{"title":"Control Chart Pattern Recognition Using Wavelet Based Neural Networks","authors":"Jun Seok Kim, Cheong-Sool Park, Jun-Geol Baek, Sung-Shick Kim","volume":72,"journal":"International Journal of Computer and Information Engineering","pagesStart":1717,"pagesEnd":1722,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/416","abstract":"
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.<\/p>\r\n","references":"[1] D. C. Montgomery, Introduction to Statistical Quality Control, 5th ed.\r\nJohn Wiley & Sons, 2004.\r\n[2] J. Guo, S. Guo, and X. Yu, \"Monitoring and diagnosis of manufacturing\r\nprocess using extreme learning machine,\" Advanced Science Letters,\r\nvol. 4, no. 6-7, pp. 6-7, 2011.\r\n[3] I. Masood and A. Hassan, \"Statistical features-ann recognizer for bivariate\r\nprocess mean shift pattern recognition,\" in Intelligent and Advanced\r\nSystems (ICIAS), 2010 International Conference on. IEEE, 2010, pp.\r\n1-6.\r\n[4] A. Hassan, M. Baksh, A. Shaharoun, and H. Jamaluddin, \"Improved spc\r\nchart pattern recognition using statistical features,\" International Journal\r\nof Production Research, vol. 41, no. 7, pp. 1587-1603, 2003.\r\n[5] J. Yang and M. Yang, \"A control chart pattern recognition system using\r\na statistical correlation coefficient method,\" Computers & Industrial\r\nEngineering, vol. 48, no. 2, pp. 205-221, 2005.\r\n[6] Y. Al-Assaf, \"Recognition of control chart patterns using multiresolution\r\nwavelets analysis and neural networks,\" Computers & Industrial\r\nEngineering, vol. 47, no. 1, pp. 17-29, 2004.\r\n[7] C. H. Wang and W. Kuo, \"Identification of control chart patterns using\r\nwavelet filtering and robust fuzzy clustering,\" Journal of Intelligent\r\nManufacturing, vol. 18, no. 3, pp. 343-350, 2007.\r\n[8] K. Assaleh and Y. Al-assaf, \"Features extraction and analysis for\r\nclassifying causable patterns in control charts,\" Computers & industrial\r\nengineering, vol. 49, no. 1, pp. 168-181, 2005.\r\n[9] H. P. Cheng and C. S. Cheng, \"Control chart pattern recognition using\r\nwavelet analysis and neural networks,\" Journal of Quality Vol, vol. 16,\r\nno. 5, p. 311, 2009.\r\n[10] D. Pham and A. Chan, \"Control chart pattern recognition using a new\r\ntype of self-organizing neural network,\" Proceedings of the Institution\r\nof Mechanical Engineers, Part I: Journal of Systems and Control\r\nEngineering, vol. 212, no. 2, pp. 115-127, 1998.\r\n[11] R. T. Ogden, Essential Wavelets for Statistical Applications and Data\r\nAnalysis. Philadelphia: SIAM, 1992.\r\n[12] W. Melssen, R. Wehrens, and L. Buydens, \"Supervised kohonen networks\r\nfor classification problems,\" Chemometrics and Intelligent Laboratory\r\nSystems, vol. 83, no. 2, pp. 99-113, 2006.\r\n[13] L. Fausett, Fundamentals of Neural Networks. Prentice Hall, 1993.\r\n[14] J.-J. Yoon, C.-S. Park, J. S. Kim, and J.-G. Baek, \"Recognition of control\r\nchart pattern using bi-directional kohonen network and artificial neural\r\nnetwork,\" Journal of the Korea Society for Simulation, vol. 20, no. 4,\r\npp. 115-125, 2011.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 72, 2012"}