Портал психологических изданий PsyJournals.ru
Каталог изданий 100Рубрики 51Авторы 8582Ключевые слова 21029 Online-сборники 1 АвторамRSS RSS

РИНЦ

0,214 — двухлетний импакт-фактор

Моделирование и анализ данных

Издатель: Московский государственный психолого-педагогический университет

ISSN (печатная версия): 2219-3758

ISSN (online): 2311-9454

DOI: https://doi.org/10.17759/mda

Лицензия: CC BY-NC 4.0

Издается с 2011 года

Периодичность: 4 номера в год

Язык журнала: русский

Доступ к электронным архивам: открытый

 

Synthesis and identification of hidden Markov models based on a novel statistical technique in condition monitoring 1009

Куравский Л.С., доктор технических наук, декан факультета информационных технологий, Московский государственный психолого-педагогический университет, Москва, Россия, l.s.kuravsky@gmail.com
Баранов С.Н., доктор физико-математических наук, генеральный директор компании ООО «Русское авиационное общество», Москва, Россия, rusavia@rusavia.com
Юрьев Г.А., кандидат физико-математических наук, зам. декана, доцент, факультет информационных технологий, ФГБОУ ВО МГППУ, Москва, Россия, g.a.yuryev@gmail.com

Аннотация

Under consideration are technologies for synthesis and identification of trained hidden Markov models based on a novel statistical technique and applied in condition monitoring. The approach can be used for both con-tinuous-and discrete-time models of technical and non-technical systems. An initial rough model results from statistical multivariate analysis of observed data or their analysis by means of Kohonen self-organizing feature maps. Then it suffers proper corrections. Histograms of observed frequencies of being in different system states after the given exploitation periods are employed to train the constructed models. Free network parameters are identified by the chi-square minimum method.

Ключевые слова: Markov networks, Markov chains, Hidden Markov models, model synthesis, model identification, condition monitoring

Рубрика: Научная жизнь

Тип: научная статья

Ссылка для цитирования

Фрагмент статьи

Acoustic loads are the most dangerous for thin-walled aircraft structures. These loads are wide-band (up to 5000 Hz) random process, with level varying from 145 dB to 170 dB in different points of aircraft surface.

In practice, destructiveness of these structures can be estimated by the changes of distributed structure stiffness, which is, in its turn, recognized by the qualitative changes of normalized spectral structure characteristics measured in checkpoints.

Литература
  1. Baranov S.N. and Kuravsky L.S., “Acoustic vibrations: modeling, optimization and diagnos­tics”, 2nd Edition, enlarged, Moscow: RUSAVIA, 224 pp, 2006.
  2. Baum L.E., Petrie T., Soules G., and Weiss N., “A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains”, Ann. Math. Statist., Vol 41, No 1, pp 164–171, 1970.
  3. Bendat J.S. and Piersol A.G., “Random data. Analysis and measurement procedures”, New York: John Wiley & Sons, 1986.
  4. Bishop Y.M.M., Fienberg S.E., and Holland P.W., “Discrete multivariate analysis: Theory and practice”, Cambridge, MA: M. I. T. Press, 1975.
  5. Bogdanoff J.L. and Kozin F., “Probabilistic Models of Cumulative Damage”, New York: John Wiley & Sons, 1985.
  6. Brousset C. and Baudrillard G., “Neural network for automating diagnosis in aircraft inspec­tion”, Review of Progress in Quantitative Nondestructive Evaluation (Ed. by D.O. Thompson and D.E. Chimenti), Plenum Press, New York, Vol 12, pp 797–802, 1993.
  7. Cramer H., “Mathematical methods of statistics”, Princeton: Princeton University Press, 1946.
  8. Kohonen T., “Self-organizing maps”, Heidelberg: Springer Verlag, 1995.
  9. Kuravsky L.S. and Baranov S.N., “Application of neural networks for diagnostics and forecast­ing of fatigue failures of thin-walled structures”, Neurocomputers: Design and Applications, No 12, pp 47–63, 2001 (in Russian).
  10. Kuravsky L.S. and Baranov S.N., “Condition monitoring of the structures suffered acoustic fatigue failure and forecasting their service life”, Proc. Condition Monitoring 2003, Oxford, United Kingdom, pp 256-279, July 2003.
  11. Kuravsky L.S. and Baranov S.N., “Discriminant networks to solve diagnostics problems”, Neurocomputers: Design and Applications, No 8-9, pp 39, 2003 (in Russian).
  12. Kuravsky L.S. and Baranov S.N., “Neural networks in fatigue damage recognition: diagnostics and statistical analysis”, Proc. 11th International Congress on Sound and Vibration, St.­Petersburg, Russia, pp 29292944, July 2004.
  13. Kuravsky L.S. and Baranov S.N., “Synthesis of Markov networks for forecasting fatigue fail­ures”, Proc. Condition Monitoring 2003, Oxford, United Kingdom, pp 7691, July 2003.
  14. Kuravsky L.S. and Baranov S.N., “The concept of multifactor Markov networks and its appli­cation to forecasting and diagnostics of technical systems”, Proc. Condition Monitoring 2005, Cambridge, United Kingdom, pp 111117, July 2005.
  15. Kuravsky L.S., Baranov S.N., and Malykh S.B., “Neural networks to solve the problems of forecasting, diagnostics and data analysis”, Moscow: RUSAVIA, 2003 (in Russian).
  16. Lawley D.N. and Maxwell A.E., “Factor analysis as a statistical method”, London: Butter­worths, 1963.
  17. Marple S.L., Jr., “Digital spectral analysis with applications”, New Jersey: Prentice-Hall, 1987.
  18. Pidaparti R.M.V. and Palakal M.J., “Neural network approach to fatigue-crack-growth predic­tions under aircraft spectrum loadings”, Journal of Aircraft, Vol 32, pp 825–831, 1995.
  19. Rabiner L.R., “A tutorial on hidden Markov models and selected applications in speech recog­nition”, Proc. IEEE, Vol 77, No 2, pp 257–286, 1989.
  20. Viterbi A.J., “Error bounds for convolutional codes and an asymptotically optimum decoding algorithm”, IEEE Transactions on Information Theory, Vol 13, No 2, pp.260–269, 1967.
 
О проекте PsyJournals.ru

© 2007–2020 Портал психологических изданий PsyJournals.ru  Все права защищены

Свидетельство регистрации СМИ Эл № ФС77-66447 от 14 июля 2016 г.

Издатель: ФГБОУ ВО МГППУ

Creative Commons License

Яндекс.Метрика