Multivariate Pattern Recognition in MSPC Using Bayesian Inference

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 T2 chart, which validates our model.

A simple procedure to improve the performance of Klein's 2‐of‐2 run rule in the Shewhart X¯$\bar{X}$ control chart

“…Different types of run rules, as described in Shmueli and Cohen, 8 Champ, 9 and Walker et al, 10 have also been proposed and compared in Palm. 11 More recently, researchers such as Rocha et al, 12 Kim and Cho, 13 Adeoti and Malela-Majika, 14 Tran, 15 Ruiz-Tamayo et al, 16 Malela-Majika et al, 17 and Karavigh and Amiri 18 have also suggested different complex run rules aiming to improve the performance of control charts. Jalilibal et al 19 performs a recent literature review on run rules schemes for statistical process monitoring.…”