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STUDY OF THE TECHNICAL CONDITION OF ELECTRIC DRIVE UNDER DIFFERENT LOADING CONDITIONS
Author(s) -
Татьяна Круглова,
Tatiana Kruglova
Publication year - 2019
Publication title -
bulletin of belgorod state technological university named after v g shukhov
Language(s) - English
Resource type - Journals
ISSN - 2071-7318
DOI - 10.34031/article_5ca1f6347299b0.43047357
Subject(s) - reliability (semiconductor) , reliability engineering , mode (computer interface) , vibration , wavelet , electrical equipment , condition monitoring , computer science , automotive engineering , electric motor , wavelet transform , voltage , process (computing) , state (computer science) , control engineering , engineering , electrical engineering , artificial intelligence , power (physics) , algorithm , quantum mechanics , operating system , physics
The main elements of the process equipment are DC and AC motors that largely determine its reliability and efficiency of operation. The constant monitoring of technical condition by methods of technical diagnostics allows a significant extension of equipment life and reduces financial costs. To implement this approach, specialized methods are required. They allow to determine the technical condition of DC and AC motors with a high degree of reliability, distinguishing their faulty state from changing the operating mode. Diagnostics should be performed in the mode of equipment operation; therefore, the use of complex measuring devices is not permissible. This article presents the results of search studies of diagnosis method that meets the above-mentioned requirements. Current, voltage and vibration are selected as diagnostic parameters. It is proposed to analyze them by the wavelet transform. As a result of numerous experiments, the relationship between changes in the wavelet transform coefficients on characteristic scales has been established. This allows to determine the technical condition of the electric motor and the mode of its load, on the basis of which a diagnostic method has been developed using neural networks.

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