This thesis describes research to implement a Bayesian belief network based
expert system to solve a real-world diagnostic problem troubleshooting integrated
circuit (IC) testing machines. Several models of the IC tester diagnostic problem
were developed in belief networks, and one of these models was implemented
using Symbolic Probabilistic Inference (SPI). The difficulties and advantages
encountered in the process are described in this thesis.
It was observed that modelling with interdependencies in belief networks
simplified the knowledge engineering task for the IC tester diagnosis problem, by
avoiding procedural knowledge and sticking just to diagnostic component's
interdependencies. Several general model frameworks evolved through knowledge
engineering to capture diagnostic expertise that facilitated expanding and modifying
the networks. However, model implementation was restricted to a small portion of
the modelling - contact resistance failures - because evaluation of the probability
distributions could not be made fast enough to expand the code to a complete
model with real-time diagnosis. Further research is recommended to create new
methods, or refine existing methods, to speed evaluation of the models created in
this research. If this can be done, more complete diagnosis can be achieved. / Graduation date: 1994
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/36573 |
| Date | 06 December 1993 |
| Creators | Mittelstadt, Daniel Richard |
| Contributors | Paasch, Robert |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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