Applying ICA and SVM to mixture control chart patterns recognition in a process

Authors:
Chi-Jie Lu;Yuehjen E. Shao;Chao-Liang Chang
Affiliations:
Department of Industrial Engineering and Management, Ching Yun University, Jung-Li, Taoyuan, Taiwan, R.O.C;Department of Statistics and Information Science, Fu Jen Catholic University, Hsinchuang, Taipei County, Taiwan, R.O.C;Department of Statistics and Information Science, Fu Jen Catholic University, Hsinchuang, Taipei County, Taiwan, R.O.C
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Year:
2011

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Abstract

Mixture control chart patterns (CCPs) mixed by two types of basic CCPs together usually exist in the real manufacture process. However, most existing studies are considered to recognize the single abnormal CCPs. This study utilizes independent component analysis (ICA) and support vector machine (SVM) for recognizing mixture CCPs recognition in a process. The proposed scheme, firstly, uses ICA to the monitoring process data containing mixture patterns for generating independent components (ICs). The undetectable basic patterns of the mixture patterns can be revealed in the estimated ICs. The ICs are then used as the input variables of the SVM for building CCP recognition model. Experimental results revealed that the proposed scheme is promising for recognizing mixture control chart patterns in a process.