Development of a condition monitoring system for compressor equipment with neural network data analysis

Описание

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Scientific Conference on Applied Physics, Information Technologies and Engineering 2019, APITECH 2019

Год издания: 2019

Идентификатор DOI: 10.1088/1742-6596/1399/2/022058

Аннотация: Currently, one of the most widely used and effective types of technological equipment is screw compressor equipment. Along with the fact, that such equipment has a number of advantages that determine its high efficiency, it is characterized by increased wear of important structural elements. This can lead to reduced compressor effiПоказать полностьюciencies and malfunctions that can result in emergencies. In this regard, the paper presents the results of developing a scheme for continuous monitoring of the technical condition of screw compressor units. Variants of installing vibration sensors that provide data collection of vibration diagnostics are determined. In order to automate the analysis of the collected data, it is proposed to use the method of data mining based on neural networks to recognize the technical condition. The results of testing the neural network data method of a real compressor unit are presented. © Published under licence by IOP Publishing Ltd.

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Издание

Журнал: Journal of Physics: Conference Series

Выпуск журнала: Vol. 1399, Is. 2

Номера страниц: 22058

ISSN журнала: 17426588

Издатель: Institute of Physics Publishing

Авторы

  • Zyryanov D.K. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation)
  • Bukhtoyarov V.V. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, Krasnoyarsky Rabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
  • Bukhtoyarova N.A. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation)
  • Kukartsev V.V. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, Krasnoyarsky Rabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
  • Tynchenko V.S. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, Krasnoyarsky Rabochy Av. 31, Krasnoyarsk, 660037, Russian Federation)
  • Bashmur K.A. (Siberian Federal University, Svobodny pr. 79, Krasnoyarsk, 660041, Russian Federation)

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