Titlebook: System Test and Diagnosis; William R. Simpson,John W. Sheppard Book 1994 Springer Science+Business Media New York 1994 complexity.developm

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System Level Analysis for Diagnosisdels, but the resulting model is incomplete. As we saw in the previous chapter, with a well defined set of failure modes, we may be able to use a simulator and define the test patterns in an automatic fashion. This . approach has an advantage in that a large part of the work in constructing diagnost

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The Information Flow Modelodel-based approach to system test and diagnosis incorporates techniques from information fusion and artificial intelligence to guide analysis. The model represents the problem to be solved as information flow. Tests provide information and diagnostic inference combines information from multiple tes

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Architectures for System Diagnosisplex systems involves many disciplines that are layered in like building blocks as shown in Figure 10-1. Each of the disciplines as well as each of the techniques must fit within the maintenance structure that is sometimes crafted and sometimes evolves. The particular maintenance architecture may be

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Inexact Diagnosistests, and the conclusions to be drawn from the test outcomes. In many diagnostic systems, test outcomes are assumed to be 100% certain, and diagnosis proceeds through partitioning of the answer set into feasible and infeasible conclusions (as described in detail in Chapter 7). Frequently, this appr

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Diagnosis—Art versus Scienceategies. Because these techniques have been successfully applied to many systems, a number of tools have been developed that either apply these techniques directly, or perform calculations that are closely similar (Simpson 1985; Cantone and Caserta 1988; DePaul 1985; Franco and Scott 1986; Beniaminy

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