Back-propagation pattern recognizers for X¯ control charts: methodology and performance
Computers and Industrial Engineering
Automated unnatural pattern recognition on control charts using correlation analysis techniques
Computers and Industrial Engineering
A neural network based model for abnormal pattern recognition of control charts
Computers and Industrial Engineering
Control chart pattern recognition using semi-supervised learning
ACS'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Computer Science - Volume 7
Control chart pattern recognition using a novel hybrid intelligent method
Applied Soft Computing
Applying ICA and SVM to mixture control chart patterns recognition in a process
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Pattern Recognition Letters
Computers and Industrial Engineering
Computers and Industrial Engineering
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This paper presents a control chart pattern recognition system using a statistical correlation coefficient method. Pattern recognition techniques have been widely applied to identify unnatural patterns in control charts. Most of them are capable of recognizing a single unnatural pattern for different abnormal types. However, before an unnatural pattern occurs, a change point from normal to abnormal may appear at any point in control charts for most practical cases. Moreover, concurrent patterns where two unnatural patterns simultaneously exist may also occur in a control chart pattern recognition system. Our statistical correlation coefficient approach is a simple mechanism for recognizing these unnatural control chart patterns with good performance. This approach is also an effective method for the control chart pattern recognition without a tedious training process.