JITA

JITA Journal of Information Technology and Applications

Vol. 8 No. 1 (2018): JITA - APEIRON

Petr Filimonovich Filimonovich

Evaluation of the Period of Sensors Motion Parameters of the Train

Original scientific paper

DOI:https://doi.org/ 10.7251/JIT1801014B

Abstract

The choice of polling period sensors for measuring the speed and acceleration of the train can be produced using the spectral representation of functions of velocity and acceleration from time to time. Using the spectral method of determining the period of the survey, you can choose different value depending on the category of the train and its dynamic characteristics. For high-speed trains, the period of sensors is less than for trucks, since the latter are tightened by the transients during acceleration and deceleration.

Keywords: Hartley transform, parameters of the train, period of sensors, spectral method.

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.