Publication detail

PROGNOSIS OF BEHAVIOUR OF MACHINE TOOL SPINDLES, THEIR DIAGNOSTICS AND MAINTENANCE

MOSYURCHAK, A. VESELKOV, V. TURYGIN, A. HAMMER, M.

Original Title

PROGNOSIS OF BEHAVIOUR OF MACHINE TOOL SPINDLES, THEIR DIAGNOSTICS AND MAINTENANCE

Type

journal article in Scopus

Language

English

Original Abstract

The present article focuses on the use of neural networks to predict the behaviour of certain diagnostic parameters of spindles of machine tools. An online vibro-diagnostics is used, and from many specific measured parameters, the effective value of some variables in the time series. The results are used in the maintenance based on the technical condition, in particular, in preventive and proactive maintenance. This procedure is completely original and allows for better setting of maintenance policy, which is also beneficial for planning maintenance costs. The article also mentions the necessity to implement the steps described, also in the context of the Industry 4.0 initiative, and further, it briefly discussesprognostics, technical diagnostics, maintenance and maintenance systems. The used neural networks and the calculation procedure are also analysed. The conclusions obtained are evaluated.

Keywords

Neural networks, prognosis of diagnostic parameters behaviour, machine tool spindle, preventive and proactive maintenance, maintenance system, Industry 4.0

Authors

MOSYURCHAK, A.; VESELKOV, V.; TURYGIN, A.; HAMMER, M.

Released

13. 12. 2017

Publisher

MM publishing, s.r.o. (2017)

Location

ČR

ISBN

1805-0476

Periodical

MM Science Journal

Number

5

State

Czech Republic

Pages from

2100

Pages to

2104

Pages count

4

URL

http://www.mmscience.eu/content/file/archives/MM_Science_201794.pdf

BibTex

@article{BUT142883,
  author="Andriy {Mosyurchak} and Vladimir {Veselkov} and Andrei {Turygin} and Miloš {Hammer}",
  title="PROGNOSIS OF BEHAVIOUR OF MACHINE TOOL SPINDLES, THEIR DIAGNOSTICS AND MAINTENANCE
",
  journal="MM Science Journal",
  year="2017",
  number="5",
  pages="2100--2104",
  doi="10.17973/MMSJ.2017\{_}12\{_}201794",
  issn="1805-0476",
  url="http://www.mmscience.eu/content/file/archives/MM_Science_201794.pdf"
}