In view of the fact that the production process of Polyvinyl chloride (PVC) polymerization has more fault types and its type is complex, a fault diagnosis algorithm based on the hybrid Dynamic Kernel Principal Component Analysis-Fisher Discriminant Analysis (DKPCA-FDA) method is proposed in this paper. Kernel principal component analysis and Dynamic Kernel Principal Component Analysis are used for fault diagnosis of Polyvinyl chloride (PVC) polymerization process, while Fisher Discriminant Analysis (FDA) method was adopted to make failure data for further separation. The simulation results show that the Dynamic Kernel Principal Component Analyses to fault diagnosis of Polyvinyl chloride (PVC) polymerization process have better diagnostic accuracy, the Fisher Discriminant Analysis (FDA) can further realize the fault isolation, and the actual fault in the process of Polyvinyl chloride (PVC) polymerization production can be monitored by Dynamic Kernel Principal Component Analysis.

Abstract. In traditional FPN model,the matrix operations are too excessive and it does not meet the requirements of real-time in fault diagnosis system.The paper has improved the FPN model and proposed a CFPN(Controlled Fuzzy Petri Net) modeling method.This method has added controlled place in FPN and improved the design method of control arc of the control place.The method is based on the principle of p-and it enables to simplify the logic of FPN fault diagnosis.That can reduce the matrix computation in FPN fault diagnosis reasoning to improve the efficiency of fault diagnosis.This paper has applied the method in the distillation column fault diagnosis system and verified the applicability and effectiveness of fault diagnosis method based on CFPN model.