Decentralized diagnosis of discrete event systems using unconditional and conditional decisions

The past decade has witnessed the development of a body of theory, with associated applications, for fault diagnosis of dynamic systems that can be modeled in a discrete event systems framework. This paper first discusses the dual problem of diagnosing the absence of faults in centralized and decentralized settings. The paper then develops new definitions of decentralized diagnosis in the context of a general decentralized architecture that allows for the use of "conditional decisions" by local diagnosers. The properties of these new definitions of decentralized diagnosability are presented and their relationship with existing work discussed. Corresponding verification algorithms are also described.