A study on the various features for effective control chart pattern recognition

The identification of various unnatural patterns that are usually exhibited in quality control charts leads to more focussed diagnosis and, thereby, significantly minimises the effort towards effective troubleshooting. Feature-based control chart pattern (CCP) recognition systems have the advantage that the users can easily understand how a particular pattern is identified. Pham and Wani have presented a feature-based heuristic approach for CCP recognition which can differentiate six types of CCPs, based on the extraction of nine shape features. The extraction of some of these features requires users’ inputs and, thus, this CCP recognition system is not truly automated. Moreover, many real-life situations require detection of all of the eight basic CCPs, including stratification and systematic patterns. The extraction of the features after the scaling of pattern data into an (0, 1) interval can ensure that the magnitudes of the features are independent of the mean and standard deviation of the underlying process. But the distinction between normal and stratification patterns is lost when the pattern data are scaled. A CCP recogniser that will identify a stratification pattern, therefore, needs to be developed using unscaled pattern data. In this paper, potentially useful 32 features, the extraction of which do not require users’ inputs of any form, are proposed. The magnitudes of these features are independent of the process mean and are considerably insensitive to changes in the process standard deviation. An easy mechanism for the determination of the optimal set of features and a heuristic is also presented. Sensitivity studies on the performance of the heuristic show that it is robust against the estimation error of the process mean and is reasonably robust against the estimation error of the process standard deviation. Thus, it has enough potential for use in real-time process control applications.