Fault Detection Analysis Before and After Dynamic Model Reforming on the Benchmark Tennessee Eastman Process

Abstract

To make the simulation object closer to the actual chemical process, the author improved the model of Tennessee Eastman (TE) process in their previous work. In this paper, based on above improved model, to further analyse its effect on the fault detection performance, a research on fault data of the improved model and original model is done, based on principle component analysis (PCA), the detection rate of Hotelling's T2 statistic, Q statistic and support vector machine (SVM) integrated particle swarm optimization (PSO) approach are used to reflect their detection performance on the two models. From the detection rates, the detection performance gets worse when detecting the fault of the improved model. The analysis indicates that when the detection methods are used to detect the faults in actual chemical process, the detection performance will be influenced and may not be as effective as described in literature.