Using Combination of Optimized Recurrent Neural Network with Design of Experiments and Regression for Control Chart Forecasting

Authors: R. Behmanesh, I. Rahimi

Abstract:

recurrent neural network (RNN) is an efficient tool for modeling production control process as well as modeling services. In this paper one RNN was combined with regression model and were employed in order to be checked whether the obtained data by the model in comparison with actual data, are valid for variable process control chart. Therefore, one maintenance process in workshop of Esfahan Oil Refining Co. (EORC) was taken for illustration of models. First, the regression was made for predicting the response time of process based upon determined factors, and then the error between actual and predicted response time as output and also the same factors as input were used in RNN. Finally, according to predicted data from combined model, it is scrutinized for test values in statistical process control whether forecasting efficiency is acceptable. Meanwhile, in training process of RNN, design of experiments was set so as to optimize the RNN.

Keywords: RNN, DOE, regression, control chart.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079164

References:

[1] D. MONTGOMERY, "Control estadístico de la calidad", 2004, 3rd Ed. Limusa Wiley, Mexico, pp.797.
[2] J. A. Vazquez-Lopez, I. Lopez-Juarez, M. Pe├▒a-Cabrera, " On the Use of the Fuzzy ARTMAP Neural Network for Pattern Recognition in Statistical Process Control using a Factorial Design ", Int. J. of Computers, Communications & Control, ISSN 1841-9836, Vol. V (2010), No. 2, pp. 205-215.
[3] A.J. MORRIS, G.A. MONTAGUE, M.J. WILLIS, "Artificial neural networks : studies in process modeling and control", 1994, Chem. Eng. Res. Des. 72(A1), pp. 3-19.
[4] D.F. COOK, C.T. RAGSDALE, R.L. MAJOR, "Combining a neural network with a genetic algorithm for process parameter optimization", 2000, Eng.Appl.Artif. Intell. 13, pp. 391-396.
[5] L.R. MedsKer, L.C. Jain, "Recurrent Neural Networks : Design and Application", CRC Press, Boca Raton London New York Washington, D.C., 2001.
[6] L.G. Esteban, F. García Fernández, P. de Palacios, M. Conde, "Artificial neural networks in variable process control: application in particleboard manufacture", Sistemas y Recursos Forestales 2009 18(1), pp. 92-100.
[7] D.F. COOK, C.C. CHIU, "Predicting the internal bond strength of particleboard: utilizing a radial basis function neural network", 1997, Eng. Appl. Artif. Intell. 10 (2), pp. 171-177.
[8] S. MALINOV, W. SHA, J.J. MCKEOWN, "Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network", 2001, Comput. Mater. Sci. 21, pp. 375-394.
[9] W. Laosiritaworn, N. Chotchaithanakorn, "Artificial Neural Networks Parameters Optimization with Design of Experiments: An Application in Ferromagnetic Materials Modeling", Chiang Mai J. Sci. 2009; 36(1), pp.83-91.
[10] M.S. Packianather, P.R. Drake, H. Rowland, "Optimizing the Parameters of Multilayered Feed Forward Neural Networks through Taguchi Design of Experiments", Qual. Reliab. Eng. Int., 2000; 16: pp. 461-473.