Distributed model projection based transition processes recognition and quality-related fault detection

In this paper, a novel transition process identification algorithm based on distributed model projection (DMP) is proposed for clustering nonlinear transition data and monitoring the variations in the transition process. Compared to several alternative identification methods, the DMP algorithm considers both the correlations between variables and correlations between samples. Also, a framework is proposed to combine DMP algorithm and hierarchical clustering to derive an optimal clustering results through a large amount of individual trials of the DMP algorithm. Based on the offline classification results, a transition process is divided into several sub segments and each of them can be characterized by a stable model. Then the online identification and Monitoring methods are carried out based on the sub-models established in those segments. Finally, the Tennessee Eastman (TE) benchmark process is utilized to demonstrate the performance of the proposed process identification and monitoring strategy. Compared to previous works, the proposed algorithm is shown to be superior both in identification and monitoring.