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

Baranov Leonid Avramovich, Sidorenko Valentina Gennadievna, Balakina Ekaterina Petrovna, Loginova Lyudmila Nikolaevna

Methods and Principles of Construction of Intelligent Unmanned Systems for Train Control of Urban Off-Street Transport

Original scientific paper

DOI:https://doi.org/10.7251/JIT22020100A

Abstract

The principles of constructing intelligent unmanned traffic control systems for off-street urban rail transport are considered, while a block diagram and connections between subsystems are proposed. The features of the construction of upperlevel control algorithms are shown. Functional features of subsystems are defined, and links between subsystems are considered.

Keywords: Urban Rail Transport System, Traffic Control, Control Algorithm, Unmanned Control, Intelligent Systems.

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.