Recognizing mixture control chart patterns with independent component analysis and support vector machine

Abstract

Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control chats can be associated with certain assignable causes adversely affecting the process. Most of the existing studies assumed that the observed process data needed to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns which are mixed by two basic CCPs together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme first uses ICA to the mixture patterns for generating independent components (ICs). The hidden basic patterns of the mixture patterns could be discovered in these 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 able to effectively recognize mixture control chart patterns. © 2010 Springer-Verlag.