This thesis describes the application of Bayesian networks for monitoring and
diagnosis of a multi-stage manufacturing process, specifically a high speed production
part at Hewlett Packard. Bayesian network "part models" were designed to represent
individual parts in-process. These were combined to form a "process model", which is a
Bayesian network model of the entire manufacturing process. An efficient procedure is
designed for managing the "process network". Simulated data is used to test the validity
of diagnosis made from this method. In addition, a critical analysis of this method is
given, including computation speed concerns, accuracy of results, and ease of
implementation. Finally, a discussion on future research in the area is given. / Graduation date: 1999
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/33629 |
| Date | 25 June 1998 |
| Creators | Wolbrecht, Eric T. |
| Contributors | Paasch, Robert K. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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