Situation prediction based on fuzzy clustering for industrial complex processes
Résumé
Prediction of process behavior is important and useful to understand the system status and to take early control actions during operation. This paper presents a fuzzy clustering approach for predicting situations (functional states) in complex process industries. The proposed methodology combines a static measurement, such as the result of a fuzzy classifier trained with historical process data, and an estimation algorithm based on Markov‘s theory for discrete event systems. The situation prediction function is integrated into a process monitoring system without increasing the computational cost, which makes real-time implementation feasible. The monitoring strategy includes two principal stages: an offline stage for designing the fuzzy classifier and the predictor, and an online stage for identifying current process situations and for estimating predicted functional states. Thus, at each sample time, the results of a fuzzy classifier are used as inputs in the prediction procedure. An attractive feature of our proposed method, for situation prediction, is that it provides information about the evolution of the process. The proposed approach was tested on a monitoring system for a power transmission line, and also for monitoring a boiler subsystem of a steam generator. Experimental results indicate that our proposed technique in this paper is effective and can be used as a tool, for operators, to be used in industrial process decision making.