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Diagnostic reasoning is an activity aimed at finding the causes of incorrect behavior of various technological systems. In order to perform diagnosis, a typical diagnostic system should be equipped with the expert knowledge of the domain and statistical evidence of former failures. More advanced solution combines model-based reasoning (MBR ) and abduction. It is assumed that a model of the system under investigation is specified. Such a model allows us to simulate the normal behavior of the system. It can also be used to detect incorrect behavior and perform sophisticated reasoning in order to identify potential causes of the observed failure. Such potential causes form a set of possible diagnoses. In this chapter, formal bases for the so-called model-based diagnostic reasoning paradigm are presented and application examples are discussed in detail. A method of modeling system behavior with the use of causal graphs is put forward. Then, a systematic method for discovering all the so-called conflict sets (disjunctive conceptual faults) is described. Such conflict sets describe sets of elements in such a manner that in order to explain the observed misbehavior at least one of them must be faulty. By selecting and removing such elements from all conflicts sets – for each conflict set one such element – the proper candidate diagnoses are generated. An example of the application of the proposed methods to the three-tank dynamic system is presented and some bases for on-line generation of diagnoses for dynamic systems are outlined, together with some theorems. The chapter introduces an easy and self-contained material being an introduction to modern model-based diagnosis, covering static and dynamic systems.

DOI: 10.1007/978-3-319-30526-4_20

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