Fuzzy Evidential Approximate Reasoning Scheme for fault diagnosis of complex processes
Résumé
Supervision of nonlinear and complex processes is of great importance to industries as a means of achieving improved productivity and stable product quality. The advanced model-based condition monitoring methodologies, can contribute significantly to the achievement of these objectives. In this paper, a fuzzy evidential model based fault detection and diagnosis method is presented. The multi-model based symptom generation procedure is used to detect changes of the current process behavior. The diagnosis task is accomplished by an evidential approximate reasoning scheme to handle different kinds of uncertainty that are inherently present in many real word processes. The validity of the method is illustrated on the well-known benchmark of three tanks and different faults can be detected and isolated continuously, over all ranges of operation.