SOM-based visualization monitoring and fault diagnosis for chemical process

Abstract

Data-based fault diagnosis technology applied in chemical industry process has attracted great attention, in which the effective methods for visualizing the process variation are still challenging. The self-organizing map (SOM) is an unsupervised learning algorithm of neural network, which is presented to solve the visualization monitoring and fault diagnosis problem. The high-dimensional input space is mapped to the two-dimensional output space through training the large sample data sets of SOM method. Meanwhile, the fault data sets can be automatically clustering by SOM so that the faulty category information will be obtained. Distinguish between other methods, SOM can preserve the topological structure and the density distribution of original data, so the visualization of on-line process monitoring and fault diagnosis can be effectively realized. Then, the Iris data benchmark is used to test the clustering results of SOM algorithm. Finally, a case study of the Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the SOM-based visualization monitoring method.