Abstract
In this paper a novel one-class classification approach (called OSA) is proposed. The algorithm is particularly suitable for fault detection in complex technological systems, such as aircraft. This study is based on the capability of one-class support vector machine (SVM) method to classify correctly the observation and measurement data, obtained during the exploitation of the system such as airborne aircraft into a single class of ‘normal’ behavior and, respectively, leave data that is not assigned to this class as suspected anomalies. In order to ensure real time (in flight) application a recursive learning procedure of the method is proposed. The proposed method takes into account both “positive”/“normal” and “negative”/“abnormal” examples of the base class, keeping the overall model structure as an outlier-detection approach. This approach is generic for any fault detection problem (for example in areas such as process control, computer networks, analysis of data from interrogations, etc.). The advantages of the new algorithm based on OSA are verified by comparison with several classifiers, including the traditional one-class SVM. The proposed approach is tested for fault detection problem using real flight data from a large number of aircraft of different make (USA, Western European as well as Russian).
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References
Vapnik, V., Golovich, S.E., Smola, A.: Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing (1997)
Angelov, P.: Anomalous System State Identification. GB1208542.9, priority date (May 15, 2012)
Koppel, M., Schler, J.: Authorship Verification as a One-Class Classification Problem. Dept. of Computer Science Bar-Ilan University Ramat-Gan, Israel
Gretton, A., Desobry, F.: On-line one-class support vector machines an application to signal segmentation. In: Proc. IEEE ICASSP, Hong Kong (2003)
Gâlmeanu, H., Andonie, R.: Implementation Issues of an Incremental and Decremental SVM. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 325–335. Springer, Heidelberg (2008)
Das, S., Oza, N.C.: Sparse Solutions for Single Class SVMs: A Bi-Criterion Approach. In: Proc. SDM, SIAM, pp. 816–827. Omnipress (2011)
Jensen, S.: An Introduction to Lagrange Multipliers, http://www.slimy.com/~steuard/teaching/tutorials/Lagrange.html
Gershwin, S.B.: KKT — Examples. MIT Open Course Ware (2010)
Tax, D.M.J., Laskov, P.: Online SVM learning: from classification to data description and back. Journal of Machine Learning Research 7, 1909–1936 (2006)
Hill, S.I., Doucet, A.: Adapting Two-Class Support Vector Classification Methods to Many Class Problems. In: Proc. of the 22nd Intern. Conf. on Machine Learning, ICML 2005, pp. 313–320 (2005)
Scholkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high dimensional distribution. Neural Computation 13(7), 1443–1471 (2001)
Angelov, P.: Autonomous Learning Systems: From Data Streams to Knowledge in Real time. Willey (December 2012) ISBN: 978-1-1199-5152-0
Bishop, C.: Machine Learning and Pattern Classification, 2nd edn. Springer (2009)
Stibor, T., Timmis, J., Eckert, C.: A Comparative study of real-valued negative selection to statistical anomaly detection techniques. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds.) ICARIS 2005. LNCS, vol. 3627, pp. 262–275. Springer, Heidelberg (2005)
TU-204-100 Aircraft Operations Manual. Tupolev ANTK (1998)
Analysis Ground Station (AGS), Sagem/SAFRAN, France, http://www.sagem-ds.com/spip.php?rubrique230 (accessed November 8, 2012)
Kolev, D., Zvikhachevskiy, D., Angelov, P.: Safety (and maintenance) improvement Through automated flight data analysis, Scale Focused Research Project for project SVETLANA, Grant Agreement: ACPO-GA-2010-265940 (March 19, 2012)
Angelov, P., Zhou, X.: Evolving Fuzzy-Rule-based Classifiers from Data Streams. IEEE Trans. on Fuzzy Systems 16(6), 1462–1475 (2008)
Brefeld, U., Geibel, P., Wysotzki, F.: Support vector machines with example dependent costs. In: Proceedings of the European Conference on Machine Learning (2003)
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Suvorov, M., Ivliev, S., Markarian, G., Kolev, D., Zvikhachevskiy, D., Angelov, P. (2013). OSA: One-Class Recursive SVM Algorithm with Negative Samples for Fault Detection. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_25
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DOI: https://doi.org/10.1007/978-3-642-40728-4_25
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