@misc{10481/55293,
year = {2018},
month = {1},
url = {http://hdl.handle.net/10481/55293},
abstract = {Multivariate Statistical Process Control (MSPC) based on Principal Component
Analysis (PCA) is a well-known methodology in chemometrics that is aimed at testing whether an industrial process is under Normal Operation Conditions (NOC).
As a part of the methodology, once an anomalous behaviour is detected, the root
causes need to be diagnosed to troubleshoot the problem and/or avoid it in the
future. While there have been a number of developments in diagnosis in the past
decades, no sound method for comparing existing approaches has been proposed.
In this paper, we propose such a procedure and use it to compare several diagnosis
methods using randomly simulated data and from realistic data sources. This is a
general comparative approach that takes into account factors that have not previously been considered in the literature. The results show that univariate diagnosis
is more reliable than its multivariate counterpart.},
keywords = {MSPC},
keywords = {diagnosis},
keywords = {Contribution Plots},
keywords = {PCA},
keywords = {Networkmetrics},
keywords = {Smearing},
title = {Evaluation of Diagnosis Methods in PCA-based Multivariate Statistical Process Control},
doi = {https://doi.org/10.1016/j.chemolab.2017.12.008},
author = {Fuentes García, Noemí Marta and Macía Fernández, Gabriel and Camacho Páez, José},
}