Statistical-based monitoring of multivariate non-Gaussian systems

Xueqin Liu, L. Xie, U. Kruger, Timothy Littler, S. Wang

Research output: Contribution to journalArticlepeer-review

110 Citations (Scopus)
5 Downloads (Pure)


The monitoring of multivariate systems that exhibit non-Gaussian behavior is addressed. Existing work advocates the use of independent component analysis (ICA) to extract the underlying non-Gaussian data structure. Since some of the source signals may be Gaussian, the use of principal component analysis (PCA) is proposed to capture the Gaussian and non-Gaussian source signals. A subsequent application of ICA then allows the extraction of non-Gaussian components from the retained principal components (PCs). A further contribution is the utilization of a support vector data description to determine a confidence limit for the non-Gaussian components. Finally, a statistical test is developed for determining how many non-Gaussian components are encapsulated within the retained PCs, and associated monitoring statistics are defined. The utility of the proposed scheme is demonstrated by a simulation example, and the analysis of recorded data from an industrial melter.
Original languageEnglish
Pages (from-to)2379-2391
Number of pages13
JournalAIChE Journal
Issue number9
Publication statusPublished - Sept 2008

ASJC Scopus subject areas

  • Biotechnology
  • General Chemical Engineering
  • Environmental Engineering


Dive into the research topics of 'Statistical-based monitoring of multivariate non-Gaussian systems'. Together they form a unique fingerprint.

Cite this