Control Chart Pattern Recognition Using Wavelet Based Neural Networks

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, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.

• Jun Seok Kim
• Cheong-Sool Park
• Jun-Geol Baek
• Sung-Shick Kim

• Control chart pattern recognition
• Multi-resolution wavelet analysis
• Bi-directional Kohonen network
• Back-propagation network
• Feature extraction.

[1] D. C. Montgomery, Introduction to Statistical Quality Control, 5th ed.
John Wiley & Sons, 2004.
[2] J. Guo, S. Guo, and X. Yu, "Monitoring and diagnosis of manufacturing
process using extreme learning machine," Advanced Science Letters,
vol. 4, no. 6-7, pp. 6-7, 2011.
[3] I. Masood and A. Hassan, "Statistical features-ann recognizer for bivariate
process mean shift pattern recognition," in Intelligent and Advanced
Systems (ICIAS), 2010 International Conference on. IEEE, 2010, pp.
1-6.
[4] A. Hassan, M. Baksh, A. Shaharoun, and H. Jamaluddin, "Improved spc
chart pattern recognition using statistical features," International Journal
of Production Research, vol. 41, no. 7, pp. 1587-1603, 2003.
[5] J. Yang and M. Yang, "A control chart pattern recognition system using
a statistical correlation coefficient method," Computers & Industrial
Engineering, vol. 48, no. 2, pp. 205-221, 2005.
[6] Y. Al-Assaf, "Recognition of control chart patterns using multiresolution
wavelets analysis and neural networks," Computers & Industrial
Engineering, vol. 47, no. 1, pp. 17-29, 2004.
[7] C. H. Wang and W. Kuo, "Identification of control chart patterns using
wavelet filtering and robust fuzzy clustering," Journal of Intelligent
Manufacturing, vol. 18, no. 3, pp. 343-350, 2007.
[8] K. Assaleh and Y. Al-assaf, "Features extraction and analysis for
classifying causable patterns in control charts," Computers & industrial
engineering, vol. 49, no. 1, pp. 168-181, 2005.
[9] H. P. Cheng and C. S. Cheng, "Control chart pattern recognition using
wavelet analysis and neural networks," Journal of Quality Vol, vol. 16,
no. 5, p. 311, 2009.
[10] D. Pham and A. Chan, "Control chart pattern recognition using a new
type of self-organizing neural network," Proceedings of the Institution
of Mechanical Engineers, Part I: Journal of Systems and Control
Engineering, vol. 212, no. 2, pp. 115-127, 1998.
[11] R. T. Ogden, Essential Wavelets for Statistical Applications and Data
Analysis. Philadelphia: SIAM, 1992.
[12] W. Melssen, R. Wehrens, and L. Buydens, "Supervised kohonen networks
for classification problems," Chemometrics and Intelligent Laboratory
Systems, vol. 83, no. 2, pp. 99-113, 2006.
[13] L. Fausett, Fundamentals of Neural Networks. Prentice Hall, 1993.
[14] J.-J. Yoon, C.-S. Park, J. S. Kim, and J.-G. Baek, "Recognition of control
chart pattern using bi-directional kohonen network and artificial neural
network," Journal of the Korea Society for Simulation, vol. 20, no. 4,
pp. 115-125, 2011.