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Vol. 12 No. 2 (2022): JITA - APEIRON

Efim Rozenberg, Alexey Ozerov, Zoran Avramovich

Rail Operations Control Centres

Original scientific paper

DOI:https://doi.org/10.7251/JIT2202069R

Abstract

The article presents an overview of common trends in the evolution of rail operations control as well as the factors stipulating the existing approaches to the design of Rail Operations Control Centres (ROCCs) around the world. Based on the comparative analysis of various ROCCs and traffic parameters, the authors propose some classification of global traffic control models. The article outlines further steps towards a more detailed analysis of ROCCs in terms of their effectiveness by introducing a number of additional criteria and performance indicators to be taken into account.

Keywords: Railway transport, Rail Operations Control Centres (ROCC), rail traffic control models, effectiveness, UIC.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.