Diagnosis of condition systems using diagnostic causal networks

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 this paper, a system failure is defined in terms of observed behavior versus expected behavior, where the expected behavior is defined through condition system models. A. diagnosis of this failure localizes the subsystem that is the source of the discrepancy between output and expected observations. We show that the structure of the interacting subsystems define a diagnostic causal model that captures the causal structure of subsystem dependencies. The diagnostic causal model can then be used to determine a set of subsystems that might be the source of a failure