Optimal filter design approaches to statistical process control for autocorrelated processes

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dc.contributor.advisor Apley, Daniel W. en_US
dc.contributor.advisor Ding, Yu en_US
dc.creator Chin, Chang-Ho, 1974- en_US
dc.date.accessioned 2005-11-01T15:51:39Z
dc.date.available 2005-11-01T15:51:39Z
dc.date.created 2004-08 en_US
dc.date.issued 2005-11-01T15:51:39Z
dc.identifier.uri http://handle.tamu.edu/1969.1/2776
dc.description.abstract Statistical Process Control (SPC), and in particular control charting, is widely used to achieve and maintain control of various processes in manufacturing. A control chart is a graphical display that plots quality characteristics versus the sample number or the time line. Interest in effective implementation of control charts for autocorrelated processes has increased in recent years. However, because of the complexities involved, few systematic design approaches have thus far been developed. Many control charting methods can be viewed as the charting of the output of a linear filter applied to the process data. In this dissertation, we generalize the concept of linear filters for control charts and propose new control charting schemes, the general linear filter (GLF) and the 2nd-order linear filter, based on the generalization. In addition, their optimal design methodologies are developed, where the filter parameters are optimally selected to minimize the out-of-control Average Run Length (ARL) while constraining the in-control ARL to some desired value. The optimal linear filters are compared with other methods in terms of ARL performance, and a number of their interesting characteristics are discussed for various types of mean shifts (step, spike, sinusoidal) and various ARMA process models (i.i.d., AR(1), ARMA(1,1)). Also, in this work, a new discretization approach for substantially reducing the computational time and memory use for the Markov chain method of calculating the ARL is proposed. Finally, a gradient-based optimization strategy for searching optimal linear filters is illustrated. en_US
dc.description.provenance Made available in DSpace on 2005-11-01T15:51:39Z (GMT). No. of bitstreams: 1 etd-tamu-2004B-INEN-Chin.pdf: 696179 bytes, checksum: e0d1dc9738ccf223a9a1e08970863136 (MD5) en
dc.format.extent 696179 bytes
dc.format.medium electronic en_US
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.publisher Texas A&M University en_US
dc.subject Statistical Process Control en_US
dc.subject Control Chart en_US
dc.subject Autocorrelated Process en_US
dc.subject Average Run Length en_US
dc.subject ARL en_US
dc.subject Linear Filter en_US
dc.subject Markov Chain Method en_US
dc.subject Control Chart Design en_US
dc.title Optimal filter design approaches to statistical process control for autocorrelated processes en_US
thesis.degree.department Industrial Engineering en_US
thesis.degree.discipline Industrial Engineering en_US
thesis.degree.grantor Texas A&M University en_US
thesis.degree.name Ph. D. en_US
thesis.degree.level Doctoral en_US
dc.contributor.committeeMember Wichern, Dean W. en_US
dc.contributor.committeeMember Kuo, Way en_US
dc.type.genre Electronic Dissertation en_US
dc.type.material text en_US
dc.format.digitalOrigin born digital en_US

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