JITA

JITA Journal of Information Technology and Applications

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

A. S. Kosmodamianskiy, V. I. Vorobiev, A. A. Pugachev

Automatic Temperature Regulation System of Locomotive Traction Induction Motors With Power Losses Minimization

Original scientific paper

DOI: https://doi.org/10.7251/JIT1501013K

Abstract

The air cooling systems are shown to be used to provide required temperature condition of traction induction motors on locomotives. The automatic temperature regulation system is developed for its using to solve such a task. Results of experimental investigation showed that the AO63-4 induction motor stator end winding on the side opposite to air supply is the most heated part of the induction motor. Based on the results of the research, it was used an aperiodic second-order transfer function for approximation of the thermal transient curves. The design of an induction motor control system maintaining operating mode with minimum of the stator current are considered. It is shown that the modes of minimum of the stator current and minimum of power losses are quite close to each other. The MatLab simulation results taking typical nonlinearities and iron power losses in an induction motor and conduction and commutation power losses in semiconductors of frequency converter into account are presented. It is shown that as a result of application of the suggested system the power losses reduction may be led up to 20 % relatively to classical scalar control.

Keywords: induction motor, locomotive, automatic system, equivalent circuit, power losses minimization

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.