JITA

JITA Journal of Information Technology and Applications

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

Vasilyeva Marina Alekseevna, Filipchenko Konstantin Mikhailovich

Application of Tree Data Structures for Systems Modeling

Original scientific paper

DOI:https://doi.org/10.7251/JIT2202152A

Abstract

The authors developed a binary tree search library. The article presents UML diagrams of the developed classes. The authors supposed Abstract factory design pattern for the opportunity of using search trees’ node classes inheritance. The unit tests one developed. This library can be used in the creating the scheduling technical maintenance calculation automated system and the metro train energy optimal trajectory calculation system.

Keywords: Tree structure, interval tree, transport system modeling.

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.