JITA

JITA Journal of Information Technology and Applications

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

Efim Rozenberg, Alexey Ozerov

Evolution of Rail Operations Control Centres

Original scientific paper

DOI:https://doi.org/10.7251/JIT2202166R

Abstract

The article gives an overview of the evolution trends and stages of rail operations control centres (ROCCs) and outlines their future development and challenges in the context of the digital transformation of railway transport. It addresses the key aspects of further evolution of ROCCs in terms of closer integration of various functional layers and systems, automation of control and supervision functionalities, application of new data processing methods based on artificial intelligence.

Keywords: Railway transport, digital transformation, Rail Operations Control Centres (ROCC), rail traffic control models, ERTMS, FRMCS, artificial intelligence, deep learning, Big Data.

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.