Recognition of control chart patterns using a neural network-based pattern recognizer with features extracted from correlation analysis

Control chart pattern analysis is a crucial task in statistical process control. There are various types of nonrandom patterns that may appear on the control chart indicating that the process is out of control. The presence of nonrandom patterns manifests that a process is affected by assignable causes, and corrective actions should be taken. From a process control point of view, identification of nonrandom patterns can provide clues to the set of possible causes that must be searched; hence, the troubleshooting time could be reduced in length. In this paper, we discuss two implementation modes of control chart pattern recognition and introduce a new research issue concerning pattern displacement problem in the process of control chart analysis. This paper presents a neural network-based pattern recognizer with selected features as inputs. We propose a novel application of statistical correlation analysis for feature extraction purposes. Unlike previous studies, the proposed features are developed by taking the pattern displacement into account. The superior performance of the feature-based recognizer over the raw data-based one is demonstrated using synthetic pattern data.