Jun Seok Kim and Cheong-Sool Park and Jun-Geol Baek and Sung-Shick Kim
Control Chart Pattern Recognition Using Wavelet Based Neural Networks
1717 - 1721
2012
6
12
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/416
https://publications.waset.org/vol/72
World Academy of Science, Engineering and Technology
Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multiresolution wavelet analysis is used to generate timeshape and timefrequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bidirectional Kohonen network to make reduced and robust information. Third, a backpropagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.
Open Science Index 72, 2012