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DOI: 10.15862/14ECOR125 (https://doi.org/10.15862/14ECOR125)

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Goncharov A.V., Cherkassova M.A. Formation of methodological foundations for building fault-tolerant distributed control systems for technological processes with elements of artificial intelligence. Russian journal of resources, conservation and recycling. 2025; 12(1). Available at: https://resources.today/PDF/14ECOR125.pdf (in Russian). DOI: 10.15862/14ECOR125

Formation of methodological foundations for building fault-tolerant distributed control systems for technological processes with elements of artificial intelligence

Goncharov Andrey Vitalievich
Moscow State University of Technology and Management
named after K.G. Razumovsky (First Cossack University), Moscow, Russia
E-mail: a.goncharov@mgutm.ru

Cherkassova Maria Anatolyevna
Moscow State University of Technology and Management
named after K.G. Razumovsky (First Cossack University), Moscow, Russia
E-mail: marusya.7908@mail.ru

Abstract. The study is devoted to the analysis of methodological approaches to building fault-tolerant distributed process control systems (DPCS) using elements of artificial intelligence (AI). The paper presents a retrospective analysis of the evolution of fault-tolerance concepts, tracing the path from the basic principles of redundancy to modern adaptive architectures based on predictive analytics. The key principles and methods for ensuring fault tolerance are systematized, including multi-level redundancy, functional decomposition, proactive reconfiguration and intelligent monitoring. Particular attention is paid to the analysis of the application of AI methods for solving problems of early anomaly detection, fault diagnostics, failure prediction and recovery planning. Promising architectures for integrating AI components into the fault-tolerance control loop of DPCS are considered. A comparative analysis of modern methodologies for building intelligent fault-tolerant systems is carried out, their features, advantages and limitations are identified. Practical recommendations are offered for the selection and adaptation of the methodology, taking into account the specifics of the subject area and system reliability requirements. The results of the conducted study convincingly demonstrate that the effective use of artificial intelligence elements allows for a qualitative increase in the stability of distributed process control systems to various types of failures and anomalies. However, the realization of the potential of intelligent technologies requires a comprehensive methodological approach that takes into account the structural and functional features of a specific system, existing resource constraints and potential risks associated with the integration of new technologies into critical production systems.

Keywords: distributed control systems; fault tolerance; artificial intelligence; intelligent data analysis; machine learning; proactive control; multi-agent systems; adaptive architectures; functional safety; reliability

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