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Volume 2017 (2017), Article ID 2323082, 10 pages
Research Article

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

1Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan
2Department of Industrial Management, Chien Hsin University of Science and Technology, Zhongli, Taoyuan County 32097, Taiwan

Correspondence should be addressed to Yuehjen E. Shao

Received 7 July 2017; Revised 6 September 2017; Accepted 19 September 2017; Published 22 October 2017

Academic Editor: Yanan Li

Copyright © 2017 Yuehjen E. Shao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The effective controlling and monitoring of an industrial process through the integration of statistical process control (SPC) and engineering process control (EPC) has been widely addressed in recent years. However, because the mixture types of disturbances are often embedded in underlying processes, mixture control chart patterns (MCCPs) are very difficult for an SPC-EPC process to identify. This can result in problems when attempting to determine the underlying root causes of process faults. Additionally, a large number of categories of disturbances may be present in a process, but typical single-stage classifiers have difficulty in identifying large numbers of categories of disturbances in an SPC-EPC process. Therefore, we propose a two-stage neural network (NN) based scheme to enhance the accurate identification rate (AIR) for MCCPs by performing dimension reduction on disturbance categories. The two-stage scheme includes a combination of a NN, support vector machine (SVM), and multivariate adaptive regression splines (MARS). Experimental results reveal that the proposed scheme achieves a satisfactory AIR for identifying MCCPs in an SPC-EPC system.