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

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.