Intelligent Recognition Research of Control Charts Patterns

Author

Yang, Jing

Author_Institution

Electron. Eng. Depts., East China Jiaotong Univ., Nanchang, China

fYear

2009

fDate

11-13 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

Control chart is one of important tools for on-line quality control. It is most difficult to identify unnatural patterns which are associated with a specific set of assignable causes on quality control charts. This paper discusses about control charts patterns recognition, and proposes a method for feature extraction from control chart based on principal component analysis(PCA). First, the principal component analysis is used to pre-process the sample data. Meanwhile, three methods were used to recognise control charts patterns: an improved backpropagation algorithm, PCA_BP and PCA_SVM. Simulation indicates that PCA_SVM is most effective.

Keywords

backpropagation; control charts; pattern recognition; principal component analysis; quality control; support vector machines; PCA backpropagation; PCA support vector machine; control charts pattern recognition; intelligent recognition; neural network; on-line quality control; principal component analysis; Backpropagation algorithms; Control charts; Manufacturing processes; Neural networks; Pattern recognition; Principal component analysis; Process control; Quality control; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4507-3

Electronic_ISBN

978-1-4244-4507-3

Type

conf

DOI

10.1109/CISE.2009.5366198

Filename

5366198