Automated unnatural pattern recognition on control charts using correlation analysis techniques

Pattern recognition techniques are currently pursued to identify unnatural patterns on quality control charts. This approach has been shown to enhance the ability to utilize the information of the chart more effectively than conventional run rules. This paper presents analysis and development of a pattern recognition system for identifying unnatural patterns on quality control charts. The system is based on correlation analysis, where a set of optimal matched filters are generated. To illustrate the design methodology and operation of the system, a set of commonly encountered patterns is utilized, such as the trend, the systematic, and the cyclic patterns. A training algorithm that minimizes the probabilities of Type I and Type II errors is presented. To evaluate the system performance, a testing algorithm as well as a set of newly-defined performance measures are introduced. The obtained results, based on extensive simulation runs, have proved the effectiveness of correlation analysis for control chart pattern recognition. (C) 1997 Elsevier Science Ltd.