The learning path to neural network industrial application in distributed environments

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F21%3A10248432" target="_blank" >RIV/61989100:27230/21:10248432 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2227-9717/9/12/2247" target="_blank" >https://www.mdpi.com/2227-9717/9/12/2247</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/pr9122247" target="_blank" >10.3390/pr9122247</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The learning path to neural network industrial application in distributed environments

Popis výsledku v původním jazyce

Industrial companies focus on efficiency and cost reduction, which is very closely related to production process safety and secured environments enabling production with reduced risks and minimized cost on machines maintenance. Legacy systems are being replaced with new systems built into distributed production environments and equipped with machine learning algorithms that help to make this change more effective and efficient. A distributed control system consists of several subsystems distributed across areas and sites requiring application interfaces built across a control network. Data acquisition and data processing are challenging processes. This contribution aims to present an approach for the data collection based on features standardized in industry and for data classification processed with an applied machine learning algorithm for distinguishing exceptions in a dataset. Files with classified exceptions can be used to train prediction models to make forecasts in a large amount of data. (C) 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Název v anglickém jazyce

The learning path to neural network industrial application in distributed environments

Popis výsledku anglicky

Industrial companies focus on efficiency and cost reduction, which is very closely related to production process safety and secured environments enabling production with reduced risks and minimized cost on machines maintenance. Legacy systems are being replaced with new systems built into distributed production environments and equipped with machine learning algorithms that help to make this change more effective and efficient. A distributed control system consists of several subsystems distributed across areas and sites requiring application interfaces built across a control network. Data acquisition and data processing are challenging processes. This contribution aims to present an approach for the data collection based on features standardized in industry and for data classification processed with an applied machine learning algorithm for distinguishing exceptions in a dataset. Files with classified exceptions can be used to train prediction models to make forecasts in a large amount of data. (C) 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

<a href="/cs/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Centrum výzkumu pokročilých mechatronických systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Processes

ISSN

2227-9717

e-ISSN

—

Svazek periodika

9

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

—

Kód UT WoS článku

000737407900001

EID výsledku v databázi Scopus

2-s2.0-85121731731