Theoretical and Methodological Principles of Implementing Intelligent Technologies Into the Technical Service System of Wheeled vehIcles

Abstract

The article presents a comprehensive study of the theoretical and methodological foundations of the implementation of intelligent technologies in the technical service system of wheeled vehicles in the context of the transformation of the industry and the growth of the role of transport as a critical infrastructure. The evolution of approaches to technical maintenance is shown - from reactive repairs and planned and warned regulations to condition-based maintenance and predictive service (Predictive Maintenance, PdM), which is based on the analysis of large data sets, sensor monitoring and artificial intelligence algorithms. It is substantiated that the complexity of the design of modern vehicles, the spread of electric vehicles and connected wheeled vehicles require a rethinking of traditional MOT models based on a nonlinear system methodology developed by the domestic scientific school. Modern foreign and Ukrainian research in the field of predictive maintenance, digital twins, IoT architectures, Explainable AI and legal regulation of artificial intelligence is analyzed. It is shown that fundamental developments in field and wave approaches to the interactions of transport enterprises create a conceptual basis for the intellectualization of technical service, while international engineering developments form a set of technological tools (ML/DL models, digital twins, edge computing). A scientific gap is identified between theoretical models and applied solutions for Ukrainian service infrastructure, taking into account the challenges of martial law, the limitations of sensor and digital infrastructure, as well as the implementation of the requirements of the EU AI Act. A conceptual model of integrating intelligent technologies into the technical service system is proposed, which includes subsystems of data collection and processing, analytics based on ML/DL, decision-making, integration with service processes and feedback with continuous model training. Methodological principles for building intelligent service systems are formulated, taking into account the requirements of cybersecurity, transparency and explainability of AI solutions. It has been proven that the implementation of the proposed model can reduce downtime and repair costs, increase traffic safety, extend the life cycle of vehicles, and develop the high-tech service market in Ukraine.