Process monitoring approach using fast moving window PCA

Xun Wang, Uwe Kruger, George Irwin

Research output: Contribution to journalArticlepeer-review

241 Citations (Scopus)


This paper introduces a fast algorithm for moving window principal component analysis (MWPCA) which will adapt a principal component model. This incorporates the concept of recursive adaptation within a moving window to (i) adapt the mean and variance of the process variables, (ii) adapt the correlation matrix, and (iii) adjust the PCA model by recomputing the decomposition. This paper shows that the new algorithm is computationally faster than conventional moving window techniques, if the window size exceeds 3 times the number of variables, and is not affected by the window size. A further contribution is the introduction of an N-step-ahead horizon into the process monitoring. This implies that the PCA model, identified N-steps earlier, is used to analyze the current observation. For monitoring complex chemical systems, this work shows that the use of the horizon improves the ability to detect slowly developing drifts.
Original languageEnglish
Pages (from-to)5691-5702
Number of pages12
JournalIndustrial & Engineering Chemistry Research
Issue number15
Publication statusPublished - 20 Jul 2005

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Environmental Science(all)
  • Polymers and Plastics


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