A Hybrid ICA-SVM Approach for Determining the Quality Variables at Fault in a Multivariate Process

Mathematical Problems in Engineering. 2012;2012 DOI 10.1155/2012/284910

 

Journal Homepage

Journal Title: Mathematical Problems in Engineering

ISSN: 1024-123X (Print); 1563-5147 (Online)

Publisher: Hindawi Publishing Corporation

LCC Subject Category: Technology: Engineering (General). Civil engineering (General) | Science: Mathematics

Country of publisher: Egypt

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Yuehjen E. Shao (Department of Statistics and Information Science, Fu Jen Catholic University, Hsinchuang, New Taipei City 24205, Taiwan)
Chi-Jie Lu (Department of Industrial Management, Chien Hsin University of Science and Technology, Taoyuan County, Zhongli 32097, Taiwan)
Yu-Chiun Wang (Department of Statistics and Information Science, Fu Jen Catholic University, Hsinchuang, New Taipei City 24205, Taiwan)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 26 weeks

 

Abstract | Full Text

The monitoring of a multivariate process with the use of multivariate statistical process control (MSPC) charts has received considerable attention. However, in practice, the use of MSPC chart typically encounters a difficulty. This difficult involves which quality variable or which set of the quality variables is responsible for the generation of the signal. This study proposes a hybrid scheme which is composed of independent component analysis (ICA) and support vector machine (SVM) to determine the fault quality variables when a step-change disturbance existed in a multivariate process. The proposed hybrid ICA-SVM scheme initially applies ICA to the Hotelling T2 MSPC chart to generate independent components (ICs). The hidden information of the fault quality variables can be identified in these ICs. The ICs are then served as the input variables of the classifier SVM for performing the classification process. The performance of various process designs is investigated and compared with the typical classification method. Using the proposed approach, the fault quality variables for a multivariate process can be accurately and reliably determined.