Diagnosis of Active Systems with Abstract Observations and Compiled Knowledge

An active system (AS) is a discrete-event system (DES) with asynchronous behavior, which is represented by a network of components that are modeled as communicating automata. When being operated, an AS performs a trajectory within its behavior space, while generating a sequence of observations, namely a temporal observation. The model of the AS and a temporal observation are the two key ingredients of the diagnosis task, which aims to find out possible faulty behavior via abductive reasoning. Among other knowledge, such reasoning requires knowing what is observable and what is not. This essential distinction constitutes the observability of the AS. In the literature, the observability of a DES boils down to qualifying each state transition either as observable or unobservable, which contrasts with the way humans observe reality, typically by mapping a collection of observations to a single, abstract perception. Moreover, the occurrence of single state transitions is not necessarily what we can observe or what we want to observe for diagnosis purposes. This paper presents an extended notion of observability, where each observation is associated with a behavioral scenario rather than a single state transition, where a scenario is defined as a regular language on state transitions. To speed up the online diagnosis engine, specific diagnosis-oriented knowledge is compiled offline. Eventually, the diagnosis technique based on abstract observability is extended to cope with temporal uncertainty.