Eva Volna

Dr. Eva Volna

Ostravská Univerzita v Ostrave, Ostrava, Czech Republic
orcid

Research Focus & Metrics

Academic Career Length

27Years (2000-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 July 2021 in Applied Sciences
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Journal Article
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FHFilip Hardt
Filip Hardt
Martin KotyrbaMartin Kotyrba
Martin Kotyrba
EVEva Volna
Eva Volna
RJRobert Jarusek
Robert Jarusek
Preventive maintenance (PM) in the production industry is one of the most essential measures to eliminate accidental machinery failures by replacing/repairing worn out machines or parts. The decision of when and where to perform preventive maintenance is non-trivial due to the complex and stochastic nature of the industry where PM is implemented. This article deals with the theoretical and practical implementation of preventive maintenance based on a unique modification of the total productive maintenance (TPM) methodology. The innovative approach of preventive maintenance management was implemented in the real production hall of ITT (Czech Republic) and has been verified. Within preventive maintenance, the new concept brings in an innovative method of managing the maintenance process as a whole, from abstract methodical conception to practical usage. The whole new approach has been verified and implemented on industrial equipment. A challenging task while implementing Industry 4.0 technologies is the issue of how to fully gather and analyse operational data from various items of equipment and users under various conditions, which would result in innovative services of equipment maintenance for clients. The solution to this problem is based on an innovative approach to preventive maintenance of complex equipment and could help many industrial companies to increase production and maintenance efficiency.

29 May 2012
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Conference Paper
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EVEva Volna
Eva Volna
MKMartin Kotyrba
Martin Kotyrba
VKVaclav Kocian
Vaclav Kocian
MJMichal Janosek
Michal Janosek

1 January 2022 in Neural Networks
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Journal Article
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RJRobert Jarusek
Robert Jarusek
EVEva Volna
Eva Volna
Martin KotyrbaMartin Kotyrba
Martin Kotyrba

30 November 2021 in Mathematics
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Journal Article
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RMRoman Marsalek
Roman Marsalek
Martin KotyrbaMartin Kotyrba
Martin Kotyrba
EVEva Volna
Eva Volna
RJRobert Jarusek
Robert Jarusek
The study is focused on monitoring the influence of selected parameters on the zeta potential values of titanium dioxide nanoparticles. The influence of pH, temperature, ionic strength, and mass content of titanium dioxide in the suspension was assessed. More than a thousand samples were measured by combining these variables. On the basis of results, the model of artificial neural network was proposed and tested. The authors have rich experiences with neural networks applications and this case shows that the neural network model works with a very high prediction success rate of zeta potential. Clearly, pH has the greatest effect on zeta potential values. The influence of other variables is not so significant. However, it can be said that increasing temperature results in an increase in the value of the zeta potential of titanium dioxide nanoparticles. The ionic force affects the zeta potential depending on the pH; in the vicinity of the isoelectric point, its effect is negligible. The effect of the mass content of titanium dioxide in the suspension is absolutely minor.

1 August 2013 in Computers & Mathematics with Applications
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Journal Article
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EVEva Volna
Eva Volna
MKMartin Kotyrba
Martin Kotyrba
RJRobert Jarusek
Robert Jarusek

1 December 2015 in Swarm and Evolutionary Computation
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Journal Article
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Martin KotyrbaMartin Kotyrba
Martin Kotyrba
EVEva Volna
Eva Volna
PBPetr Bujok
Petr Bujok