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Vol. 13 No. 1 (2023): JITA - APEIRON

Vladimir Milošević

Aircraft Performance Modeling with Polynomial Function using Small Variable Units Technique

Original scientific paper

Abstract

Mathematical methods of Regression analysis, with focus on polynomial regression, are useful analytical methods for trend-line definitions of an aircraft aerodynamics. Nomogram is graphical interpretation of polynomial regression analysis results and aero-dynamical performance according to different environmental parameters and requirements. If diagram defines relations of two variables, where the one is dependable of another one (y=f (x)), the nomogram defines relations among three variables, where the one is resulting and dependable of another two undependable. The Small Variable Units Technique is efficient method to transfer nomograms’ data in polynomial equitation which can give us different mathematical models of an aircraft performance. In combination with time based navigation, digitalization of aerodynamical characteristic will be a step forward to Continuous Climb and Descent Trajectories, as the most optimal one.

Keywords: performance optimization, digitalization, composite, regression, nomogram.

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.