Study On Industrial Process Monitoring With Independent Component Analysis

ICA (Independent Component Analysis) is introduced into the field of process industry as a data analysis method, which is a signal decomposing technique based on the higher-order statistical information. This method can utilize the statistical characteristics of the variables more efficiently. The intrinsic characteristics of the process can be described through the decomposing of the monitoring variables under the meanings of the statistical independence.In this paper, after a brief introduction to the development history and the current research status and applications of ICA, simple mathematical preliminaries in ICA technique were given, including the mathematical definition of ICA, the assumptions made about ICA problems and the mathematical theory and methods commonly used in ICA, etc. Then, some algorithms and applications of ICA in process control were investigated. MICA is studied briefly in the paper, which is to monitor the batch industry process system.This paper simulates based on Tennessee Eastman Process (TEP) and continuous stirred tank reactor (CSTR). Lastly, a multiple ICA models based process monitoring methodology and its online applications are then studied. The simulation experiment shows the feasibility and effectiveness of this algorithm, which has the better utility value in application.