A Process Monitoring Method Based on Multi-PCA Models

In order to solve the problem of fault diagnosis for nonlinear systems with correlative process variables and improve the precision of PCA models for fault detection and fault diagnosis, a fault diagnosis method based on multi-PCA models is presented. Hyper-ellipsoid bound clustering rules are adopted to classify the process data, multi-PCA models are then built up for process monitoring. SOFM network is used in fault diagnosis. Simulation results in fermentation process show that the method can give reasonable control limits and improve the precision in process monitoring, which illustrates the feasibility and effectiveness of the proposed method.