Recognition of control chart patterns using genetic algorithm and support vector machine

Control chart patterns are the important statistical process control tools for determining whether a process is run in its intended mode or in the presence of unnatural patterns. Accurate recognition of control chart patterns is essential for efficient system monitoring to maintain the high-quality products. This paper presents efficient automated method for control chart patterns recognition. In this method we have used a multi-class support vectors machine (SVM) based classifier is proposed to determine the membership of patterns. In order to find the best model of SVMs, we have used a genetic algorithm for optimization. Numerical results show that the proposed method has high accuracy.