Process situation assessment: From a fuzzy partition to a finite state machine
Process situation assessment plays a major role in supervision of complex systems. The knowledge of the system behavior is relevant to support operators in their decision tasks. For complex industrial processes such as chemical or petrochemical ones, most of supervision approaches are based on data acquisition techniques and specifically on clustering methods to cope with the difficulty of modeling the process. Consequently, the system behavior can be characterized by a state space partition. This way, situation assessment is performed online through the tracking of the system evolution from one class to another. Furthermore, a finite state machine that is a support tool for process operators is elaborated to model the system behavior. This article presents theoretical aspects according to which the intuition that the trajectory observation of a dynamical system by a sequence of classes, to which the actual state belongs, gives valuable information about the real behavior of the system is substantiated. Thus, practical aspects are developed on the state machine construction and illustrated by two simple applications in the domain of chemical processes.
situation assessment finite state machine fuzzy clustering