Functional diagnosis for complex systems can be a very time-consuming and expensive task, trying to identify the source of an observed misbehavior. We propose an automatic incremental diagnostic methodology and CAD flow, based on data mining. It is a model-based approach that incrementally determines the tests to be executed to isolate the faulty component, aiming at minimizing the total number of executed tests, without compromising 100% diagnostic accuracy. The data mining engine allows for shorter test sequences with respect to other reasoningbased solutions (e.g., Bayesian belief networks), not requiring complex pre- and post-conditions management. Experimental results on a large set of synthetic examples and on three industrial boards substantiate the quality of the proposed approach.

An Expert CAD Flow for Incremental Functional Diagnosis of Complex Electronic Boards

Cristiana Bolchini;Elisa Quintarelli;Fabio Salice
2015-01-01

Abstract

Functional diagnosis for complex systems can be a very time-consuming and expensive task, trying to identify the source of an observed misbehavior. We propose an automatic incremental diagnostic methodology and CAD flow, based on data mining. It is a model-based approach that incrementally determines the tests to be executed to isolate the faulty component, aiming at minimizing the total number of executed tests, without compromising 100% diagnostic accuracy. The data mining engine allows for shorter test sequences with respect to other reasoningbased solutions (e.g., Bayesian belief networks), not requiring complex pre- and post-conditions management. Experimental results on a large set of synthetic examples and on three industrial boards substantiate the quality of the proposed approach.
2015
Bayesian networks, data mining (DM), expert systems, functional diagnosis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/991910
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