Abstract
We describe research efforts to implement a Bayesian belief network based expert system to solve a real-world diagnostic problem — the diagnosis of integrated circuit (IC) testing machines. We describe the development of several models of the IC tester diagnostic problem in belief networks, the implementation of one of these models using Symbolic Probabilistic Inference (SPI), and discuss the difficulties and advantages encountered in the process.
We observe that modelling with interdependencies in belief networks simplified the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on 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 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. With this accomplished, a more complete diagnosis can be achieved.
