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The Detection of Reliability Prediction Cues in Manufacturing Data from Statistically Controlled Processes

Abstract Many products undergo several stages of testing ranging from tests on individual components to end-item tests. Additionally, these products may be further "tested" via customer or field use. The later failure of a delivered product may in some cases be due to circumstances that have no correlation with the product's inherent quality. However, at times, there may be cues in the upstream test data that, if detected, could serve to predict the likelihood of downstream failure or performance degradation induced by product use or environmental stresses. This study explores the use of downstream factory test data or product field reliability data to infer data mining or pattern recognition criteria onto manufacturing process or upst... (more)
Created Date 2011
Contributor Mosley, James Holton (Author) / Morrell, Darryl (Advisor) / Morrell, Darryl (Committee member) / Cochran, Douglas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Roberts, Chell (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Subject Electrical Engineering / Industrial Engineering / Applied Mathematics / Hyperplane Classifier / L-moment Kernel / Order Statistics / Statistical Process Control / Support Vector Machines / Western Electric Rules
Type Doctoral Dissertation
Extent 123 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Electrical Engineering 2011
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis