Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 284910, 12 pages
Research Article

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

1Department of Statistics and Information Science, Fu Jen Catholic University, Hsinchuang, New Taipei City 24205, Taiwan
2Department of Industrial Management, Chien Hsin University of Science and Technology, Taoyuan County, Zhongli 32097, Taiwan

Received 23 March 2012; Accepted 30 July 2012

Academic Editor: Alexei Mailybaev

Copyright © 2012 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 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.