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

Yuri M. Inkov, Andrey S. Kosmodaminskiy, Alexander A. Pugachev, Elena V. Sachkova

Simulation of Electric Drive With Direct Torque Control of Induction Motor

Original scientific paper

DOI:https://doi.org/ 10.7251/JIT1801005I

Abstract

The main requirements for traction electric drives are listed and discussed. The direct torque control of an induction motor electric drive is established by a survey of operation modes of traction electric drives to thoroughly satisfy the requirements for traction electric drive. The topologies and operation principles of two-and three-level voltage source inverters are presented. The advantages and shortcomings of three-level voltage source inverters to be applied on locomotive traction drives are highlighted in relation to the two-level ones. The recommendations of choice between different voltage source inverter topologies are given. The topology and principles of operation of direct torque control of induction motors with two- and three-level voltage source inverters are described. The simulation peculiarities of electric drives with direct torque control and two- and three-level inverters in Matlab are considered. The simulation results are presented. The techniques to reduce the torque oscillations are shown and implemented in Matlab Simulink.

Keywords: induction motor, direct torque control, voltage source inverter, equivalent circuit, control system, Matlab.

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.