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2022 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) by Kolodenkova, Anna (author);Vereshchagina, Svetlana (author) is a scholarly article available to read on EtoBox.

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Diagnostics of industrial electrical equipment (asynchronous electric motors, pumps, transformers) (EE) comes down to assessing the technical condition of the equipment. It is shown that one of the most important tasks in assessing the EE condition is to select the optimal set of diagnostic parameters and factors that characterize the equipment and affect it. This selection largely depends not only on the specific type of equipment but also on the method used. This task is poorly structured and poorly formalized in nature, which can result in an incorrect decision regarding the EE serviceability. In this regard, this paper proposes a comprehensive approach to the selection of basic parameters for the diagnosis of industrial electrical equipment using computer technology in conditions of information insufficiency (a large number of different types of parameters). This approach is based on the use of approaches to assessing the degree of interconnection (Spearman's rank correlation coefficient, associativity coefficients, sign correlation function "sign-sign") and fuzzy logic (mixed production rules). The paper proposes a classification of diagnostic parameters and factors, as well a

Author

Kolodenkova, Anna (author);Vereshchagina, Svetlana (author)

Publisher

IEEE

Published

2022

Language

EN