Step change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation

Mohammad Reza Maleki,Seyed Meysam Mousavi,Amirhossein Amiri

doi:10.1007/s40092-015-0117-7

Mohammad Reza Maleki, Seyed Meysam Mousavi + Show 1 more

Open Access

https://doi.org/10.1007/s40092-015-0117-7

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Abstract

In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-control states can help the quality engineers to eliminate the assignable causes through proper corrective actions. In this paper, first we use an artificial neural network (ANN)-based method in the literature for detecting the variance shifts as well as diagnosing the sources of variation in the multivariate-attribute processes. Then, based on the quality characteristics responsible for the out-of-control state, we propose a modular model based on the ANN for estimating the time of step change in the multivariate-attribute process variability. We also compare the performance of the ANN-based estimator with the estimator based on maximum likelihood method (MLE). A numerical example based on simulation study is used to evaluate the performance of the estimators in terms of the accuracy and precision criteria. The results of the simulation study show that the proposed ANN-based estimator outperforms the MLE estimator under different out-of-control scenarios where different shift magnitudes in the covariance matrix of multivariate-attribute quality characteristics are manifested.

Highlights

In most statistical process control (SPC) applications, the time when a control scheme triggers an out-of-control signal does not indicate the actual time of change in the process
A modular artificial neural network (ANN)-based methodology for estimating the time when the out-of-control state is manifested in the multivariate-attribute processes was studied
Before designing the ANNs required for change point estimation in the process variability, a three-layered perceptron ANN was applied for detecting the variance shifts and identifying quality characteristics responsible for the out-of-control states

Summary

Introduction

In most statistical process control (SPC) applications, the time when a control scheme triggers an out-of-control signal does not indicate the actual time of change in the process In such situations, estimating the actual time when the fault is first manifested (called change point) is inevitable, because it can facilitate identifying the assignable causes by searching in a limited time interval. As one of the first methods in change point estimation of univariate processes, Samuel et al (1998) investigated the time of step changes in the X-bar control chart. Amiri et al (2014) developed a probabilistic neural network (PNN)-based procedure to estimate the variance change point in a univariate process with normal quality characteristic. Refer to the review paper provided by Amiri and Allahyari (2012)

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