Collection and processing of bearing vibration data for their technical condition classification by machine learning methods

Authors

  • Ruslan Babudzhan National Technical University "Kharkiv Polytechnic Institute"
  • Kostiantyn Isaienkov
  • Danylo Krasii
  • Ruben Melkonian
  • Oleksii Vodka
  • Ivan Zadorozhniy
  • Michael Yushchuk

DOI:

https://doi.org/10.31713/MCIT.2021.02

Keywords:

Vibration, signals, vibrodiagnostics, signal statistics, feature extraction, exploratory data analysis, machine learning, Fourier transform

Abstract

An experimental research facility has been developed to receive vibration signals from mechanisms with installed rolling bearings. A control block for all equipment has been created. For the repeatability of the experiment, an external microcontroller with a programmed proportional-integral-derivative regulator was used.

Experiments were carried out to obtain initial data for different types of bearings. The processed data were grouped and made publicly available for further research. It is proposed to solve the problem of emergency stop of the generator, arising during operation due to bearings worn, by recognizing the pre-emergency conditions of rotary rig based on the use of advanced machine learning techniques: to highlight the signs of vibration and build clusters according to the degree of worn.

Downloads

Published

2021-11-21

How to Cite

Babudzhan, R., Isaienkov, K., Krasii, D. ., Melkonian, R., Vodka, O. ., Zadorozhniy, I. ., & Yushchuk, M. . (2021). Collection and processing of bearing vibration data for their technical condition classification by machine learning methods. Modeling, Control and Information Technologies, (5), 10–15. https://doi.org/10.31713/MCIT.2021.02

Issue

No. 5 (2021): Modeling, control and information technologies

Section

index

License

Copyright (c) 2021 Modeling, Control and Information Technologies: Proceedings of International scientific and practical conference

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

All materials are distributed under the terms of the Creative Commons Attribution 4.0 International License, which allows others to distribute the work with attribution to the authorship of this work and the first publication in this journal.