novtexmechМехатроника, автоматизация, управлениеMekhatronika, Avtomatizatsiya, Upravlenie1684-64272619-1253Commercial Publisher «New Technologies»10.17587/mau.27.516-522novtexmech-2118Research ArticleАВТОМАТИЗАЦИЯ И УПРАВЛЕНИЕ ТЕХНОЛОГИЧЕСКИМИ ПРОЦЕССАМИ И ПРОИЗВОДСТВАМИAUTOMATION AND CONTROL TECHNOLOGICAL PROCESSESПрименение технологии искусственного интеллекта для контроля начала аварий на широко распространенных технических объектахApplication of Artifi cial Intelligence Technology for Controlling the Onset of Accidents at Common Technical ObjectsАлиевТ. А.AlievT. A.

д-р техн. наук, проф., советник НАНА

Баку

Aliev Telman A., Dr. of Tech. Sc., Professor, Advisors, National Academy of Sciences

Baku

telmancyber@gmail.comМусаеваН. Ф.MusaevaN. F.

д-р техн. наук, проф., профессор кафедры

Баку

Dr. of Tech. Sc., Professor

Baku

musanaila@gmail.comБашироваЗ. Ю.BashirovaZ. Y.

науч. сотр.

Баку

Researcher

Baku

zarina.bashirova@isi.azМаммадоваА. И.MammadovaА. I.

ассистент кафедры

Баку

Assistant Professor

Baku

anahaciyeva@gmail.comАзербайджанский университет архитектуры и строительства; Институт математики; Национальная академия наук АзербайджанаАзербайджанAzerbaijan University of Architecture and Construction; Institute of Mathematics; National Academy of SciencesAzerbaijanАзербайджанский университет архитектуры и строительства; Институт математикиАзербайджанAzerbaijan University of Architecture and Construction; Institute of MathematicsAzerbaijanИнститут математикиАзербайджанInstitute of MathematicsAzerbaijan2026101020262710516522Copyright © Commercial Publisher «New Technologies», 20262026Commercial Publisher «New Technologies»Commercial Publisher «New Technologies»https://mech.novtex.ru/jour/about/submissions#copyrightNoticehttps://mech.novtex.ru/jour/article/view/2118

Предлагается с помощью технологий искусственного интеллекта осуществить мониторинг начала скрытого периода аварий, а также контролировать начало изменения сейсмостойкости строительных объектов при маломощных землетрясениях, используя характеристики помехи как носителя диагностической информации.

Экспериментальные исследования показали, что в процессе функционирования рассматриваемых объектов возникновение скрытого периода аварий связано с тем, что при их эксплуатации в оборудованиях происходят разрушения, мелкие трещины, остаточные напряжения, усталостные повреждения, утомление, износ и стирание. В результате в этот период оценки взаимно-корреляционной функции между полезном сигналом и помехой и дисперсии помехи вибрационных сигналов представляют собой ощутимую величину. При этом нарушение таких классических условий, как нормальность закона распределения и стационарность сигналов, незначительно влияет на полученные оценки. Благодаря этому технологии искусственного интеллекта позволяют использовать оценки взаимно-корреляционной функции между полезным сигналом и помехой как надежный индикатор начала аварии. При этом для обеспечения адекватности результатов контроля шаг дискретизации необходимо определять адаптивно в реальном масштабе времени, что легко достигается путем использования частотных свойств младшего разряда отсчетов вибрационных сигналов.

It is proposed to use the estimates of these noise characteristics as a carrier of diagnostic information, utilizing artificial intelligence technology to monitor the start of the latent period of accidents. It is also shown that it is possible to employ artificial intelligence, using noise as a diagnostic carrier during low-magnitude earthquakes, to monitor the initial changes in the seismic stability of buildings.

Experimental studies have shown that during the operation of the objects under consideration, the occurrence of the latent period of accidents is due to the fact that under actual operating conditions equipment experiences destruction, fatigue cracking, residual stress, fatigue damage, fatigue, wear, and abrasion. As a result, during this period, the estimates of the cross-correlation function between the useful signal and the noise, as well as the noise variance of the vibration signals, are significant values. Furthermore, violations of classical conditions, such as the normality of the distribution law and the stationarity of the signals, have a negligible effect on the resulting estimates. Thanks to these technologies, the estimates of the cross-correlation function between the useful signal and the noise serve as reliable indicators for the onset of accidents using artificial intelligence technology. To ensure the adequacy of the control results, the sampling interval must be determined adaptively in real-time, which is easily achieved by utilizing the frequency properties of the least significant bit of the vibration signal samples.

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The authors declare that there are no conflicts of interest present.