A Hybrid Learning-Based Model for Simultaneous Monitoring of Process Mean and Variance

Abstract

Unnatural patterns exhibited on process mean and variance control charts can be associated separately with different assignable causes. Quick and accurate knowledge of the type of control chart patterns (CCPs), either because of process mean or variance, can greatly facilitate identification of assignable causes. Over the past few decades, however, process mean and variance CCPs are seldom studied simultaneously in the statistical process control literature. This study proposes a hybrid learning-based model for simultaneous monitoring of process mean and variance CCPs. In this model, a self-organization map neural network-based quantization error control chart is responsible for detecting the out-of-control signals, a discrete particle swarm optimization-based selective ensemble of back-propagation networks is responsible for classifying the detected out-of-control signals into categories of mean and/or variance abnormality, and two discrete particle swarm optimization-based selective ensembles of learning vector quantization networks are responsible for further identifying the detected mean and variance out-of-control signals as one of the specific CCP types, respectively. Extensive simulations indicate that the proposed hybrid learning-based model outperforms other existing approaches in detecting mean and variance changes, while also capable of CCP recognition. In addition, a case study is conducted to demonstrate how the proposed hybrid learning-based model can function as an effective tool for monitoring mean and variance simultaneously. Copyright © 2013 John Wiley & Sons, Ltd.