KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2582
Online Publication Date2017-01-20
Print Publication Date2016-07
Permanent link to this recordhttp://hdl.handle.net/10754/622826
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AbstractFault 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.
CitationHarrou F, Sun Y (2016) PLS-based memory control scheme for enhanced process monitoring. 2016 IEEE 14th International Conference on Industrial Informatics (INDIN). Available: http://dx.doi.org/10.1109/INDIN.2016.7819208.
SponsorsThis publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.