As manufacturing systems are becoming more complex, the use of multivariate fault detection and diagnosis methods are increasingly important. Effective fault detection and diagnosis methods can minimize cost of rework, plant down time and maintenance time and improve reliability and safety. This thesis proposes Principal Components Analysis (PCA) based root cause identification approach for quality improvement in complex manufacturing processes. Simulation studies are presented to demonstrate the improved diagnosability of the proposed approach compared to existing methods. / A Thesis submitted to the Department of Industrial and Manufacturing Engineering in
partial fulfillment of the requirements for the degree of Master of Science. / Degree Awarded: Summer Semester, 2010. / Date of Defense: June 11, 2010. / Multivariate Analysis, Principal Components Analysis (PCA), Contribution Plot, Root Cause Identification / Includes bibliographical references. / Arda Vanli, Professor Directing Thesis; Ben Wang, Committee Member; Chuck Zhang, Committee Member; Joseph J. Pignatiello, Jr., Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_168903 |
| Contributors | Ding, Yi (authoraut), Vanli, Arda (professor directing thesis), Wang, Ben (committee member), Zhang, Chuck (committee member), Pignatiello, Joseph J. (committee member), Department of Industrial and Manufacturing Engineering (degree granting department), Florida State University (degree granting institution) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text |
| Format | 1 online resource, computer, application/pdf |
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