JITA

JITA Journal of Information Technology and Applications

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

Baranov Leonid Avramovich, Safronov Anton Igorevich, Sidorenko Valentina Gennadievna

Stages of Development, Methods and Intellectualization of Automated Scheduling of Metro Passenger Trains

Original scientific paper

DOI:https://doi.org/10.7251/JIT2202077A

Abstract

The article examines the underground passenger trains planned schedule automated construction system development stages. The technical means, basic methods and information technologies applied at system development stages aimed at its intellectualization, the ways of its integration into the Unified vehicles control automated traffic intelligent system on urban rail transport systems are described.

Keywords: transportation scheduling, automation, autodriver, speech recognition, machine learning, artificial intelligence, uniformity, information technologies, intelligent transport systems, unified intelligent automated vehicle traffic control system.

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.