Evaluation of Diagnosis Methods in PCA-based Multivariate Statistical Process Control Fuentes García, Noemí Marta Macía Fernández, Gabriel Camacho Páez, José MSPC diagnosis Contribution Plots PCA Networkmetrics Smearing 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. 2019-04-01T06:32:19Z 2019-04-01T06:32:19Z 2018-01 journal article http://hdl.handle.net/10481/55293 https://doi.org/10.1016/j.chemolab.2017.12.008 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ open access Atribución-NoComercial-SinDerivadas 3.0 España