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.