A nonparametric fault isolation approach through one-class classification algorithms

Seoung Bum Kim, Thuntee Sukchotrat, Sun Kyoung Park

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Multivariate control charts provide control limits for the monitoring of processes and detection of abnormal events so that processes can be improved. However, these multivariate control charts provide limited information about the contribution of any specific variable to the out-of-control alarm. Although many fault isolation methods have been developed to address this deficiency, most of these methods require a parametric distributional assumption that restricts their applicability to specific problems of process control and thus limits their broader usefulness. This study proposes a nonparametric fault isolation method based on a one-class classification algorithm that overcomes the limitation posed by the parametric assumption in existing fault isolation methods. The proposed approach decomposes the monitoring statistics obtained from a one-class classification algorithm into components that reflect the contribution of each variable to the out-of-control signal. A bootstrap approach is used to determine the significance of each variable. A simulation study is presented that examines the performance of the proposed method under various scenarios and to results are compared with those obtained using the T 2 decomposition method. The simulation results reveal that the proposed method outperforms the T2 decomposition method in non-normal distribution cases.

Original languageEnglish
Pages (from-to)505-517
Number of pages13
JournalIIE Transactions (Institute of Industrial Engineers)
Volume43
Issue number7
DOIs
Publication statusPublished - 2011 Jul 1

    Fingerprint

Keywords

  • Decomposition
  • Fault isolation
  • Multivariate statistical process control
  • Nonparametric
  • One-class classification algorithm

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this