Objective: one of the key areas of the economy’s digital transformation is the deployment of technologies built around digital twins. Within the context, the present study explores an application of this technology to analyze the time a passenger railcar remains in an out-of-service fleet for scheduled maintenance. The duration of this period affects the total amount of unproductive rolling stock downtime and, consequently, the operational efficiency of the passenger service segment. The use of adaptive, self-configuring technologies together with mathematical representations of the time components that shape the process of withdrawing a passenger railcar from service to perform large-scale scheduled maintenance, including overhauls and depot-level repairs. This makes it possible to examine the repair-related interruption not as a single event, but as a sequence of interrelated stages. The paper considers the full period of removal from operational service. It starts when a passenger railcar is officially recorded in the information system as transferred to the inactive fleet and physically uncoupled from the train. It ends when, after completion of the scheduled repair, the railcar is returned to service and attached to the train. An additional task of the research is to establish the probability-based distribution patterns that characterize each component of this process. Identifying these regularities creates a basis for more grounded decisions concerning the planned use of repair resources at the enterprise. It also supports the calculation of the required size of the operating passenger-car fleet in relation to the daily timetable of the train-formation station, while improving the planning of repair-facility capacity for major scheduled work. The study presents a case study of how a digital twin can be employed to model and analyze the process of sending a passenger railcar for scheduled repair. Method: probability-based and statistical analysis. Results: the proposed method enables real-time planning and adjustment operational performance indicators for the effective use of the railcar fleet as statistical information accumulates, taking into account the potential force majeure events; it also allows modelling of stochastic departures from prescribed time norms in cases where such norms are established. Practical significance: from a practical standpoint, the study offers benefits for entities within the railcar maintenance sector. Additionally, the insights derived are highly relevant for a broad spectrum of stakeholders, such as rolling stock owners, freight and passenger carriers, and operators managing non-traction fleets.
Ivanov et al. (Tue,) studied this question.
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