Probability measure on regular languages and its application in fault diagnosis for discrete event systems

In fault diagnosis for discrete event systems, it is very common that a diagnosis result may contain several fault candidates because of the insufficient number of sensors in the system. In this paper we discuss how to construct a probability space over a regular language and perform probabilistic reasoning to reduce diagnostic ambiguity.