PLS-based memory control scheme for enhanced process monitoring

Fouzi Harrou, Ying Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Fault detection is important for safe operation of various modern engineering systems. Partial least square (PLS) has been widely used in monitoring highly correlated process variables. Conventional PLS-based methods, nevertheless, often fail to detect incipient faults. In this paper, we develop new PLS-based monitoring chart, combining PLS with multivariate memory control chart, the multivariate exponentially weighted moving average (MEWMA) monitoring chart. The MEWMA are sensitive to incipient faults in the process mean, which significantly improves the performance of PLS methods and widen their applicability in practice. Using simulated distillation column data, we demonstrate that the proposed PLS-based MEWMA control chart is more effective in detecting incipient fault in the mean of the multivariate process variables, and outperform the conventional PLS-based monitoring charts.
Original languageEnglish (US)
Title of host publication2016 IEEE 14th International Conference on Industrial Informatics (INDIN)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages477-480
Number of pages4
ISBN (Print)9781509028702
DOIs
StatePublished - Jan 20 2017

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