JITA

JITA Journal of Information Technology and Applications

Vol. 12 No. 1 (2022): JITA - APEIRON

Barbara Bagarić

Encapsulation and Functionality of Sensor Systems in the Welding Process

Original scientific paper

DOI:https://doi.org/10.7251/JIT2201055B

Abstract

This paper shows some aspects of interaction between human and robots and role of the sensors in welding process. Today, use of the robots is very important in modern industry. Sensors are very important in welding process and they increase the productivity and precision of the robot. It is very important to optimize use of the sensor, from choosing the right programming metod, creating good environment such as acceptable amounts of humidity in the air, temperature to educating the operators to be able to work with robot machine and understand and run pre-programmed codes. When all the mentioned steps are correctly defined, sensor will work perfectly and production can be as good as possible.

Keywords: robots, online programming, offline programming, welding process.

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.