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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


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.

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