JITA

JITA Journal of Information Technology and Applications

Vol. 12 No. 1 (2022): JITA - APEIRON

Efim Rozenberg, Alexey Ozerov

Train command and control for commuter and urban lines

Original scientific paper

Abstract

The paper presents the state of the art of command and control and the challenges faced by the Russian Railways (RZD), with a focus on the migration to new paradigms of train separation, train localization and obstacle detection. The authors give an overview of the practical results of some ongoing projects carried out with the direct involvement of NIIAS researchers and developers for the Moscow Central Circle (MCC) railway.

Keywords: RZD; Virtual block; Moving block; Hybrid system; ATO; GOA3/4; Driver assistance system (DAS); Self-driving (autonomous) trains; Artifical Neural Network (ANN); GNSS; Digital route map.

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.