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

Process monitoring
Principal component analysis
Chemical reactors
Parameter estimation
Sensors

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

Online process monitoring using a new PCMD index. / Jaffel, Ines; Taouali, Okba; Harkat, Mohamed-Faouzi; Messaoud, Hassani.

In: International Journal of Advanced Manufacturing Technology, Vol. 80, No. 5-8, 01.09.2015, p. 947-957.

Research output: Contribution to journalArticle

Jaffel, Ines ; Taouali, Okba ; Harkat, Mohamed-Faouzi ; Messaoud, Hassani. / Online process monitoring using a new PCMD index. In: International Journal of Advanced Manufacturing Technology. 2015 ; Vol. 80, No. 5-8. pp. 947-957.
@article{4a0d46082d7f4654bbf4c930e74165bc,
title = "Online process monitoring using a new PCMD index",
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",
keywords = "Mahalanobis distance, MWPCA, PCA, PCMD, Sensor fault",
author = "Ines Jaffel and Okba Taouali and Mohamed-Faouzi Harkat and Hassani Messaoud",
year = "2015",
month = "9",
day = "1",
doi = "10.1007/s00170-015-7094-2",
language = "English",
volume = "80",
pages = "947--957",
journal = "International Journal of Advanced Manufacturing Technology",
issn = "0268-3768",
publisher = "Springer London",
number = "5-8",

}

TY - JOUR

T1 - Online process monitoring using a new PCMD index

AU - Jaffel, Ines

AU - Taouali, Okba

AU - Harkat, Mohamed-Faouzi

AU - Messaoud, Hassani

PY - 2015/9/1

Y1 - 2015/9/1

N2 - 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

AB - 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

KW - Mahalanobis distance

KW - MWPCA

KW - PCA

KW - PCMD

KW - Sensor fault

UR - http://www.scopus.com/inward/record.url?scp=84927546225&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84927546225&partnerID=8YFLogxK

U2 - 10.1007/s00170-015-7094-2

DO - 10.1007/s00170-015-7094-2

M3 - Article

VL - 80

SP - 947

EP - 957

JO - International Journal of Advanced Manufacturing Technology

JF - International Journal of Advanced Manufacturing Technology

SN - 0268-3768

IS - 5-8

ER -