Exploiting causal structure in the refined diagnosis of condition systems

Abstract

A condition system is a collection of Petri nets that interact with each other and the external environment through condition signals. Some of these condition signals may be unobservable. In previous work, fault diagnosis was defined in terms of observed behavior versus expected behavior of subsystem models, where the expected behavior is defined through condition system models, and approximate methods were presented for detection and diagnosis. We have also presented a method to determine a best possible diagnosis within the constraints of observability. However this method requires significant state space exploration. In this paper, we wish to exploit the causal structure imposed on the system by a partition of subsystem models in order to reduce (in certain situations) the amount of work required to perform a diagnosis.