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Vol. 5 No. 1 (2015): JITA - APEIRON

Boris A. Lievin, Boris L. Nedorchuk

Prospects of High Technologies in the Remote Diagnosis of the Track

Original scientific paper

DOI: https://doi.org/10.7251/JIT1501065L

Abstract

The article assesses trends of development of devices for control and diagnostics of railway tracks, highlights the growing importance of advanced technologies that make use of more sophisticated methods of remote monitoring of the technical condition and ensure safe operation of railroad bed. In particular, the authors analyze in detail the results of their own developments, with an emphasis on options for optical control with the use of aircraft and video recording, significantly expanding the possibilities of monitoring and quality of observations, and at the same time forecast (considering experimental data and economic factors) promising areas of engineering research.

Keywords: railway, track monitoring, remote diagnostics, aerial photography, optical sensors, polarization of refl ected light, infrared technology, satellite communications.

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.