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Korean Journal of Chemical Engineering

, Volume 30, Issue 6, pp 1181–1186 | Cite as

Fault detection in nonlinear chemical processes based on kernel entropy component analysis and angular structure

  • Qingchao Jiang
  • Xuefeng YanEmail author
  • Zhaomin Lv
  • Meijin Guo
Process Systems Engineering, Process Safety

Abstract

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.

Key words

Kernel Entropy Component Analysis Process Monitoring Fault Detection Angular Structure 

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Copyright information

© Korean Institute of Chemical Engineers, Seoul, Korea 2013

Authors and Affiliations

  • Qingchao Jiang
    • 1
  • Xuefeng Yan
    • 1
    Email author
  • Zhaomin Lv
    • 1
  • Meijin Guo
    • 2
  1. 1.Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of EducationEast China University of Science and TechnologyShanghaiP. R. China
  2. 2.State Key Laboratory of Bioreactor EngineeringEast China University of Science and TechnologyShanghaiP. R. China

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