Test-data volume optimization for diagnosis
Proceedings of the 49th Annual Design Automation Conference
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iAF2D (incremental Automatic Functional Fault Detective) is a methodology for the identification of the faulty component in a complex system using data collected from a test session. It is an incremental approach based on a Bayesian Belief Network, where the model of the system under analysis is extracted from a faulty signature description. iAF2D reduces time, cost and efforts during the diagnostic phase by implementing a step-by-step selection of the tests to be executed from the set of available tests. This paper focuses on the evolution of the BBN nodes probabilities, to define a stop criterion to interrupt the diagnosis process when additional test outcomes would not provide further useful information for identifying the faulty candidate. Methodology validation is performed on a set of experimental results.