Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process
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 processe...
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doaj-9b50f74d0dbb4ba0803a0bbd2d6d29ef2020-11-24T20:49:14ZengHindawi-WileyComplexity1076-27871099-05262017-01-01201710.1155/2017/23230822323082Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC ProcessYuehjen E. Shao0Po-Yu Chang1Chi-Jie Lu2Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, TaiwanDepartment of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, TaiwanDepartment of Industrial Management, Chien Hsin University of Science and Technology, Zhongli, Taoyuan County 32097, TaiwanThe 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.http://dx.doi.org/10.1155/2017/2323082 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yuehjen E. Shao Po-Yu Chang Chi-Jie Lu |
spellingShingle |
Yuehjen E. Shao Po-Yu Chang Chi-Jie Lu Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process Complexity |
author_facet |
Yuehjen E. Shao Po-Yu Chang Chi-Jie Lu |
author_sort |
Yuehjen E. Shao |
title |
Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process |
title_short |
Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process |
title_full |
Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process |
title_fullStr |
Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process |
title_full_unstemmed |
Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process |
title_sort |
applying two-stage neural network based classifiers to the identification of mixture control chart patterns for an spc-epc process |
publisher |
Hindawi-Wiley |
series |
Complexity |
issn |
1076-2787 1099-0526 |
publishDate |
2017-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2017/2323082 |
work_keys_str_mv |
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1716806370612740096 |