AERONAUTICAL ENGINEERING MAINTENANCE PERIODICITY OPTIMIZATION WITH THE HELP OF SUBJECTIVE PREFERENCES DISTRIBUTIONS
Journal Title: Вісник Національного Авіаційного Університету - Year 2017, Vol 71, Issue 2
Abstract
Purpose: The goal of this research is to investigate the possibility of the objectively existing aeronautical engineering maintenance optimal periodicity determination in the different from the probabilistic methods way. In this paper there is a scientifically proven explanation for the mentioned above periodicity optimization with the help of subjective preferences distributions. Methods: The described approach proposes to consider functioning of an aeronautical engineering system with possible degrading failure on the multi-alternativeness basis. The objective effectiveness functions in such a case are intensities of damage and failure which corresponds with the set of considered operational alternatives. It allows obtaining the related subjective preferences functions implying the preferences proportionality to the alternative intensities of damage and failure. Results: The found expression for the optimized maintenance periodicity is the same as yielded by probabilistic methods and subjective preferences functions deliver the extremal (maximal) value to the operational-maintenance purpose functional. While this objective functional undergoes its maximum for optimal both canonical and proposed view preference distributions, the subjective entropy of the preference does not have any exremum in the vicinity at all. Discussion: The proposed approach model parameters interpretation, with the help of the carried out calculation experiments and plotting necessary diagrams, means that the damage and failure intensities are the selfmeasured subjective preferences obtained with the use of the subjective entropy maximum principle. They are expressed so in the terms of the subjective analysis theory.
Authors and Affiliations
Andriy Goncharenko
Keywords
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- EP ID EP459681
- DOI 10.18372/2306-1472.71.11746
- Views 133
- Downloads 0
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