Recognition of Concurrent Control Chart Patterns in Autocorrelated Processes Using Support Vector Machine

Abstract

Control chart pattern recognition (CCPR) is an important issue in statistical process control because unnatural control chart patterns (CCPs) exhibited on control charts can be associated with specific causes that adversely affect the manufacturing processes. In recent years, many machine learning techniques have been successfully applied to CCPR. However, such existing research for CCPR has mostly been developed for identification of basic CCPs (Shift Patterns, Trend Patterns, Cyclic Pattern and Systematic Pattern). Little attention has been given to the identification of concurrent CCPs (two or more basic CCPs occurring simultaneously) which are commonly encountered in practical manufacturing processes. In addition, these existing researches also assume the process data are independently and identically distributed which may not be appropriate for certain manufacturing processes. This study proposes a support vector machine (SVM) approach to identify concurrent CCPsfor a multivariate process with autocorrelated observations which can be characterized by afirst order autoregressive (AR(1)) model. The numerical results indicate that the proposed model can effectively identify two concurrent identical CCPs but for those cases involving one trend pattern and one shift pattern, their recognition accuracy deteriorates to around 20% to 50% depending on the autocorrelation coefficients used in the data model.