Improved Process Monitoring Using Nonlinear Principal Component Models

David Antory, George W. Irwin, Uwe Kruger, Geoffrey McCullough

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.
Original languageEnglish
Pages (from-to)520-544
Number of pages25
JournalInternational Journal of Intelligent Systems
Issue number5
Publication statusPublished - May 2008

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering


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