A fuzzy-logic based fault diagnosis strategy for process control loops

By considering the fault propagation behaviors in process systems with control loops, a fuzzy-logic based fault diagnosis strategy has been developed in the present work. The proposed fault diagnosis methods can be implemented in two stages. In the off-line preparation stage, the fault origins of a system hazard are identified by determining the minimal cut sets of the corresponding fault tree. The fault propagation patterns in a feedback loop are obtained on the basis of system digraph. The occurrence order of observable symptoms caused by each fault origin is derived accordingly and then encoded into a set of IF-THEN diagnosis rules. In the next on-line diagnosis stage, the occurrence indices of the top event and also the fault origins are computed in a fuzzy inference system based on real-time measurement data. Simulation studies have been carried out to demonstrate the feasibility of the proposed approach.