화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Korean Journal of Chemical Engineering, Vol.30, No.6, 1181-1186, June, 2013
DOI10.1007/s11814-013-0034-7  Export Citation
Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure
Qingchao Jiang, Xuefeng Yan†, Zhaomin Lv, and Meijin Guo1
  • Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
  • 1State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, P. R. China
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Considering that kernel entropy component analysis (KECA) is a promising new method of nonlinear data transformation and dimensionality reduction, a KECA based method is proposed for nonlinear chemical process monitoring. In this method, an angle-based statistic is designed because KECA reveals structure related to the Renyi entropy of input space data set, and the transformed data sets are produced with a distinct angle-based structure. Based on the angle difference between normal status and current sample data, the current status can be monitored effectively. And, the confidence limit of the angle-based statistics is determined by kernel density estimation based on sample data of the normal status. The effectiveness of the proposed method is demonstrated by case studies on both a numerical process and a simulated continuous stirred tank reactor (CSTR) process. The KECA based method can be an effective method for nonlinear chemical process monitoring.
Keywords:Kernel Entropy Component Analysis;Process Monitoring;Fault Detection;Angular Structure
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