Online process monitoring using a new PCMD index

Ines Jaffel, Okba Taouali, Mohamed-Faouzi Harkat, Hassani Messaoud

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

This paper proposes a new online principal component analysis (PCA) index-based parameter estimation approach to detect a sensor fault. The proposed index is based on PCA technique and Mahalanobis distance and it is entitled principal component Mahalanobis distance (PCMD). The principle of the proposed PCMD is to detect a disagreement between the reference PCA model parameter that represent a normal system function and the PCA model parameter that estimated online to represent current system behavior. Indeed, the PCMD index evaluate the Mahalanobis distance between the principal components (PCs) of the reference PCA model and the new PCs that represent the current function of the system. These PCs are determined online using a Moving Window PCA technique (MWPCA). To evaluate performances of the proposed index, PCMD is applied on a numerical example and a chemical reactor (CSTR), and the results are satisfactory compared to other index such as SPCA and Spca λ

Original languageEnglish
Pages (from-to)947-957
Number of pages11
JournalInternational Journal of Advanced Manufacturing Technology
Volume80
Issue number5-8
DOIs
Publication statusPublished - 1 Sep 2015
Externally publishedYes

    Fingerprint

Keywords

  • Mahalanobis distance
  • MWPCA
  • PCA
  • PCMD
  • Sensor fault

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Cite this