Demonstration of Neural Network in Prediction of Bearing Lifetime

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Demonstration of Neural Network in Prediction of Bearing Lifetime

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Demonstration of Neural Network in Prediction of Bearing Lifetime

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Manufacturing Technology: Journal for Science, Research and Production

ISSN

1213-2489

e-ISSN

—

Svazek periodika

25

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

4

Strana od-do

170-173

Kód UT WoS článku

001671367500002

EID výsledku v databázi Scopus

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