A Novel Framework for Integrating Discrete Event System Control and Diagnosis

Diagnosis of discrete event systems (DES) is an important task, with several desiderata that include the ability to (1) encode both control and diagnosis properties within a single representation, and (2) to compose system models from component models in a simple and efficient manner, e.g., without the kind of state explosion that can occur during parallel composition of component Finite State Machines (FSMs). Many formalisms have been proposed for the Model-Based Diagnosis (MBD) of a DES, but each has drawbacks. The representations based on FSMs, e.g., [Sampath et al., 1995], have a clear control semantics but suffer from incompletelyspecified diagnostic semantics and state explosion during model composition. MBD representations, e.g., [Darwiche, 1998], have been used to model DESs using space-efficient compositional methods [Darwiche and Provan, 1996], but lack well-developed control-theoretic specifications. This article proposes a framework based on FSMs that has a clear control and diagnostics semantics, yet enables model composition without state-space explosions. We propose a two-level modeling framework, based on a system hypergraph H that can serve as a generator for MBD and FSM models. H specifies the component causal relations using the graphical framework described within causal networks (CN) [Darwiche, 1998], whereby we can clearly specify independence relations for system variables, and as a consequence compose system models with system state spaces limited through independence relations. Our representation is not significantly more complex than that of either FSM or CN alone, yet is equivalent to both these representations. We introduce to MBD modeling additional requirements for specifying control transitions, and to FSM modeling requirements on state descriptions and causal independence of components.