gumrfВестник Государственного университета морского и речного флота имени адмирала С. О. МакароваVestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S. O. Makarova2309-51802500-0551ФГБОУ ВО «Государственный университет морского и речного флота имени адмирала С.О. Макарова»10.21821/2309-5180-2026-18-4-755-771BUHOAIgumrf-787Research ArticleАВТОМАТИЗАЦИЯ И УПРАВЛЕНИЕ ТЕХНОЛОГИЧЕСКИМИ ПРОЦЕССАМИ И ПРОИЗВОДСТВАМИAUTOMATION AND CONTROL OF TECHNOLOGICAL PROCESSES AND PRODUCTIONSИнтеллектуальная система поддержки принятия решений для главных судовых дизельных двигателей на основе нечеткой логикиIntelligent Fuzzy Inference-Based Decision-Support System for Marine Main Diesel EnginesПолковниковаН. А.PolkovnikovaN. A.

Полковникова Наталья Анатольевна — кандидат технических наук, доцент

353918, Краснодарский край, г. Новороссийск, пр. Ленина, 93

Polkovnikova, Natalia A. — Candidate of Technical Sciences, Associate Professor

93, Lenin’s avenue, Novorossiysk, Krasnodar Krai, 353918

natalia-polkovnikova@mail.ruПолковниковА. К.PolkovnikovA. K.

Полковников Анатолий Карпович — кандидат технических наук, доцент

353918, Краснодарский край, г. Новороссийск, пр. Ленина, 93

Polkovnikov, Anatoly K. — Candidate of Technical Sciences, Associate Professor

93, Lenin’s avenue, Novorossiysk, Krasnodar Krai, 353918

polkov5@mail.ruФГБОУ ВО «Государственный морской университет имени адмирала Ф. Ф. Ушакова»РоссияAdmiral Ushakov Maritime State UniversityRussian Federation202620092026184755771Copyright © Полковникова Н.А., Полковников А.К., 20262026Полковникова Н.А., Полковников А.К.Polkovnikova N.A., Polkovnikov A.K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://journal.gumrf.ru/jour/article/view/787

обслуживания. Разработана архитектура интеллектуальной системы поддержки принятия решений,обеспечивающая переход от традиционного мониторинга к интеллектуальному информационно-ориентированному управлению в человеко-машинных системах. Рассмотрены база данных неисправностей и способов их устранения, а также база знаний, формализующая продукционные правила с использованием концепции отклонений. Выполнена реализация процедуры нечеткого вывода, включающей фазификацию входныхпараметров, получаемых из систем мониторинга двигателя, их обработку по методу Мамдани – Задеи дефаззификацию результатов для получения оценок состояния двигателя в реальном времени. Система обеспечивает отображение отклонений параметров, вероятных неисправностей и эксплуатационныхрекомендаций в графическом интерфейсе. Результаты исследования показывают, что предложенный подход эффективно аппроксимирует экспертное рассуждение, учитывает нелинейности и неопределенностии создает основу для повышения эффективности прогнозного технического обслуживания, оптимизациирежимов эксплуатации и повышения общей эффективности работы судовых дизельных двигателей.

The subject of the study is the development of an intelligent decision support system based on fuzzy logic for marine diesel engines, aimed at improving diagnostic accuracy and operational reliability under conditions of incomplete, uncertain, or fragmented data. The relevance of the study is determined by the increasing complexity of parameters monitored by shipboard automation systems and the stringent requirements for safe and efficient engine operation, which create significant difficulties for shipboard specialists in real-time information analysis. The study examines the application of fuzzy logic, production rules, intelligent data analysis methods, and expert knowledge bases to assess the technical condition of marine diesel engines, detect faults at the individual-cylinder level, determine permissible operating modes with due regard to technical and navigational conditions, predict gradually developing faults, and prioritize maintenance activities. The architecture of an intelligent decision support system is developed to provide a transition from conventional monitoring to intelligent information-oriented control in human–machine systems. A fault and troubleshooting database and a knowledge base formalizing production rules using the concept of deviations are considered. A fuzzy inference procedure is implemented, including fuzzification of input parameters obtained from engine monitoring systems, processing using the Mamdani–Zadeh method, and defuzzification of the results to obtain real-time assessments of engine condition. The system provides a graphical interface displaying parameter deviations, probable faults, and operational recommendations. The results show that the proposed approach effectively approximates expert reasoning, accounts for nonlinearities and uncertainties, and provides a basis for improving the efficiency of predictive maintenance, optimizing operating modes, and improving the overall operating efficiency of marine diesel engines.

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