Demonstration of Neural Network in Prediction of Bearing Lifetime
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F25%3A00384412" target="_blank" >RIV/68407700:21220/25:00384412 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.21062/mft.2025.017" target="_blank" >https://doi.org/10.21062/mft.2025.017</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.21062/mft.2025.017" target="_blank" >10.21062/mft.2025.017</a>
Alternative languages
Result language
angličtina
Original language name
Demonstration of Neural Network in Prediction of Bearing Lifetime
Original language description
The topic of this paper is the application of machine learning and neural networks in engineering, specifically in the prediction of the lifetime of bearings operating in different conditions. In addition, the basics of ma-chine learning are introduced, giving an idea of the importance of input data quality for model training. It also presents the elements of neural network training to be used in other projects. The article is supplemented by a source code examples written using only the Python language, and some other popular libraries, like the NumPy, Matplotlib, Tensorflow, Keras, and Scikit-learn. The main advantage of the libraries used is that they are freely available and widely used, bringing variety of sophisticated tools for gen-eral use.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20301 - Mechanical engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Manufacturing Technology: Journal for Science, Research and Production
ISSN
1213-2489
e-ISSN
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Volume of the periodical
25
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
4
Pages from-to
170-173
UT code for WoS article
001671367500002
EID of the result in the Scopus database
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