DETECTION OF ANOMALIES IN THE INFO-COMMUNICATION SYSTEM USING THE INFORMATIVE SEQUENCE METHOD

Authors

  • О. V. Shefer Electronics and Telecommunications of National University “Yuri Kondratyuk Poltava Polytechnic”
  • V. M. Halai Electronics and Telecommunications of National University “Yuri Kondratyuk Poltava Polytechnic”
  • V. O. Shefer National University “Yuri Kondratyuk Poltava Polytechnic”
  • O. V. Mykhailenko National University “Yuri Kondratyuk Poltava Polytechnic”

DOI:

https://doi.org/10.18372/1990-5548.65.14988

Keywords:

Identification of parameters, anomaly, failure, uncertainty, quality of damage detection, generalized plausibility ratio, informative sequence method, time interval, reduced order

Abstract

The article deals with the method of detection and determination of anomalies or damages in info communication system. As a part of development of the mentioned method, the mathematical apparatus of assuring the high property of system’s robustness was taken into consideration. One of the approaches to increase the robustness of the damage detection system, offered in the article, is to minimize the sum of the indices of the generalized probability in the time interval that is being used in relation to the unknown parameters. To reduce real-time computing costs, it is proposed to minimize the functionality at certain intervals. Under the initial conditions of the minimization procedure, it is recommended to accept the data obtained from the previous implementation of this procedure. To simplify the damage detection procedure, it is necessary to analyze the decomposition coefficients simultaneously with the generation of generalized plausibility indices, which is due to a more progressive change of these coefficients in the normal mode of operation than in the abnormal mode, as well as noise level.

Author Biographies

О. V. Shefer, Electronics and Telecommunications of National University “Yuri Kondratyuk Poltava Polytechnic”

Chief of Department of Automation

Doctor of Science (Engineering). Associate Professor

V. M. Halai, Electronics and Telecommunications of National University “Yuri Kondratyuk Poltava Polytechnic”

Department of Automation

Candidate of Science (Engineering). Associate Professor

V. O. Shefer, National University “Yuri Kondratyuk Poltava Polytechnic”

Student

O. V. Mykhailenko, National University “Yuri Kondratyuk Poltava Polytechnic”

Post-graduate student

References

P. V. ZHuk, O. L. ZHuk, and O. V. Severіnov, “Estimation of exchange intensity and volumes of information flows in automated control systems of special purpose,” Information processing systems, no. 9(107), pp. 169–176, 2012. (in Ukrainian) http://www.hups.mil.gov.ua/periodic-app/article/10229

S. Panchenko, S. Prykhodko, S. Kozelkov, M. Shtompel, V. Kosenko, O. Shefer, and O. Dunaievska, “Analysis of efficiency of the bioinspired method for decoding algebraic convolutional codes,” Eastern-European Journal of Enterprise Technologies, 2/4(98), 2019. https://doi.org/10.15587/1729-4061.2019.160753

V. I. Dzhigan, Adaptive signal filtering: theory and algorithms. Moscow: Tekhnosfera, 2013, 528 p. (in Russian)

N. Benvenuto and G. Cherubini, Algorithms for communication systems and their applications. NJ, Hoboken: John Wiley and Sons, Inc., 2002, 1285 p. https://doi.org/10.1002/0470855509

G. Olsson and D. Piani, Digital automation and control systems. St. Petersburg: Nevskij dialekt, 2001, 558 p. (in Russian)

Adaptive filters, edited by K. F. N. Kouena and P. M. Granta, Moscow: Mir, 1988, 392 p. (in Russian)

V. V. Emelyanov, V. V. Kurejchik, and V. M. Kurejchik, Theory and practice of evolutionary modeling, Moscow: Fizmatlit, 2003, 432 p. (in Ukrainian)

Synthesis, analysis and diagnostics of electronic circuits: International collection of scientific papers. Issue 10, edited by V. V. Filaretova. Ulyanovsk: UlGTU, 2012, 280 p. (in Russian)

V. M. Halai, L. Yu. Spinul, and A. V. Shefer, Object dynamics control. Kyiv: Ekonomika i pravo, 2004, 185 p. (in Russian)

P. S. R. Diniz, Adaptive filtering algorithms and practical implementation. 3nd ed. New York, Springer Science + Business Media, 2008, 627 p.

E. Ajficher and B. Dzhervis, Digital signal processing. A hands-on approach Moscow: ID "Vil'yams", 2004, 992 p. (in Russian)

T. Ogunfunmi, Adaptive nonlinear system identification: The Volterra and Wiener model approaches. Springer Science + Business Media, LCC. 2007, 230 p. ISBN-13:978-0387263281, ISBN-10:0817641351

R. G. Brown, Introduction to random signal analysis and Kalman filtering. New York: Wiley & Sons, 1983, 347 p. ISBN-10:0471087327

R. E. Kalman and R. S. Bucy, “New results in linear filtering and theory of prediction,” Trans. ASME, J. of Basic Eng., vol. 83-D, pp. 95–108, 1961. https://doi.org/10.1115/1.3658902

M. Atans and P. Falb, Optimal control, Moscow: Mashinostroenie, 1968, 764 p. (in Russian)

S. Tanaka and P. C. Muller, “Fault detection in linear discrete systems by a pattern recognition of a generalized likelihood-ratio,” Trans. ASME, Dynamic Systems, Measurement and Control, vol. 112, no. 3, pp. 276–282, 1990. https://doi.org/10.1115/1.2896136

A. I. Orlov, Applied statistics, Moscow: Ekzamen, 2004, 656 p. (in Russian)

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AUTOMATION AND COMPUTER-INTEGRATED TECHNOLOGIES