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Automation of the control process of the mining machines based on fuzzy logic

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Category: Information Technologies

Last Updated on 29 June 2019

Published on 16 June 2019

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Authors:

A.V.Bublikov, Cand. Sc. (Tech.), Assoc. Prof., orcid.org/0000-0003-3015-6754, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.

V.V.Tkachov, Dr. Sc. (Tech.), Prof., orcid.org/0000-0002-2079-4923, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract:

Purpose. To improvethe efficiency of mining equipment functioning through the introduction of fuzzy inference algorithms into the control of mining facilities being complex objects with dynamic, randomly varied operation modes.

Methodology. A stage to determine nonfuzzy input variable systems as statistic indices has been added to universally accepted algorithm of a fuzzy interference. The indices make it possible to identify operation mode of mining equipment or operation mode transitions. Each input nonfuzzy variable of the system is a result of statistic processing of information signals from the sensors to find unique regularities within the signal; the regularities should correspond to one or several operation modes of the machine, or transitions between them. Additional linguistic input variables of “Previously, their operation mode was…” are introduced to trace temporally varied operation modes of the mining machine. Taking into consideration features of input nonfuzzy variables formation, the system performs fuzzy inference discretely in time; in this context, fuzzy inference interference algorithm is added by the conditions for data accumulation, variations, and zero variations in the machine operation mode.

Findings. A technique to integrate a behavioural model of a mining machine, considered as a control object, into a fuzzy inference algorithm has been proposed. Moreover, recommendations, concerning determination of both nonfuzzy and linguistic input variables of the system, formation of a basis of fuzzy products, and determination of conditions for data accumulation have been formulated.

Originality. Originality of the proposed technique is to use a behavioural model of a mining machine, considered as a control object, for the fuzzy inference algorithm in the form of a set of operation modes of a machine and in the form of a scheme of time-dependent changes in operation modes.

Practical value. The proposed technique istheoretical basis to solve topical scientific and application problem as well as to use nonfuzzy algorithms to control mining machines to improve their efficiency.

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