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

Kristina Jakimovska, Biljana Stojcevska, Anita Vasileva

Assessment of Intelligent Solutions for Improving Elevators’ Performances

Original scientific paper

DOI:https://doi.org/10.7251/JIT2002112J

Abstract

In the process of introduction of information as well as data capabilities the first approach is adding technology that can be used in many spheres for buildings and upgrading apparatus and utensils. However the focus of this study is on the deficiency of current elevators associated with efficiency and debugging of the errors or security systems where we concentrate on the introduction of new trends which advise that elevators should be implemented with intelligent devices. Smart elevators easily provide means to predict and prevent errors and bring the chances of an error to a minimum. Needless to say is that a range of negative effects are unavoidable when it comes to the introduction of new technology. This paper will illustrate both the advantages and the disadvantages of using intelligent devices in elevators and through an analysis of the various options using Multi- Criteria Analysis method perform ranking of the presented solutions.

Keywords: elevator, intelligent technology, The Multi-Criteria Decision-Making (MCDM).

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.