Application of Nonlinear PCA for Fault Detection in Polymer Extrusion Processes

Xueqin Liu, Kang Li, Marion McAfee, Jing Deng

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

This paper describes the application of an improved nonlinear principal component analysis (PCA) to the detection of faults in polymer extrusion processes. Since the processes are complex in nature and nonlinear relationships exist between the recorded variables, an improved nonlinear PCA, which incorporates the radial basis function (RBF) networks and principal curves, is proposed. This algorithm comprises two stages. The first stage involves the use of the serial principal curve to obtain the nonlinear scores and approximated data. The second stage is to construct two RBF networks using a fast recursive algorithm to solve the topology problem in traditional nonlinear PCA. The benefits of this improvement are demonstrated in the practical application to a polymer extrusion process.
Original languageEnglish
Pages (from-to)1141-1148
Number of pages8
JournalNeural Computing and Applications
Volume20
Issue number6
DOIs
Publication statusPublished - Sep 2011

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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