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Vol. 11 No. 1 (2021): JITA - APEIRON

Jefto Džino, Branko Latinović, Zoran Ž. Avramović

Making Decisions in Monitoring by Using Decision-Making Method, Knowledge Bases and New it Solutions

Original scientific paper

DOI:https://doi.org/ 10.7251/JIT2101033D

Abstract

In this paper we deal with decision-making processes in monitoring with the use of new technological solutions. This is an area where decision-makers in monitoring face a large number of different challenges and need appropriate specific knowledge. We give an example of a method for making complex decisions. Here we propose the application of the semantic web and knowledge bases that can provide decision-makers with a quick access to the necessary knowledge in the decision-making process. To update some of the knowledge we will use the Protégé editor, an open source platform. Our goal is not to update all the necessary knowledge needed by those who make decisions in monitoring, but only to propose a new concept to their faster fullfilment and more efficient use.

Keywords: monitoring, decision-making, knowledge base, efficient use of knowledge, methods, facilitation, business intelligence.

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.