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2017 | 132 | 3 | 1054-1057
Article title

Analysis of Out of Control Signals in Multivariate Processes with Multilayer Neural Network

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EN
Abstracts
EN
Control charts that are used for monitoring the process and detecting the out-of-control signals are important tools for statistical process control. It is simple to estimate source(s) for out-of-control signals in the univariate process, whereas it is difficult to identify the source(s) in the multivariate processes. The reason is that these kinds of processes require monitoring and controlling of more than one quality characteristics simultaneously. In this study, the proposed model is expected to detect the source(s) for out-of-control signals without help of an expert in the process, by using a multilayer neural network. This model was implemented in furniture fasteners manufacturing. Time gain was obtained while detecting source(s) for out-of-control signals.
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Contributors
author
  • Sakarya University, Department of Industrial Engineering, Sakarya, Turkey
author
  • Sakarya University, Department of Industrial Engineering, Sakarya, Turkey
References
  • [1] M.-H. Shu, H.-C. Wu, Comput. Ind. Eng. 61, 676 (2011)
  • [2] D.C. Montgomery, Introduction to Statistical Quality Control, 6th ed., John Wiley & Sons, New York 2001
  • [3] H. Hotelling, in: Techniques of Statistical Analysis, Eds. C. Eisenhart, M.W. Hastay, W.A. Wallis, McGraw Hill, New York 1947, p. 111
  • [4] R.L. Mason, N.D. Tracy, Y.C. Young, J. Qual. Technol. 27, 99 (1995)
  • [5] F. Aparisi, A. Gerardo, J. Sanz, IIE Trans. 38, 647 (2006), doi: 10.1080/07408170600692200
  • [6] J. Li, J. Jin, J. Shi, J. Qual. Technol. 40, 46 (2008)
  • [7] M.R. Pina-Monarrez, Int. J. Eng.-Theory 20, 401 (2013)
  • [8] N.S. Agog, H.C. Dikko, O.E. Asiribo, Int. J. Innov. Res. Sci Eng. Tech. 3, 449 (2014)
  • [9] A.A. Akeem, A. Yahaya, O. Asiribo, Am. J. Theor. Appl. Stat. 4, 432 (2015), doi: 10.11648/j.ajtas.20150406.13
  • [10] B. Warner, M. Mısra, Am. Stat. 50, 284 (1996), doi: 10.2307/2684922
  • [11] R. Tuntas, Acta Phys. Pol. A 128, B-78 (2015), doi: 10.12693/APhysPolA.128.B-78
  • [12] E. Boutalbi, L. Ait Gougam, F. Mekideche-Chafa, Acta Phys. Pol. A 128, B-271 (2015), doi: 10.12693/APhysPolA.128.B-271
  • [13] M. Davraz, Ş. Kilinçarslan, H. Ceylan, Acta Phys. Pol. A 128, B-184 (2015), doi: 10.12693/APhysPolA.128.B-184
  • [14] Y. Özcanli, F. Kosovali Çavuş, M. Beken, Acta Phys. Pol. A 130, 444 (2016), doi: 10.12693/APhysPolA.130.444
  • [15] M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Addison Wesley, 2005
  • [16] S. Taghi, A. Niaki, B. Abbasi, Qual. Reliab. Eng. Int. 21, 825 (2005)
  • [17] C.-S. Cheng, H.-P. Cheng, Expert. Syst. Appl. 35, 198 (2008), doi: 10.1016/j.eswa.2007.06.002
  • [18] T.T. El-Midany, M.A. El-Baz, M.S. Abd-Elwahed, Expert. Syst. Appl. 37, 1035 (2008)
  • [19] T.-F. Li, S. Hu, Z.-Y. Wein, Z.-Q. Liao, Math. Probl. Eng. 2, 9 (2013)
  • [20] S. Du, J. Lv, L. Xi, Int. J. Prod. Res. 50, 6288 (2012), doi: 10.1080/00207543.2011.631596
  • [21] C.-H. Wang, T.-P. Dong, A. Kuo, J. Intell. Manuf. 20, 409 (2009), doi: 10.1007/s10845-008-0115-3
  • [22] X. Wang, in: Int. Conf. Computational Intelligence and Security, (2008), p. 238
  • [23] J. Yu, L. Xi, X. Zhou, Eng. Appl. Artif. Intel. 22, 141 (2009), doi: 10.1016/j.engappai.2008.05.009
  • [24] C.-J. Lu, Y.E. Shao, P.H. Li, Neurocomputing 74, 1908 (2011), doi: 10.1016/j.neucom.2010.06.036
  • [25] W.-A. Yang, J. Intell. Manuf. 26, 769 (2015), doi: 10.1007/s10845-013-0833-z
  • [26] I. Masood, A. Hassan, Int. J. Adv. Manuf. Technol. 9, 1201 (2013)
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-app132z3-iip065kz
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