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Vol. 2 No. 2 (2012): JITA - APEIRON

Mahir Zajmović, Hadzib Salkić, Saša Stanić

Management of Induction (Asynchronous) Motors Using PLC

Original scientific paper

DOI: https://doi.org/10.7251/JIT1202095Z

Abstract

This paper describes the management of an induction (asynchronous) motor using PLC and VSD. For the realization of the practical part of this paper Schneider Electric equipment was used, which makes a complete system that is used in Natron Hayat d.o.o Maglaj, where this experiment was done. For this paper, a Schneider Zelio PLC was used, which with the aid of a 5.5 kW Schneider Altivar ATV312HU55N4 modulator (frequency transformer), managed the work of a 5.5 kW induction motor at speed of 1500 RPM. Managing controls were given for HP mobile working stations, where Windows XP operating system with SCADA software from DAQFactory was installed. The link used between the working stations and the PLC was Ethernet (Modbus TCP/IP).

Keywords: asynchronous, motor, PLC, VSD, frequency, transformer, management, SCADA, software, DAQFactory.

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.