Type
Conference PaperAuthors
Harrou, Fouzi
Sun, Ying

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2015-CRG4-2582Date
2017-01-20Online Publication Date
2017-01-20Print Publication Date
2016-07Permanent link to this record
http://hdl.handle.net/10754/622826
Metadata
Show full item recordAbstract
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.Citation
Harrou 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.Sponsors
This 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.Additional Links
http://ieeexplore.ieee.org/document/7819208/ae974a485f413a2113503eed53cd6c53
10.1109/INDIN.2016.7819208