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4.5. DIAGNOSISModel-based diagnosis (MBD) has received more widespread practical application than any other application of MBR techniques. It appears that this is a particularly fertile ground for using these techniques. Diagnosis is a task of high practical and direct economic significance. Improving availability of, e.g., a manufacturing plant by as little as a few tenths of a percent can yield enormous benefits. A survey of techniques and applications of diagnostic advisory systems, including systems using MBR techniques, can be found in Kramer and Fjellheim (1995). One can generalize the flow of activities in the diagnostic process to the following steps:
A generic system architecture that supports this diagnostic process, using the model-based approach, is shown on Figure 2 (Leitch et al., 1992). The architecture is representative of a large number of MBD applications, even though details may differ significantly from system to system. In this architecture, a system model is run in parallel with the system itself, producing predictions of what the observations "ought to be." A discrepancy detector module tracks observations and predictions, and triggers on significant deviations (note that this may in itself be a nontrivial operation, perhaps involving data validation and statistical processing). The discrepancies are fed into a candidate generator module, which proposes one or more fault candidates. These are used to modify the behavior of the system model according to the hypothesized fault. If the predictions now match the observations, we know that the fault has been identified. The whole diagnostic process is steered by a diagnostic supervisor module, which may range in complexity from a simple invocation interface to a sophisticated strategy reasoner. A recent and very substantial MBD application is the TIGER system (Travé-Massuyés and Milne, 1996). The application domain of TIGER is monitoring and diagnosis of gas turbines, an area of large industrial interest. The TIGER system has been deployed at a large petroleum company. Some technical features characterizing this application are:
Other successful expert systems that use an MBD approach are (many other systems could have been listed):
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