GLR Control Charts for Process Monitoring with Sequential Sampling
| dc.contributor.author | Peng, Yiming | en |
| dc.contributor.committeechair | Reynolds, Marion R. Jr. | en |
| dc.contributor.committeemember | Woodall, William H. | en |
| dc.contributor.committeemember | Du, Pang | en |
| dc.contributor.committeemember | Hong, Yili | en |
| dc.contributor.department | Statistics | en |
| dc.date.accessioned | 2014-11-07T09:00:18Z | en |
| dc.date.available | 2014-11-07T09:00:18Z | en |
| dc.date.issued | 2014-11-06 | en |
| dc.description.abstract | The objective of this dissertation is to investigate GLR control charts based on a sequential sampling scheme (SS GLR charts). Phase II monitoring is considered and the goal is to quickly detect a wide range of changes in the univariate normal process mean parameter and/or the variance parameter. The performance of the SS GLR charts is evaluated and design guidelines for SS GLR charts are provided so that practitioners can easily apply the SS GLR charts in applications. More specifically, the structure of this dissertation is as follows: We first develop a two-sided SS GLR chart for monitoring the mean μ of a normal process. The performance of the SS GLR chart is evaluated and compared with other control charts. The SS GLR chart has much better performance than that of the fixed sampling rate GLR chart. It is also shown that the overall performance of the SS GLR chart is better than that of the variable sampling interval (VSI) GLR chart and the variable sampling rate (VSR) CUSUM chart. The SS GLR chart has the additional advantage that it requires fewer parameters to be specified than other VSR charts. The optimal parameter choices are given, and regression equations are provided to find the limits for the SS GLR chart. If detecting one-sided shifts in μ is of interest, the above SS GLR chart can be modified to be a one-sided chart. The performance of this modified SS GLR chart is investigated. Next we develop an SS GLR chart for simultaneously monitoring the mean μ and the variance 𝜎² of a normal process. The performance and properties of this chart are evaluated. The design methodology and some illustrative examples are provided so that the SS GLR chart can be easily used in applications. The optimal parameter choices are given, and the performance of the SS GLR chart remains very good as long as the parameter choices are not too far away from the optimized choices. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:3905 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/50819 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Average Time to Signal | en |
| dc.subject | Generalized Likelihood Ratio | en |
| dc.subject | Sequential Sampling | en |
| dc.subject | Statistical Process Control | en |
| dc.subject | Variable Sampling Rate | en |
| dc.title | GLR Control Charts for Process Monitoring with Sequential Sampling | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Statistics | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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