AN INTEGRATED CONTROL CHART PATTERN RECOGNITION SYSTEM USING CORRELATION COEFFICIENT METHOD AND RBF NEURAL NETWORKS

摘要

Abnormal patterns in control charts are some unnatural causes for variations in statistical process control (SPC) and need to be eliminated so that control chart pattern recognition becomes important in SPC. Although pattern recognition techniques have been widely applied to identify abnormal patterns in control charts, a complete pattern recognition system should better have the abilities with pattern identification and parameter estimation. In this paper, we present an integrated control chart pattern recognition system which contains a correlation coefficient method for pattern identification and RBF neural networks for parameter estimation. We consider with concurrent patterns where two abnormal patterns may simultaneously occur in a control chart pattern recognition system. The correlation coefficient method is used for identification of abnormal control charts with single and concurrent abnormal patterns. RBF neural networks are then used for constructing the relation among input-output data so that they are adopted to accomplish parameter estimation for abnormal patterns. This integrated control chart pattern recognition system can be effectively used in real-time processing. We demonstrate their usefulness with several examples.