A configurable board-level adaptive incremental diagnosis technique based on decision trees

Abstract

Functional diagnosis for complex electronic boards is a time-consuming task that requires big expertise to the diagnosis engineers. In this paper we propose a new engine for board-level adaptive incremental functional diagnosis based on decision trees. The engine incrementally selects the tests that have to be executed and based on the test outcomes it automatically stops the diagnosis as soon as one or more faulty candidates can be identified, thus allowing to reduce the number of executed tests. Moreover, we propose a configurable early stop condition for the engine that allows to further reduce the number of executed tests leveraging the diagnosis accuracy. The effectiveness of the proposed approach has been assessed using a set of synthetic but realistic boards and three industrial boards.