMES M, ZAK M, KYNARD M, SEBGHATI J, and GREENE W. Analysis of Space Shuttle Main Engine Data Using Beacon-based Exception Analysis for Multi- Missions. Proceedings of the IEEE Aerospace Conference, IEEE, New York, Vol. 6, March 9-16, 2002: 6-2835 - 6-2844.

DOI: 10.1109/aero.2002.1036123

[15] CHANDOLA V, BANERJEE A, KUMAR V. Anomaly detection: A survey. ACM Computing Surveys, 2009, 41(3): 1-58.

DOI: 10.1145/1541880.1541882