Diagnosing Time-Varying Misbehavior: an Approach Based on Model Decomposition

The analysis of time-varying systems is attracting a lot of attention in the model-based diagnosis community. In this paper we propose an approach to the diagnosis of such systems, relying on a component-oriented model; we provide separately a behavioral model, that is, knowledge about the consequences of differentbehavioral modes of the components, and a model of the possible temporal evolution of such modes (mode transition graphs). In the basic approach, we assume that the consequences of behavioral modes are instantaneous with respect to the transition between two modes; this allows us to decompose the solution of a temporal diagnostic problem into two subtasks: determining solutions of atemporal problems in different time points and assembling the solution of the temporal problem from those of the atemporal ones. Most of the definitions and machinery developed for static diagnosis can be re-used in such a framework. We then consider the consequences of some extensions. Even allowing for very simple temporal relations in the behavioral model leads to a more complex interference between reasoning on the behavioral models and the consistency check with respect to possible temporal evolutions. We also briefly analyze the case of adding quantitative temporal knowledge or probabilistic knowledge to the mode transition graphs