JITA

JITA Journal of Information Technology and Applications

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

Iz. B. Peshkov, M. Yu. Shuvalov, V. L. Ovsienko

Water treeing in extruded cable insulation as Rehbinder electrical effect

Original scientific paper

DOI: https://doi.org/10.7251/JIT1501055P

Abstract

The paper contains systematic comparison of signs and properties of the water treeing phenomenon (the basic mechanism of degradation of medium voltage electric cable extrudered insulation which develops under combined action of electric stress and water) and Rehbinder Effect – the reduction of mechanical strength of solids due to physical and chemical action of liquid medium.
The analysis of the published data permits to distinguish 13 indications of the Rehbinder Effect. The authors show successively the direct analogy of the water treeing and the Rehbinder Effect using the above mentioned indications, including decrease of work of the development of new surfaces in the course of destruction, chemical specificity, role of material defects, two-stage destruction nature, etc. The analogy obtained is accepted as a working hypothesis which permits to bring certain order into theoretical and experimental studies of the water treeing.

Keywords: water treeing, Rehbinder Effect, destruction, medium, defects.

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.