Using neural networks to detect the bivariate process variance shifts pattern

Authors:
Chuen-Sheng Cheng;Hui-Ping Cheng
Affiliations:
Department of Industrial Engineering and Management, Yuan-Ze University, Chung-Li, Taoyuan 320, Taiwan, ROC;Department of Business Innovation and Development, MingDao University, Pee-Tow, Changhua 52345, Taiwan, ROC
Venue:
Computers and Industrial Engineering
Year:
2011

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Abstract

Most of the research in statistical process control has been focused on monitoring the process mean. Typically, it is also important to detect variance changes as well. This paper presents a neural network-based approach for detecting bivariate process variance shifts. Some important implementation issues of neural networks are investigated, including analysis window size, number of training examples, sample size, training algorithm, etc. The performance of the neural network, in terms of the ARL and run length distribution, is compared with that of traditional multivariate control charts. Through rigorous evaluation and comparison, our research results show that the proposed neural network performs substantially better than the traditional generalized variance chart and might perform better than the adaptive sizes control charts in the case that the out-of-control covariance matrix is not known in advance.