Statistical Methods for Improving and Maintaining Product Reliability
| dc.contributor.author | Dickinson, Rebecca | en |
| dc.contributor.committeechair | Vining, G. Geoffrey | en |
| dc.contributor.committeemember | Driscoll, Anne R. | en |
| dc.contributor.committeemember | Hong, Yili | en |
| dc.contributor.committeemember | Freeman, Laura J. | en |
| dc.contributor.department | Statistics | en |
| dc.date.accessioned | 2014-09-18T08:01:18Z | en |
| dc.date.available | 2014-09-18T08:01:18Z | en |
| dc.date.issued | 2014-09-17 | en |
| dc.description.abstract | When a reliability experiment is used, practitioners can understand better what lifetimes to expect of a product under different operating conditions and what factors are important to designing reliability into a product. Reliability experiments, however, can be very challenging to analyze because often the reliability or lifetime data tend to follow distinctly non-normal distributions and the experiments typically involve censoring. Time and cost constraints may also lead to reliability experiments with experimental protocols that are not completely randomized. In many industrial experiments, for example, the split-plot structure arises when the randomization of the experimental runs is restricted. Additionally, for many reliability experiments, it is often cost effective to apply a treatment combination to a stand with multiple units on it as opposed to each unit individually, which introduces subsampling. The analysis of lifetime data assuming a completely randomized design has been well studied, but until recently analysis methodologies for more complex experimental designs with multiple error terms have not been a focus of the reliability field. This dissertation provides two analysis methods for analyzing right-censored Weibull distributed lifetime data from a split-plot experiment with subsampling. We evaluate the proposed methods through a simulation study. Companies also routinely perform life tests on their products to ensure that products meet requirements. Each of these life tests typically involves testing several units simultaneously with interest in the times to failure. Again, the fact that lifetime data tend to be nonnormally distributed and censored make the development of a control charting procedure more demanding. In this dissertation, one-sided lower and upper likelihood ratio based cumulative sum (CUSUM) control charting procedures are developed for right-censored Weibull lifetime data to monitor changes in the scale parameter, also known as the characteristic life, for a fixed value of the Weibull shape parameter. Because a decrease in the characteristic life indicates a decrease in the mean lifetime of a product, a one-sided lower CUSUM chart is the main focus. We illustrate the development and implementation of the chart and evaluate the properties through a simulation study. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:3638 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/50516 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Censored Data | en |
| dc.subject | CUSUM Chart | en |
| dc.subject | Design of Experiments | en |
| dc.subject | Lifetime Monitoring | en |
| dc.subject | Reliability | en |
| dc.subject | Split-Plot Design | en |
| dc.subject | Weibull Distribution | en |
| dc.title | Statistical Methods for Improving and Maintaining Product Reliability | 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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