General Bayesian approach for manufacturing equipment diagnostics using sensor fusion

Statistical analysis is used quite heavily in production operations. To use certain advanced statistical approaches such as Bayesian analysis, statistical models must be built. This thesis demonstrates the process of building the Bayesian models and addresses some of the classical limitations by pre...

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Main Author: Locks, Stephanie Isabel
Other Authors: Kurfess, Thomas R.
Format: Others
Language:en_US
Published: Georgia Institute of Technology 2016
Subjects:
Online Access:http://hdl.handle.net/1853/55036
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spelling ndltd-GATECH-oai-smartech.gatech.edu-1853-550362016-06-29T03:35:01ZGeneral Bayesian approach for manufacturing equipment diagnostics using sensor fusionLocks, Stephanie IsabelManufacturingBayesianNaive BayesianDigital manufacturingStatistical analysis is used quite heavily in production operations. To use certain advanced statistical approaches such as Bayesian analysis, statistical models must be built. This thesis demonstrates the process of building the Bayesian models and addresses some of the classical limitations by presenting mathematical examples and proofs, by demonstrating the process with experimental and simulated implementations, and by completing basic analysis of the performance of the implemented models. From the analysis, it is shown that the performance of the Bayesian models is directly related to the amount of separation between the likelihood distributions that describe the behavior of the data features used to generate the multivariate Bayesian models. More specifically, the more features that had clear separation between the likelihood distributions for each possible condition, the more accurate the results were. This is shown to be true regardless of the quantity of data used to generate the model distributions during model building. In cases where distribution overlap is present, it is found that models performance become more consistent as the amount of data used to generate the models increases. In cases where distribution overlap is minimal, it is found that models performance become consistent within 4-6 data sets.Georgia Institute of TechnologyKurfess, Thomas R.2016-05-27T13:24:48Z2016-05-27T13:24:48Z2016-052016-05-27May 20162016-05-27T13:24:48ZThesisapplication/pdfhttp://hdl.handle.net/1853/55036en_US
collection NDLTD
language en_US
format Others
sources NDLTD
topic Manufacturing
Bayesian
Naive Bayesian
Digital manufacturing
spellingShingle Manufacturing
Bayesian
Naive Bayesian
Digital manufacturing
Locks, Stephanie Isabel
General Bayesian approach for manufacturing equipment diagnostics using sensor fusion
description Statistical analysis is used quite heavily in production operations. To use certain advanced statistical approaches such as Bayesian analysis, statistical models must be built. This thesis demonstrates the process of building the Bayesian models and addresses some of the classical limitations by presenting mathematical examples and proofs, by demonstrating the process with experimental and simulated implementations, and by completing basic analysis of the performance of the implemented models. From the analysis, it is shown that the performance of the Bayesian models is directly related to the amount of separation between the likelihood distributions that describe the behavior of the data features used to generate the multivariate Bayesian models. More specifically, the more features that had clear separation between the likelihood distributions for each possible condition, the more accurate the results were. This is shown to be true regardless of the quantity of data used to generate the model distributions during model building. In cases where distribution overlap is present, it is found that models performance become more consistent as the amount of data used to generate the models increases. In cases where distribution overlap is minimal, it is found that models performance become consistent within 4-6 data sets.
author2 Kurfess, Thomas R.
author_facet Kurfess, Thomas R.
Locks, Stephanie Isabel
author Locks, Stephanie Isabel
author_sort Locks, Stephanie Isabel
title General Bayesian approach for manufacturing equipment diagnostics using sensor fusion
title_short General Bayesian approach for manufacturing equipment diagnostics using sensor fusion
title_full General Bayesian approach for manufacturing equipment diagnostics using sensor fusion
title_fullStr General Bayesian approach for manufacturing equipment diagnostics using sensor fusion
title_full_unstemmed General Bayesian approach for manufacturing equipment diagnostics using sensor fusion
title_sort general bayesian approach for manufacturing equipment diagnostics using sensor fusion
publisher Georgia Institute of Technology
publishDate 2016
url http://hdl.handle.net/1853/55036
work_keys_str_mv AT locksstephanieisabel generalbayesianapproachformanufacturingequipmentdiagnosticsusingsensorfusion
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