Hidden Markov Models for Analysis of Defective Industrial Machine Parts
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F14%3A%230006649" target="_blank" >RIV/46747885:24210/14:#0006649 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Hidden Markov Models for Analysis of Defective Industrial Machine Parts
Original language description
Monthly counts of industrial machine part errors are modeled using two-state hidden Markov models (HMMs) in order to describe the effect of machine part error correction on the likelihood of the machine parts to be in a ?defective? or ?non-defective? state. A Bayesian framework is used for parameter estimation. The study finds that the machine part error correction does not improve the machine part status of individual part, but there is a very strong month-to-month dependence of machine part states. Acomparison shows that the proposed HMM has a better performance than the traditional Poisson generalized estimating equations (GEE) that directly model the counts.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JR - Other machinery industry
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the International MultiConference of Engineers and Computer Scientists 2014
ISBN
978-988-19253-3-6
ISSN
2078-0958
e-ISSN
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Number of pages
5
Pages from-to
1100-1104
Publisher name
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Place of publication
Hong Kong
Event location
Hong Kong
Event date
Jan 1, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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