Title:
New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles

Simple item page
dc.contributor.advisor Kim, Seong-Hee
dc.contributor.advisor Wilson, James R.
dc.contributor.author Lee, Joongsup en_US
dc.contributor.committeeMember Huo, Xiaoming
dc.contributor.committeeMember Hur, Youngmi
dc.contributor.committeeMember Shi, Jianjun
dc.contributor.department Industrial and Systems Engineering en_US
dc.date.accessioned 2012-02-17T19:12:18Z
dc.date.available 2012-02-17T19:12:18Z
dc.date.issued 2011-08-18 en_US
dc.description.abstract In this thesis, we first investigate the use of automated variance estimators in distribution-free statistical process control (SPC) charts for univariate autocorrelated processes. We introduce two variance estimators---the standardized time series overlapping area estimator and the so-called quick-and-dirty autoregressive estimator---that can be obtained from a training data set and used effectively with distribution-free SPC charts when those charts are applied to processes exhibiting nonnormal responses or correlation between successive responses. In particular, we incorporate the two estimators into DFTC-VE, a new distribution-free tabular CUSUM chart developed for autocorrelated processes; and we compare its performance with other state-of-the-art distribution-free SPC charts. Using either of the two variance estimators, the DFTC-VE outperforms its competitors in terms of both in-control and out-of-control average run lengths when all the competing procedures are tested on the same set of independently sampled realizations of selected autocorrelated processes with normal or nonnormal noise components. Next, we develop WDFTC, a wavelet-based distribution-free CUSUM chart for detecting shifts in the mean of a high-dimensional profile with noisy components that may exhibit nonnormality, variance heterogeneity, or correlation between profile components. A profile describes the relationship between a selected quality characteristic and an input (design) variable over the experimental region. Exploiting a discrete wavelet transform (DWT) of the mean in-control profile, WDFTC selects a reduced-dimension vector of the associated DWT components from which the mean in-control profile can be approximated with minimal weighted relative reconstruction error. Based on randomly sampled Phase I (in-control) profiles, the covariance matrix of the corresponding reduced-dimension DWT vectors is estimated using a matrix-regularization method; then the DWT vectors are aggregated (batched) so that the nonoverlapping batch means of the reduced-dimension DWT vectors have manageable covariances. To monitor shifts in the mean profile during Phase II operation, WDFTC computes a Hotelling's T-square--type statistic from successive nonoverlapping batch means and applies a CUSUM procedure to those statistics, where the associated control limits are evaluated analytically from the Phase I data. We compare WDFTC with other state-of-the-art profile-monitoring charts using both normal and nonnormal noise components having homogeneous or heterogenous variances as well as independent or correlated components; and we show that WDFTC performs well, especially for local shifts of small to medium size, in terms of both in-control and out-of-control average run lengths. en_US
dc.description.degree PhD en_US
dc.identifier.uri http://hdl.handle.net/1853/42711
dc.publisher Georgia Institute of Technology en_US
dc.subject SPC en_US
dc.subject Brownian motion en_US
dc.subject Wavelets en_US
dc.subject Profiles en_US
dc.subject Variance estimation en_US
dc.subject CUSUM en_US
dc.subject.lcsh Process control Statistical methods
dc.subject.lcsh Quality control
dc.subject.lcsh Process control
dc.title New control charts for monitoring univariate autocorrelated processes and high-dimensional profiles en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Kim, Seong-Hee
local.contributor.corporatename H. Milton Stewart School of Industrial and Systems Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 7d0731d7-690b-4695-86cd-fbf52c7c8b6f
relation.isOrgUnitOfPublication 29ad75f0-242d-49a7-9b3d-0ac88893323c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
lee_joongsup_201112_phd.pdf
Size:
514.79 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Theses and Dissertations