Multivariate statistical diagnosis using triangular representation of fault patterns in principal component space

H. W. Cho, K. J. Kim, M. K. Jeong

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

A pattern‐based multivariate statistical diagnosis method is proposed to diagnose a process fault on‐line. A triangular representation of process trends in the principal component space is employed to extract the on‐line fault pattern. The extracted fault pattern is compared with the existing fault patterns stored in the fault library. A diagnostic decision is made based on the similarity between the extracted and the existing fault patterns, called a similarity index. The diagnosis performance of the proposed method is demonstrated using simulated data from Tennessee Eastman process. The diagnosis success rate and robustness to noise of the proposed method are also discussed via computational experiments.

Original languageEnglish (US)
Pages (from-to)5181-5198
Number of pages18
JournalInternational Journal of Production Research
Volume43
Issue number24
DOIs
StatePublished - Dec 2005
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Keywords

  • Diagnosis
  • On-line monitoring
  • PCA
  • Similarity index
  • Triangular representation

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