Statistical Process Control in Big Data Environment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27360%2F20%3A10247098" target="_blank" >RIV/61989100:27360/20:10247098 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9257251" target="_blank" >https://ieeexplore.ieee.org/document/9257251</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCC49264.2020.9257251" target="_blank" >10.1109/ICCC49264.2020.9257251</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Statistical Process Control in Big Data Environment

Popis výsledku v původním jazyce

Big data analysis tools are an inevitable part of instruments and methods for monitoring and predicting the longitudinal performance of the processes in the production systems of the future, based on the deep automatization and overall digitalization. From this point of view statistical process control (SPC) will continue to be very effective method for meeting these goals. But there must be done some modifications. This paper deals with such possible modifications of SPC. In the first part of the paper the stress is put on various methods that can be integrated into SPC to meet new challenges in collecting, analysing and interpreting data (control charts for high yield processes, multivariable approaches, profile monitoring, data mining tools including machine learning methods, nonparametric control charts). SW for the selected discussed methods is also mentioned. The second part of the paper is devoted to the nonparametric methods of SPC and to the methodology of their practical application. (C) 2020 IEEE.

Název v anglickém jazyce

Statistical Process Control in Big Data Environment

Popis výsledku anglicky

Big data analysis tools are an inevitable part of instruments and methods for monitoring and predicting the longitudinal performance of the processes in the production systems of the future, based on the deep automatization and overall digitalization. From this point of view statistical process control (SPC) will continue to be very effective method for meeting these goals. But there must be done some modifications. This paper deals with such possible modifications of SPC. In the first part of the paper the stress is put on various methods that can be integrated into SPC to meet new challenges in collecting, analysing and interpreting data (control charts for high yield processes, multivariable approaches, profile monitoring, data mining tools including machine learning methods, nonparametric control charts). SW for the selected discussed methods is also mentioned. The second part of the paper is devoted to the nonparametric methods of SPC and to the methodology of their practical application. (C) 2020 IEEE.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

<a href="/cs/project/EF17_049%2F0008425" target="_blank" >EF17_049/0008425: Platforma pro výzkum orientovaný na Průmysl 4.0 a robotiku v ostravské aglomeraci</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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2020 21st International Carpathian Control Conference, ICCC 2020

ISBN

978-1-72811-952-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Košice

Datum konání akce

27. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—