Uncertain dynamic process monitoring using moving window PCA for interval-valued data

M. Faouzi Harkat, Tarek Ait-Izem, Frédéric Kratz, Majdi Mansouri, Mohamed Nounou, Hazem Nounou

Research output: Contribution to journalConference article

Abstract

In this paper, we present a new process monitoring approach for uncertain, or highly noisy systems, which is based on the well known Moving Window Principal Component Analysis (MWPCA) extended to the interval case. We propose to use The Midpoints-Radii PCA (MRPCA) for modelling, which independently exploits two PCAs on the center and radius matrices of the system’s sensor interval-valued data. Furthermore, by changing the size and the shift of the window, Both center and radius model parameters are updated on-line; thus deriving a new Moving Window Midpoints-Radii PCA (MWMRPCA) approach. Based on the updated MWMRPCA, an interval SPE statistic and its control limit are calculated and updated through time, and are used for monitoring the state of the process. The performances of the proposed approach is illustrated by an application to the detection of faults on the Tennesse Eastman Process (TEP).

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume2289
Publication statusPublished - 1 Jan 2018
Event29th International Workshop on Principles of Diagnosis, DX 2018 - Warsaw, Poland
Duration: 27 Aug 201830 Aug 2018

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Process monitoring
Principal component analysis
Statistics
Monitoring
Sensors

Keywords

  • Dynamic Systems
  • Fault detection
  • Interval Data
  • Midpoints-Radii PCA
  • Moving Window PCA
  • SPE statistic

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Uncertain dynamic process monitoring using moving window PCA for interval-valued data. / Harkat, M. Faouzi; Ait-Izem, Tarek; Kratz, Frédéric; Mansouri, Majdi; Nounou, Mohamed; Nounou, Hazem.

In: CEUR Workshop Proceedings, Vol. 2289, 01.01.2018.

Research output: Contribution to journalConference article

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AU - Nounou, Mohamed

AU - Nounou, Hazem

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N2 - In this paper, we present a new process monitoring approach for uncertain, or highly noisy systems, which is based on the well known Moving Window Principal Component Analysis (MWPCA) extended to the interval case. We propose to use The Midpoints-Radii PCA (MRPCA) for modelling, which independently exploits two PCAs on the center and radius matrices of the system’s sensor interval-valued data. Furthermore, by changing the size and the shift of the window, Both center and radius model parameters are updated on-line; thus deriving a new Moving Window Midpoints-Radii PCA (MWMRPCA) approach. Based on the updated MWMRPCA, an interval SPE statistic and its control limit are calculated and updated through time, and are used for monitoring the state of the process. The performances of the proposed approach is illustrated by an application to the detection of faults on the Tennesse Eastman Process (TEP).

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