Control chart pattern recognition using spectral clustering technique and support vector machine under gamma distribution

Abstract

Most studies on control chart patterns (CCPs) recognition assume that the measured values of process characteristic follow a normal distribution. However, by collecting large-size on-line datasets, it could be found that this assumption may not be tenable. In the present study, a method to combine spectral clustering technique with support vector machine (SVM) for recognizing CCPs under a gamma distribution, which is typically employed to represent various probability distributions, is proposed. Spectral clustering is a useful tool for segmenting the observation window to obtain the correct feature set. Multiple SVM classifiers are applied to recognize CCPs. Recognition accuracy and average run length (ARL) are indicators that are used to validate the CCP recognition ability of our proposed method. Based on the comparative studies, our proposed method in the recognition efficiency of most CCP types generally outperforms the Shewhart X¯ chart and EWMA chart with run rules. It is also noted that a more skewed underlying distribution always leads to a higher false alarm rate for our proposed method. A real-world case study of the tire manufacturing process is given to illustrate how our proposed method can be practically applied to identify and recognize the CCPs.