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Vol. 10 No. 2 (2020): JITA - APEIRON

Tomislav Vujinović, Dragan Mihić, Esad Jakupović

Use of Electronic Modules on Device for Tribological Research in the Field of Plastic Deformation of Slim Metal Sheets

Original scientific paper

DOI:https://doi.org/10.7251/JIT2002081V

Abstract

Electronic modules are important components of manufacturing and research equipment in the field of plastic deformation of sheet metal fabrication, as well as in other processes. Depending on the type and complexity of the production or research process, different electronic modules are also used. The indispensable electronic modules in production as well as experimental (research) systems are: encoders, signal processing, A/D and D/A converters, required software of all levels, all the way to large packages for numerical process simulation. This scientific paper presents an original computerized device for testing tribological influences in plastic deformation of slim (thin) sheet metal forming (fabrication), whose control base consists of electronic modules. Some results are also shown as dependencies, obtained by testing on this developed device.

Keywords: slim (thin) metal sheet, tribology, plastic deformation, electronic modules.

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.