PLS-based EWMA fault detection strategy for process monitoring

Fouzi Harrou, Mohamed Nounou, Hazem Nounou, Muddu Madakyaru

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

Fault detection (FD) and diagnosis in industrial processes is essential to ensure process safety and maintain product quality. Partial least squares (PLS) has been used successfully in process monitoring because it can effectively deal with highly correlated process variables. However, the conventional PLS-based detection metrics, such as the Hotelling's T2 and the Q statistics are ill suited to detect small faults because they only use information from the most recent observations. Other univariate statistical monitoring methods, such as the exponentially weighted moving average (EWMA) control scheme, has shown better abilities to detect small faults. However, EWMA can only be used to monitor single variables. Therefore, the main objective of this paper is to combine the advantages of the univariate EWMA and PLS methods to enhance their performances and widen their applicability in practice. The performance of the proposed PLS-based EWMA FD method was compared with that of the conventional PLS FD method through two simulated examples, one using synthetic data and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS, especially in the presence of faults with small magnitudes.

Original languageEnglish
Pages (from-to)108-119
Number of pages12
JournalJournal of Loss Prevention in the Process Industries
Volume36
DOIs
Publication statusPublished - 1 Jul 2015

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Keywords

  • Data-based fault detection
  • Distillation columns
  • EWMA control scheme
  • Partial least squares
  • Small faults
  • Statistical fault detection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Food Science
  • Chemical Engineering(all)
  • Safety, Risk, Reliability and Quality
  • Energy Engineering and Power Technology
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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