Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Xueqin Liu, Kang Li, Marion McAfee, George Irwin

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.
Original languageEnglish
Pages (from-to)1306-1317
Number of pages12
JournalJournal of Process Control
Issue number9
Publication statusPublished - Oct 2011

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Modelling and Simulation
  • Computer Science Applications


Dive into the research topics of 'Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description'. Together they form a unique fingerprint.

Cite this