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Monitoring Nonlinear Profiles Using Support Vector Machines

  • Javier M. Moguerza
  • Alberto Muñoz
  • Stelios Psarakis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

In this work we focus on the use of SVMs for monitoring techniques applied to nonlinear profiles in the Statistical Process Control (SPC) framework. We develop a new methodology based on Functional Data Analysis for the construction of control limits for nonlinear profiles. In particular, we monitor the fitted curves themselves instead of monitoring the parameters of any model fitting the curves. The simplicity and effectiveness of the data analysis method has been tested against other statistical approaches using a standard data set in the process control literature.

Keywords

Kernel methods Statistical Process Control Support Vector Machines 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Javier M. Moguerza
    • 1
  • Alberto Muñoz
    • 2
  • Stelios Psarakis
    • 3
  1. 1.University Rey Juan Carlos, Camino del Molino s/n, 28943 FuenlabradaSpain
  2. 2.University Carlos III, c/ Madrid 126, 28903 GetafeSpain
  3. 3.Athens Univ. of Econ. and Business, 76 Patission Str., 10434 AthensGreece

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