Multivariate Pattern Recognition in MSPC Using Bayesian Inference

Jose Ruiz-Tamayo, Jose Antonio Vazquez-Lopez, Edgar Augusto Ruelas-Santoyo, Aidee Hernandez-Lopez, Ismael Lopez-Juarez and Armando Javier Rios-Lira
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Jose Ruiz-Tamayo: Tecnologico Nacional de Mexico/Instituto Tecnologico de Celaya, Celaya 38010, Mexico
Jose Antonio Vazquez-Lopez: Tecnologico Nacional de Mexico/Instituto Tecnologico de Celaya, Celaya 38010, Mexico
Edgar Augusto Ruelas-Santoyo: Instituto Tecnologico Superior de Irapuato, Irapuato 36821, Mexico
Aidee Hernandez-Lopez: Sistema Avanzado de Bachillerato y Educacion Superior, Celaya 38010, Mexico
Ismael Lopez-Juarez: Centro de Investigacion y de Estudios Avanzados del IPN (CINVESTAV), Ramos Arizpe 25900, Mexico
Armando Javier Rios-Lira: Tecnologico Nacional de Mexico/Instituto Tecnologico de Celaya, Celaya 38010, Mexico

Mathematics, 2021, vol. 9, issue 4, 1-18

Abstract: Multivariate Statistical Process Control (MSPC) seeks to monitor several quality characteristics simultaneously. However, it has limitations derived from its inability to identify the source of special variation in the process. In this research, a proposed model that does not have this limitation is presented. In this paper, data from two scenarios were used: (A) data created by simulation and (B) random variable data obtained from the analysed product, which in this case corresponds to cheese production slicing process in the dairy industry. The model includes a dimensional reduction procedure based on the centrality and data dispersion. The goal is to recognise a multivariate pattern from the conjunction of univariate variables with variation patterns so that the model indicates the univariate patterns from the multivariate pattern. The model consists of two stages. The first stage is concerned with the identification process and uses Moving Windows ( MWs ) for data segmentation and pattern analysis. The second stage uses Bayesian Inference techniques such as conditional probabilities and Bayesian Networks. By using these techniques, the univariate variable that contributed to the pattern found in the multivariate variable is obtained. Furthermore, the model evaluates the probability of the patterns of the individual variables generating a specific pattern in the multivariate variable. This probability is interpreted as a signal of the performance of the process that allows to identify in the process a multivariate out-of-control state and the univariate variable that causes the failure. The efficiency results of the proposed model compared favourably with respect to the results obtained using the Hotelling’s T 2 chart, which validates our model.

Keywords: Multivariate Statistical Process Control; control charts; Bayesian Network; Bayesian Inference; moving windows (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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