Hidden Markov Models for Analysis of Defective Industrial Machine Parts

Result code in 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>
Result on the web
<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>

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 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 =
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JR - Other machinery industry
OECD FORD branch
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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