Closed-loop fault diagnosis based on a nonlinear process model and automatic fuzzy rule generation
In this contribution a new approach for fault detection and diagnosis (FDD) for nonlinear processes is presented. A nonlinear fuzzy model with transparent inner structure is used for the generation of relevant symptoms. The resulting symptom patterns are classified with a new self-learning classification structure based on fuzzy rules. The approach is successfully applied to an electro-pneumatic valve in a closed control loop.
fault diagnosis fault detection fuzzy model fuzzy classification closed-loop supervision