Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process
Joint Authors
Lu, Chi-Jie
Chang, Po-Yu
Shao, Yuehjen E.
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-22
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Shao, Yuehjen E.& Chang, Po-Yu& Lu, Chi-Jie. 2017. Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process. Complexity،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142636
Modern Language Association (MLA)
Shao, Yuehjen E.…[et al.]. Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process. Complexity No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1142636
American Medical Association (AMA)
Shao, Yuehjen E.& Chang, Po-Yu& Lu, Chi-Jie. Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process. Complexity. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142636
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142636