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Vol. 9 No. 2 (2019): JITA - APEIRON

Alexey Ozerov

CYBERSECURITY OF RAILWAY COMMAND AND CONTROL SYSTEMS

Original scientific paper

DOI:https://doi.org/10.7251/JIT1902053O

Abstract

With the large-scale migration to computer-based and network technology, the threat of unauthorized remote access to railway command and control systems does not appear to be something extraordinary.But external effects shall be considered alongside with internal factorsof signalling software and hardware such errors and undocumented features. Risk mitigation in terms of cybersecurity of signalling installations can onlybe achieved as a combination of means designed within some holistic approach integrating both safety and IT security aspects.

Keywords: Cybersecurity, functional safety, signalling, undocumented features, wrong-side failure.

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.