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Articolul precedent |
Articolul urmator |
740 4 |
Ultima descărcare din IBN: 2022-10-05 10:42 |
Căutarea după subiecte similare conform CZU |
622.6:622.23/24 (1) |
Горное дело. Горные предприятия (рудники, шахты, карьеры). Добыча нерудных ископаемых (56) |
SM ISO690:2012 ДОВБЫШ, Анатолий, ЗИМОВЕЦ, Виктория, ЗУБАНЬ, Юрий, ПРИХОДЧЕНКО, Александр. Машинное обучение системы функционального диагностирования шахтной подъемной машины. In: Problemele Energeticii Regionale, 2019, nr. 2(43), pp. 88-102. ISSN 1857-0070. DOI: https://doi.org/10.5281/zenodo.3367060 |
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Problemele Energeticii Regionale | ||||||
Numărul 2(43) / 2019 / ISSN 1857-0070 | ||||||
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DOI:https://doi.org/10.5281/zenodo.3367060 | ||||||
CZU: 622.6:622.23/24 | ||||||
Pag. 88-102 | ||||||
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The aim of the work is to increase the accuracy of functional diagnostics of a mine hoist by using the method of information-extreme machine teaching with a hierarchical data structure. The tasks set forth in the work were to develop a categorical model; to carry out synthesis based on its hi-erarchical machine teaching algorithm for a functional diagnosis system; and to optimize the system of acceptance tolerance. Functional diagnostics necessitates the analysis of a large number of diagnostic features and recognition classes that characterize not only possible malfunctions, but also intermediate technical conditions of nodes and assemblies of a complex machine. The proposed algorithm is devel-oped in the framework of the so-called information-extreme intellectual data analysis technology based on maximizing the information ability of the system in the process of machine teaching. The main idea of the proposed method is to adapt the input mathematical description of the functional di-agnostics system to the maximum reliability of diagnostic solutions in the process of machine teach-ing. The implementation of the proposed method of the information-extremal machine teaching is car-ried out by the example of functional diagnostics of a multi-rope mine hoist. The most significant re-sult is the increase in the reliability of diagnostic solutions when using the hierarchical machine teach-ing algorithm of the functional diagnostics system as compared with the linear classifier. In addition, the crucial rules based on the optimal geometrical parameters of hyperspherical containers of recogni-tion classes make it possible to take highly reliable diagnostic decisions in real time. |
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Cuvinte-cheie information-extreme intellectual technology, machine learning, information criterion, functional diagnostics, shaft lifting machine, ehnologie extremum-informațională intelegentă, învăţare automată, criteriu informațional, diagnosticare funcțională, mașină de ridicare minieră, информационно-экстремальная интеллектуальная технология, машинное обучение, информационный критерий, функциональное диагностирование, шахтная подъемная машина |
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