Diagnosis of Out-of-Control Signals in Complex Manufacturing Processes
Some new perspectives of applications of advanced data-driven modeling in control and fault diagnosis of manufacturing processes are presented. The time-series analysis can help to identify and isolate autocorrelations in the process, being a source of misleading conclusions about the process disturbances. Learning systems such artificial neural networks and classification trees can be used in identification of non-standard out-of-control signals. Finding the root-cases of the process disturbances can be facilitated using advanced models linking the process inputs and process outputs.
The authors would like to thank prof. Jan Jezierski and prof. Jan Szajnar for the permission to use the copyrighted material from our three papers which appeared in Archives of Foundry Engineering.
We would also like to thank prof. Witold Bialy for the permission to use the copyrighted material from our chapter in the monograph “Systems Supporting Production Engineering”, published by PA NOVA SA. Gliwice, Poland in 2013 (ISBN 978-83-937845-0-9).
- 1.Stanley, G.M.: Guide to Fault Detection and Diagnosis. White Paper available from: http://gregstanleyandassociates.com/whitepapers/FaultDiagnosis/faultdiagnosis.htm (2013)
- 3.Jiang, W., Farr, J.V.: Integrating SPC and EPC methods for quality improvement. Qual. Technol. Quant. Manag. 4/2007, 345–363, StatSoft Inc. (2014)Google Scholar
- 4.Statistica (data analysis software system) v.12. www.statsoft.com. Accessed 13 Apr 2016
- 5.Hoyer, R.W., Ellis, W.C.: A Graphical Exploration of SPC Part 2: the probability structure of rules for interpreting control charts. Qual. Prog. 29(5), 57–64 (1996)Google Scholar
- 6.Alwan, L.C., Roberts, H.V.: Time-series modelling for statistical process control. J. Bus. Econ. Stat. 6(1), 87–95 (1988)Google Scholar
- 7.Price, B., Price, K., et al.: SPC modifications for continuous autocorrelated processes. Manuf. Rev. 5, 184–192 (1992)Google Scholar
- 13.Chiu, C.C., Shao, Y.E., et al.: Identification of process disturbance using SPC/EPC and neural networks. J. Intell. Manuf. 14, 379–388 (2003)Google Scholar
- 14.Shao, Y.E., Wu, C.H., et al.: Identifying the change point of a process with the integration of SPC charts and neural networks. In: Proceedings of the Second International Conference on Innovative Computing, Information and Control ICICIC ‘07 Kumamoto Japan, pp. 400–403 (2007)Google Scholar
- 16.Perzyk, M., Kozlowski, J., et al.: Application of computational intelligence methods in control and diagnosis of production processes. In: Kazmierczak, J. (ed.), Systems Supporting Production Engineering Gliwice Poland: PA NOVA SA, pp. 104–125 (2013)Google Scholar
- 17.Masters, T.: Practical Neural Network Recipes in C++, pp. 248–249. Academic Press (1993)Google Scholar
- 18.Perzyk, M., Krawiec, K., et al.: Application of time-series analysis in foundry production. Arch. Foundry Eng. 9(3), 109–114 (2009)Google Scholar
- 19.Perzyk, M., Maciejak, S., et al.: Application of time-series analysis for prediction of molding sand properties in production cycle. Arch. Foundry Eng. 11(2), 95–100 (2011)Google Scholar
- 20.Perzyk, M., Rodziewicz, A.: Application of time-series analysis in control of chemical composition of grey cast iron. Arch. Foundry Eng. 12(4), 171–175 (2012)Google Scholar
- 21.Green, M.E.: Critical Assessment of Current Metalcasting Green Sand System Control and Monitoring Processes (A Thesis in Industrial Engineering). The Pennsylvania State University, The Graduate School Harold and Inge Marcus Department of Industrial and Manufacturing Engineering (2009)Google Scholar
- 23.Jacobs, D.A., Luke, S.R.: Training artificial neural networks for statistical process control. In: Proceedings of the Tenth Biennial University/Government/Industry Microelectronics Symposium, pp. 235–239. https://doi.org/10.1109/ugim.1993.297059