Monitoring chenille yarn defects using image processing with control charts

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24410%2F12%3A%230001002" target="_blank" >RIV/46747885:24410/12:#0001002 - isvavai.cz</a>

Výsledek na webu

<a href="http://trj.sagepub.com/content/81/13.toc" target="_blank" >http://trj.sagepub.com/content/81/13.toc</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1177/0040517511402123" target="_blank" >10.1177/0040517511402123</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Monitoring chenille yarn defects using image processing with control charts

Popis výsledku v původním jazyce

In this paper, a control chart is introduced for monitoring various defect types occurring on chenille yarns. To implement the control chart, a grey level image of chenille yarn is captured as an image matrix. Image preprocessing is applied and this involves thresholding to a binary image and a morphological opening operation for removing small objects from the image. The height of the pile yarn, measured from the processed images, is selected as the monitored quality characteristic. Since the monitoredquality characteristic was highly autocorrelated, a first-order autoregressive AR(1) model was found to be appropriate for modelling the autocorrelation structure. Due to estimation of the AR(1) process parameters, a modified exponentially weighted moving average (EWMA) control chart for residuals is implemented as a tool for monitoring and detecting defects. It is shown that the modified EWMA control chart can be used successfully for monitoring different types of chenille yarn defects

Název v anglickém jazyce

Monitoring chenille yarn defects using image processing with control charts

Popis výsledku anglicky

In this paper, a control chart is introduced for monitoring various defect types occurring on chenille yarns. To implement the control chart, a grey level image of chenille yarn is captured as an image matrix. Image preprocessing is applied and this involves thresholding to a binary image and a morphological opening operation for removing small objects from the image. The height of the pile yarn, measured from the processed images, is selected as the monitored quality characteristic. Since the monitoredquality characteristic was highly autocorrelated, a first-order autoregressive AR(1) model was found to be appropriate for modelling the autocorrelation structure. Due to estimation of the AR(1) process parameters, a modified exponentially weighted moving average (EWMA) control chart for residuals is implemented as a tool for monitoring and detecting defects. It is shown that the modified EWMA control chart can be used successfully for monitoring different types of chenille yarn defects

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JS - Řízení spolehlivosti a kvality, zkušebnictví

OECD FORD obor

—

Projekt

<a href="/cs/project/1M06047" target="_blank" >1M06047: Centrum pro jakost a spolehlivost výroby</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

TEXTILE RESEARCH JOURNAL

ISSN

0040-5175

e-ISSN

—

Svazek periodika

81

Číslo periodika v rámci svazku

13

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

1344-1353

Kód UT WoS článku

000293248700004

EID výsledku v databázi Scopus

—