Process monitoring using independent component analysis based on gradient and newton iteration methods

Abstract

Data-driven fault detection technique has been widely applied for process monitoring, which can effectively detect faults happened in industrial processes. It is extremely significant for guaranteeing the normal operation of processes. Independent Component Analysis (ICA), a type of Data-driven fault detection technique, has been successfully applied to Blind Source Separation and process fault detection. There exist two classical methods, Gradient and Newton Iteration Methods, to determine independent components. ICA based on Newton Iteration Method has been well investigated and successfully applied into practice. However, ICA based on Gradient Method gains less attention. In order to give a comparison between Gradient and Newton Iteration Method based ICA in process monitoring, principles of both methods are demonstrated in detail. Moreover, numerical simulations are finished in the platform of Tennessee Eastman(TE) process. Results show that efficiency of process monitoring of ICA based on Gradient Method is better for TE process.