Integrating rough sets and situation-based qualitative models for processes monitoring considering vagueness and uncertainty
A precise process monitoring is fundamental to guarantee the correct operation of a technical process. The required monitoring applications are frequently based on models of the correct system behavior. However, the development of precise process models is very time-consuming and expensive, if at all possible, due to the complexity of real process plants. In this paper a modeling approach is presented, which is based on the qualitative description of the process states in complex technical systems, and incorporates vague and uncertain information about the industrial process that otherwise would be discarded or ignored during the modeling, as a way of enriching the information in the model, without increasing its size. The proposed method integrates principles of the Rough Set Theory and Stochastic Automata in the Situation-based Qualitative Modeling and Analysis method. The interplay of these three techniques allows the development of compact but precise models of complex industrial systems, and therefore enables a closer monitoring of complex real systems.
rough sets qualitative modeling vagueness and uncertainty stochastic automata knowledge representation model-based reasoning fault detection and diagnosis