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Diagnosing manufacturing process variation using higher order statistics: a blind source separation approach
| dc.creator | Lee, Ho Young | |
| dc.date.accessioned | 2012-06-07T23:00:01Z | |
| dc.date.available | 2012-06-07T23:00:01Z | |
| dc.date.created | 2000 | |
| dc.date.issued | 2000 | |
| dc.identifier.uri | https://hdl.handle.net/1969.1/ETD-TAMU-2000-THESIS-L437 | |
| dc.description | Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to digital@library.tamu.edu, referencing the URI of the item. | en |
| dc.description | Includes bibliographical references (leaves 53-56). | en |
| dc.description | Issued also on microfiche from Lange Micrographics. | en |
| dc.description.abstract | A large volume of multivariate data is available from the manufacturing process. A new statistical approach in the context of signal processing is investigated to diagnose the root causes of unusual variability from the multivariate measurement data. Since the existing statistical approaches, which are related to the process diagnosis and uses second order statistic, have limitations and restrictions, the new statistical approach uses higher order information. The new approach is analyzed in several situations and is compared with one of existing statistical approach. For example, autobody assembly process is used. | en |
| dc.format.medium | electronic | en |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en_US | |
| dc.publisher | Texas A&M University | |
| dc.rights | This thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries in 2008. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use. | en |
| dc.subject | industrial engineering. | en |
| dc.subject | Major industrial engineering. | en |
| dc.title | Diagnosing manufacturing process variation using higher order statistics: a blind source separation approach | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | industrial engineering | en |
| thesis.degree.name | M.S. | en |
| thesis.degree.level | Masters | en |
| dc.type.genre | thesis | en |
| dc.type.material | text | en |
| dc.format.digitalOrigin | reformatted digital | en |
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