kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2025-22-3-85-97kaz29-2104Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEРАЗРАБОТКА МУЛЬТИАГЕНТНОЙ СИСТЕМЫ ДЛЯ ИНТЕЛЛЕКТУАЛЬНОЙ ДИАГНОСТИКИ ПРОМЫШЛЕННОГО ОБОРУДОВАНИЯDEVELOPMENT OF A MULTI-AGENT SYSTEM FOR INTELLIGENT DIAGNOSTICS OF INDUSTRIAL EQUIPMENThttps://orcid.org/0000-0003-1798-9161СамигулинаГ. А.SamigulinaG. A.

д.т.н., профессор

г. Алматы 

Dr.Tech.Sc., Professor

Almaty

kz.galinasamigulina@gmail.comhttps://orcid.org/0000-0002-5862-6415СамигулинаЗ. И.SamigulinaZ. I.

PhD, доцент

г. Алматы 

PhD, Associate Professor

Almaty

z.samigulina@kbtu.kzhttps://orcid.org/0009-0002-8168-6368БекешевД. Д.BekeshevD. D.

магистр

г. Алматы 

Master's degree

Almaty

dauletb01@gmail.comИнститут информационных и вычислительных технологий, лаб. «Интеллектуальные системы управления и прогнозирования»; Казахстанско-Британский технический университетКазахстанInstitute of Information and Computing Technologies, Lab. «Intellectual Control system and forecasting»; Kazakh-British Technical UniversityKazakhstan2025270920252238597Copyright © Самигулина Г.А., Самигулина З.И., Бекешев Д.Д., 20252025Самигулина Г.А., Самигулина З.И., Бекешев Д.Д.Samigulina G.A., Samigulina Z.I., Bekeshev D.D.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/2104

Современные системы промышленной автоматизации в процессе эксплуатации генерируют большой объем производственных данных, обработка которых современными методами искусственного интеллекта позволяет своевременно осуществлять диагностику состояния и прогнозировать износ дорогостоящего оборудования. В статье разработана инновационная мультиагентная система на основе нейроиммунноэндокринного взаимодействия для диагностики промышленного оборудования. Система состоит из агентов, специализирующихся на редукции данных, на основе искусственной нейронной сети (ИНС), искусственного эндокринного алгоритма (ИЭА) и искусственной иммунной системы (ИИС). Задача данных агентов снизить размерность базы данных, при этом не потеряв ее информативность. Также разработаны предиктивные агенты на основе ИИС и ИЭА, которые решают задачу классификации состояния оборудования на основе информации, полученной после редукции данных и осуществляют прогноз износа оборудования. Эксперименты осуществлены на реальных промышленных данных нефтеперерабатывающего предприятия ТОО «ТенгизШевройл». Результаты моделирования показали перспективность использования данного подхода. Получены следующие значения метрики AUC (Area Under ROC Curve) от 0.86 до 0.90, пропускная способность мультиагентной системы составляет 1000 задач в секунду, время предсказания – 1 мс, отказоустойчивость – 100%.

Modern industrial automation systems generate a large volume of production data during operation, the processing of which by modern artificial intelligence methods allows for timely diagnostics of the condition and prediction of wear of expensive equipment. The article develops an innovative multi-agent system based on neuroimmune-endocrine interaction for diagnostics of industrial equipment. The system consists of agents specializing in data reduction, based on an artificial neural network (ANN), an artificial endocrine algorithm (AEA) and an artificial immune system (AIS). The task of these agents is to reduce the size of the database without losing its information content. Also, predictive agents based on AIS and AEA have been developed, which solve the problem of classifying the condition of equipment based on information obtained after data reduction and predict equipment wear. Experiments were carried out on real industrial data of the oil refinery TengizChevroil LLC. The modeling results showed the prospects of using this approach. The following values of the AUC (Area Under ROC Curve) metric were obtained from 0.86 to 0.90, the throughput of the multi-agent system is 1,000 tasks per second, the prediction time is 1 ms, and the fault tolerance is 100%.

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