Hidden Markov Models for Analysis of Defective Industrial Machine Parts
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F14%3A%230006613" target="_blank" >RIV/46747885:24210/14:#0006613 - isvavai.cz</a>
Výsledek na webu
<a href="http://thescipub.com/pdf/10.3844/jmssp.2014.322.330" target="_blank" >http://thescipub.com/pdf/10.3844/jmssp.2014.322.330</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3844/jmssp.2014.322.330" target="_blank" >10.3844/jmssp.2014.322.330</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Hidden Markov Models for Analysis of Defective Industrial Machine Parts
Popis výsledku v původním jazyce
Monthly counts of industrial machine part errors are modeled using a two-state Hidden Markov Model (HMM) in order to describe the effect of machine part error correction and the amount of time spent on the error correction on the likelihood of the machine part to be in a "defective" or "non-defective" state. The number of machine parts errors were collected from a thermo plastic injection molding machine in a car bumper auto parts manufacturer in Liberec city, Czech Republic from January 2012 to November 2012. A Bayesian method is used for parameter estimation. The results of this study indicate that the machine part error correction and the amount of time spent on the error correction do not improve the machine part status of the individual part, butthere is a very strong month-to-month dependence of the machine part states. Using the Mean Absolute Error (MAE) criterion, the performance of the proposed model (MAE = 1.62) and the HMM including machine part error correction only (MAE =
Název v anglickém jazyce
Hidden Markov Models for Analysis of Defective Industrial Machine Parts
Popis výsledku anglicky
Monthly counts of industrial machine part errors are modeled using a two-state Hidden Markov Model (HMM) in order to describe the effect of machine part error correction and the amount of time spent on the error correction on the likelihood of the machine part to be in a "defective" or "non-defective" state. The number of machine parts errors were collected from a thermo plastic injection molding machine in a car bumper auto parts manufacturer in Liberec city, Czech Republic from January 2012 to November 2012. A Bayesian method is used for parameter estimation. The results of this study indicate that the machine part error correction and the amount of time spent on the error correction do not improve the machine part status of the individual part, butthere is a very strong month-to-month dependence of the machine part states. Using the Mean Absolute Error (MAE) criterion, the performance of the proposed model (MAE = 1.62) and the HMM including machine part error correction only (MAE =
Klasifikace
Druh
J<sub>x</sub> - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)
CEP obor
JR - Ostatní strojírenství
OECD FORD obor
—
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2014
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Journal of Mathematics and Statistics
ISSN
1549-3644
e-ISSN
—
Svazek periodika
10
Číslo periodika v rámci svazku
3
Stát vydavatele periodika
NL - Nizozemsko
Počet stran výsledku
9
Strana od-do
322-330
Kód UT WoS článku
—
EID výsledku v databázi Scopus
—