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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

  • DOI - Digital Object Identifier

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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JR - Other machinery industry

  • OECD FORD branch

Result continuities

  • Project

  • 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

  • Number of pages

    5

  • Pages from-to

    1100-1104

  • Publisher name

  • 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