JITA

JITA Journal of Information Technology and Applications

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

Yu M. Inkov, E. V. Sachkova, Ya. A. Korobanova, T. N. Fadeikin

Simulation of Processes in Traction Electric Actuators of Autonomous Vehicles

Original scientific paper

DOI: https://doi.org/10.7251/JIT1501005I

Abstract

At the stage of construction of traction electric drives of electric power systems (EPS) the analysis of electromagnetic and energy processes in various operational and emergency modes is needed. The calculation of complex multi-electromechanical systems of modern vehicles is only possible by computer simulation. The programming of such complex systems by traditional methods is practically impossible, or is a time-consuming process. The use of universal modeling systems is the only possible way of modeling of multi-component systems. In this article we deal with the mathematical model of the synchronous generator of autonomous vehicle in computer-aided design (CAD) OrCAD 10.0 (Pspice). The software package OrCAD 10.0 (Pspice) is one of the most versatile in the field of simulation of electrical circuits with a large number of components. OrCAD libraries contain proven by the time mathematical models of practical application of electric power components and it is continuously ever-growing. At the end of the article the characteristics for different modes of operation of a synchronous generator are summarized.

Keywords: Traction electric drive, autonomous locomotive, marine engine, mathematical model, energy processes, electromechanical system, synchronous generator, the hysteresis loop.

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.