Premium
PARAMO: Enhanced Data Pre‐processing in Batch Multivariate Statistical Process Control
Author(s) -
FuentesGarcía Marta,
GonzálezMartínez José María,
MaciáFernández Gabriel,
Camacho José
Publication year - 2019
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3188
Subject(s) - normalization (sociology) , computer science , batch processing , data processing , multivariate statistics , process (computing) , statistical process control , data mining , trajectory , nonlinear system , variance (accounting) , machine learning , accounting , quantum mechanics , astronomy , sociology , anthropology , programming language , operating system , physics , business
Since the pioneering works by Nomikos and MacGregor, the Batch Multivariate Statistical Process Control (BMSPC) methodology has been extensively revised, and a sheer number of alternative monitoring approaches have been suggested. The different approaches vary in the batch data alignment, the pre‐processing approach, the data arrangement, and/or the type of model used, from two‐way to three‐way and from linear to nonlinear. One of the most accepted pre‐processing schemes, referred to as the trajectory centering and scaling (TCS), is based on the normalization to zero mean and unit variance around the average trajectory. However, the main drawback of TCS is the inherent increase of the level of uncertainty in the estimation of model parameters. In this work, we illustrate how to improve parameter estimation while maintaining the good properties of this pre‐processing approach. This enhancement is achieved with the new pre‐processing approach we call PARAMO, which uses more observations than TCS to estimate the pre‐processing parameters. We show that this improvement favorably impacts the performance of the monitoring system. The results of this research work affect a large amount of the monitoring approaches proposed to date, and we advocate that the pre‐processing procedure proposed here should be generally applied in BMSPC.