Estimation of aircraft power elements by areas of technical condition using clustering algorithm and statistical recognition method

Authors

DOI:

https://doi.org/10.5281/zenodo.4924240

Keywords:

pattern recognition, classification of technical condition, power element

Abstract

In accordance with the concept of maintaining the serviceability of aviation of the Armed Forces of Ukraine, there was a problem of ensuring the serviceability of the fleet of aircraft and accurate classification of the technical condition of power elements of different types of aircraft for timely detection of their limit state. The purpose of the work is to find a system for classifying the technical condition – an image recognition system to solve this problem. The paper considers the issue of choosing an effective method of recognition, the main requirements for it, analyzes the chosen method of classification of technical condition (or recognition of images of technical condition), which is based on the statistical method of recognition. The method of clustering the technical condition of aircraft power elements is considered, a logical block diagram is constructed and the result of the program operation with the selected algorithm “FOREL – I” as a computer program in a graphical shell is given. The implementation of the method of classification in the graphical shell using the programming language python and auxiliary scientific and graphic libraries. The reference objects are selected, the main defining parameters that characterize the intensity of resource potential depletion are determined, which were divided into two images of the technical condition, namely “good” and “bad”, and the control object. As an example, the technical condition of different chassis groups of fighter aircraft of the Armed Forces of Ukraine, which have approximately the same resource hours, and different statistics on the intensity of use during operation are analyzed.

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Published

2021-03-31

How to Cite

Bolohin , A. ., & Strela , M. . (2021). Estimation of aircraft power elements by areas of technical condition using clustering algorithm and statistical recognition method. Political Science and Security Studies Journal, 2(1), 74-83. https://doi.org/10.5281/zenodo.4924240

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