Statistical Processes Monitoring Based on Improved ICA and SVDD

  • Lei Xie
  • Uwe Kruger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


An industrial process often has a large number of measured variables, which are usually driven by fewer essential variables. An improved independent component analysis based on particle swarm optimization (PSO-ICA) is presented to extract these essential variables. Process faults can be detected more efficiently by monitoring the independent components. To monitor the non-Gaussian distributed independent components obtained by PSO-ICA, the one-class SVDD (Support Vector Data Description) is employed to find the separating boundary between the normal operational data and the rest of independent component feature space. The proposed approach is illustrated by the application to the Tennessee Eastman challenging process.


Particle Swarm Optimization Independent Component Analysis Independent Component Independent Component Analysis Normal Operating Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lei Xie
    • 1
  • Uwe Kruger
    • 2
  1. 1.National Key Laboratory of Industrial Control Technology, Institute of Advanced Process ControlZhejiang UniversityHangzhouP.R. China
  2. 2.Intelligent Systems and Control Research GroupQueen’s UniversityBelfastU.K.

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