Detail publikace

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

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

Originální název

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

Typ

článek v časopise ve Scopus, Jsc

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Vydáno

13. 12. 2017

Nakladatel

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

Místo

ČR

ISSN

1805-0476

Periodikum

MM Science Journal

Číslo

5

Stát

Česká republika

Strany od

2100

Strany do

2104

Strany počet

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"
}