Abstract

Formal diagnostic methods are emerging from the machine-learning research community and beginning to find application in Intel. In this paper we give an overview of these methods and the potential they show for improving diagnostic procedures in operational environments. We present an historical overview of Bayes networks and discuss how they can be applied to diagnosis. We then give an illustration of how they can model the faults in a vacuum subsystem of a manufacturing tool.

Citations