№1, 2014

IMMUNE NETWORK BASED METOD FOR IDENTIFICATION OF TURBINE ENGINE SURGING

Bidyuk Pyotr I., Litvinenko Vladimir I., Gasanov Aydin S.

An application of the new method and combined algorithm on the basis of immune network and negative selection for identification of aviation engine surging is considered. The problem of identification of the engine surging is examined as a problem of anomaly detection. The basic drawbacks of the negative selection algorithm are examined. It is proposed to use the method based on artificial immune network for data processing of detectors set, and for a monitoring phase the scheme of classical negative selection algorithm is used. The results obtained have shown high efficiency of the proposed method and algorithm. (pp. 47-59)

Keywords: engine surging, gas turbine engine, negative selection algorithms, artificial immune network learning algorithm
References
  • Myrgorod V.F., Grudinkin V.M. The Virtual stand for modeling the systems of aviation engines //  Artificial Intelligence, 2006, №3,­ pp.186–191.
  • Gurevich O. S. A status and prospects of development of systems of automatic control for aviation gas turbine engine / CIAM 2001-2005. The Basic results of scientific and engineering activities. Moscow: CIAM, 2005, pp.267–270.
  • Designing aviation gas turbine engine: the University textbook / Ed. by Prof. A.M. Ahmedzjanova. Мoscow: Mechanical engineering, 2000, 454 p.
  • Mathematical models of aviation engines of any schemes (computer DVIG environment): the Manual / Ed. by Prof. A.M. Ahmedzjanova; UGATU–Ufa, 1998, 128 p.
  • Sosunov V. A., Lytvynov Ju. A. Unsteady operating modes of aviation engines. Мoscow: Mechanical engineering, 1975, 216 p.
  • González F. A Study of Artificial Immune Systems Applied to Anomaly Detection, Ph.D. Dissertation, The University of Memphis, May, 2003. 
  • Hawkins D. Identification of Outliers. London: Chapman and Hall, 1980.
  • Barnett V., Lewis T. Outliers in Statistical Data, 3rd ed. New York: Wiley, 1994.
  • Hampel F., Ronchetti E., Rousseuw P., Stahel W. Robust Statistics. New York: Wiley, 1986.
  • Huber P. Robust Statistics. New York: Wiley, 1981.
  • Dasgupta D., Forrest S. An anomaly detection algorithm inspired by the immune system. In: Dasgupta D. (Ed.) Artificial Immune Systems and Their Applications. New York: Springer-Verlag, 1999, pp. 262–277.
  • D’Haeseleer P., Forrest S., Helman P. An immunological approach to change detection: algorithms, analysis and implications // Proc. of the IEEE Symposium on Computer Security and Privacy, IEEE Computer Society Press, Los Alamitos, CA, 1996.
  • Fefelov A.A., Litvinenko V.I., Bidyuk P.I. Modification of negative selection algorithm on the basis of mechanisms artificial immune sets for solution of anomalies detection problems // The Collection of scientific works in five volumes of the Second International Scientific Conference on Intellectual Systems of Decision-making and Applied Aspects of Information Technologies // Ukraine, Eupatoria 2007, vol.3, pp.73–78.
  • Forrest S., Perelson A.S., Allen L., Cherukuri R.. Self-nonself discrimination in a computer // Proc. of the IEEE Symposium on Research in Security and Privacy, 1994, pp.202–212.
  • De Castro L. N., Von Zuben F. J. “Artificial Immune Systems: Part I–Basic Theory and Applications”, Technical Report–RT DCA 01/99, FEEC/UNICAMP, Brazil, 1999, 95p.
  • Dasgupta D. Advances in Artificial Immune Systems // IEEE Computational Intelligence Magazine. November, 2006.
  • Jerne N. K. Towards a network theory of the immune system // Immunology. (Inst. Pasteur), 1974, vol.125C, pp.373–389.