Multivariate process parameter change identification by neural network

  • Farzaneh AhmadzadehEmail author
  • Jan Lundberg
  • Thomas Strömberg


Whenever there is an out-of-control signal in process parameter control charts, maintenance engineers try to diagnose the cause near the time of the signal which does not always lead to prompt identification of the source(s) of the out-of-control condition, and this in some cases yields to extremely high monetary loses for the manufacturer owner. This paper applies multivariate exponentially weighted moving average (MEWMA) control charts and neural networks to make the signal identification more effective. The simulation of this procedure shows that this new control chart can be very effective in detecting the actual change point for all process dimension and all shift magnitudes considered. This methodology can be used in manufacturing and process industries to predict change points and expedite the search for failure causing parameters, resulting in improved quality at reduced overall cost. This research shows development of MEWMA by usage of neural network for identifying the step change-point and the variable responsible for the change in the process mean vector.


Quality control Multivariate exponentially weighted moving average Artificial neural network Change point Monte Carlo simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ahmadzadeh F (2010) Change point detection with multivariate control charts by artificial neural network. Int J Adv Manuf Tech. doi: 10.1007/s00170-009-2193-6 Google Scholar
  2. 2.
    Atashgar K, Noorossana R (2010) An integrating approach to root cause analysis of a bivariate mean vector with a linear trend disturbance. Int J Adv Manuf Tech 25:407–420Google Scholar
  3. 3.
    Bersimis S, Psarakis S, Panaretos J (2007) Multivariate statistical process control chart: an overview. Qual Reliab Eng Int 23:517–543CrossRefGoogle Scholar
  4. 4.
    Cheng CS (1995) A multi-layered neural network model for detecting changes in the process mean. Comput Ind Eng 28:51–61CrossRefGoogle Scholar
  5. 5.
    Cheng CS (1997) A neural network approach for the analysis of control chart patterns. Int J Prod Res 35:667–697CrossRefzbMATHGoogle Scholar
  6. 6.
    Gazanfari M, Alaeddini A, Niaki STA, Aryanezhad MB (2008) A clustering approach to identify the time of a step change in Shewhart control charts. Qual Reliab Eng Int 24:765–778CrossRefGoogle Scholar
  7. 7.
    Guh RS, Shiue YR (2008) An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts. Comput Ind Eng 55:475–493CrossRefGoogle Scholar
  8. 8.
    Guh RS (2007) On-line identification and quantification of mean shifts in bivariate processes using a neural network-based approach. Qual Reliab Eng Int 23:367–385CrossRefGoogle Scholar
  9. 9.
    Hawkins DM, Qiu P (2003) The change point model for statistical process control. J Qual Technol 35:355–366Google Scholar
  10. 10.
    Hawkins DM, Zamba KD (2006) A multivariate change point model for statistical process control. Technometrics 48:539–549MathSciNetCrossRefGoogle Scholar
  11. 11.
    Hawkins DM (1993) Regression adjustment for variables in multivariate quality control. J Qual Technol 25:170–182Google Scholar
  12. 12.
    Hayter AJ, Tsui KL (1994) Identification and quantification in multivariate quality control problems. J Qual Technol 26:197–208Google Scholar
  13. 13.
    Hwarng HB (2004) Detecting process mean shift in the presence of autocorrelation: a neural network-based monitoring scheme. Int J Prod Res 42:573–595CrossRefzbMATHGoogle Scholar
  14. 14.
    Hwarng HB (2005) Simultaneous identification of mean shift and correlation change in AR (1) processes. Int J Prod Res 43:1761–1783CrossRefzbMATHGoogle Scholar
  15. 15.
    Hwarng HB (2008) Toward identifying the source of mean shifts in multivariate SPC: a neural network approach. Int J Prod Res 46:5531–5559CrossRefzbMATHGoogle Scholar
  16. 16.
    Kourti T, MacGregor JF (1996) Multivariate SPC methods for process and product monitoring. J Qual Technol 28:409–428Google Scholar
  17. 17.
    Li F, Runger GC, Tun E (2006) Supervised learning for change point detection. Int J Prod Res 44:2853–2868CrossRefzbMATHGoogle Scholar
  18. 18.
    Lowry C, Woodall WH, Champ C, Rigdon S (1992) A multivariate exponentially weighted moving average control chart. Technometrics 34:46–53CrossRefzbMATHGoogle Scholar
  19. 19.
    MacGregor J, Harris T (1993) The exponentially weighted moving variance. J Qual Technol 25:106–118Google Scholar
  20. 20.
    Montgomery DC (1991) Introduction to statistical quality control. John Wiley Sons, New YorkGoogle Scholar
  21. 21.
    Nedumaran G, Pignatiello JJ, Calvin JA (2000) Identifying the time of a step-change with χ2 control charts. Qual Eng 13:153–159CrossRefGoogle Scholar
  22. 22.
    Niaki STA, Abbasi B (2005) Fault diagnosis in multivariate control charts using artificial neural network. Qual Reliab Eng Int 21:825–840CrossRefGoogle Scholar
  23. 23.
    Nishina K (1992) A comparison of control charts from the viewpoint of change-point estimation. Qual Reliab Eng Int 8:537–541CrossRefGoogle Scholar
  24. 24.
    Noorossana R, Shademan A (2009) Estimating the change point of a normal process mean with a monotonic change. Qual Reliab Eng Int 25:79–90CrossRefGoogle Scholar
  25. 25.
    Nottingham QJ, Cook DF, Zobel CW (2001) Visualization of multivariate data with radial plots using SAS. Comput Ind Eng 41:17–35CrossRefGoogle Scholar
  26. 26.
    Perry MB, Pignatiello JJ, Simpson JR (2007) Estimation of the change point of the process fraction nonconforming with a monotonic change disturbance in SPC. Qual Reliab Eng Int 2:327–339CrossRefGoogle Scholar
  27. 27.
    Perry MB, Pignatiello JJ (2006) Estimation of the change point of a normal process mean with a linear trend disturbance. Qual Technol Quant Manag 3:325–334MathSciNetGoogle Scholar
  28. 28.
    Samuel TR, Pignatiello JJ (2001) Estimation of the change point of a normal process mean in SPC applications. J Qual Technol 33:82–95Google Scholar
  29. 29.
    Samuel TR, Pignatiello JJ (2001) Identifying the time of a step change in the process fraction nonconforming. Qual Eng 13:375–385Google Scholar
  30. 30.
    Sullivan JH, Woodall WH (2000) Change-point detection of mean vector or covariance matrix shifts using multivariate individual observation. IIE Trans 32:537–549Google Scholar
  31. 31.
    Wade MR, Woodall WH (1993) A review and analysis of cause selecting control charts. J Qual Technol 25:161–170Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Farzaneh Ahmadzadeh
    • 1
    Email author
  • Jan Lundberg
    • 2
  • Thomas Strömberg
    • 3
  1. 1.Division of operation and maintenanceLulea University of TechnologyLuleaSweden
  2. 2.Division of operation and maintenanceLulea University of TechnologyLuleaSweden
  3. 3.Division of mathematical sciencesLulea University of TechnologyLuleaSweden

Personalised recommendations