Diagnosing the Cause and its Timing of Changes in Multivariate Process Mean Vector from Quality Control Charts using Artificial Neural Network

Authors: Farzaneh Ahmadzadeh

Abstract:

Quality control charts are very effective in detecting out of control signals but when a control chart signals an out of control condition of the process mean, searching for a special cause in the vicinity of the signal time would not always lead to prompt identification of the source(s) of the out of control condition as the change point in the process parameter(s) is usually different from the signal time. It is very important to manufacturer to determine at what point and which parameters in the past caused the signal. Early warning of process change would expedite the search for the special causes and enhance quality at lower cost. In this paper the quality variables under investigation are assumed to follow a multivariate normal distribution with known means and variance-covariance matrix and the process means after one step change remain at the new level until the special cause is being identified and removed, also it is supposed that only one variable could be changed at the same time. This research applies artificial neural network (ANN) to identify the time the change occurred and the parameter which caused the change or shift. The performance of the approach was assessed through a computer simulation experiment. The results show that neural network performs effectively and equally well for the whole shift magnitude which has been considered.

Keywords: Artificial neural network, change point estimation, monte carlo simulation, multivariate exponentially weighted movingaverage

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1058523

References:

[1] TR. Samuel and JJ. Pignatiello , "Identifying the time of a step change in the process fraction nonconforming," Qual Eng, 3rd ed., vol 13, 2001, pp. 375-385.
[2] TR. Samuel TR and JJ. Pignatiello, "Estimation of the change point of a normal process mean in SPC applications," J Qual Technol , 1st ed , vol 33 , 2001 , pp.82-95.
[3] DM. Hawkins and P. Qiu, "The changepoint model for statistical process control," Qual Technol, 4th ed,vol 35, 2003, pp. 355-366.
[4] MB. Perry and PJJ. JR , "Estimation of the change point of a normal process mean with a linear trend disturbance," Qual Tech and Quantitative Manage, 3rd ed , vol3 , 2006 , pp. 325-334.
[5] MB. Perry , PJJ. JR and JR. Simpson , "Estimation of the change point of the process fraction nonconforming with a monotonic change disturbance in SPC," Qual Reliab Eng Int, 3rd ed , vol 23 , 2007,pp. 327-339.
[6] M. Gazanfari , A. Alaeddini , STA. Niaki and MB. Aryanezhad , "A clustering approach to identify the time of a step change in Shewhart control charts," Qual Reliab Eng Int ,7th ed , vol 24,2008, 24, pp.765- 778.
[7] R. Noorossana and A. Shademan , "Estimating the change point of a normal process mean with a monotonic change," Qual Reliab Eng Int ,vol 25, 2009 ,pp. 79-90.
[8] G. Nedumaran , JJ. Pignatiello and JA Calvin , "Identifying the time of a step-change with ¤ç2 control charts," Qual Eng, 2 nd ed , vol 13 , 2000, pp.153-159.
[9] D.C. Montgomery, Introduction to Statistical Quality Control, John Wiley SonsNew York, 1991.
[10] MR. Wade and WH. Woodall, "A review and analysis of cause selecting control charts," J Qual Technol, 3rd ed, vol 25 ,1993 ,pp. 161-170.
[11] DM. Hawkins, "Regression adjustment for variables in multivariate quality control," J Qual Technol, 3rd ed , vol 25 , 1993, pp.170-182 .
[12] AJ. Hayter and KL Tsui, "Identification and quantification in multivariate quality control problems," J Qual Technol, 3rd ed , vol 26 ,1994, pp.197-208 .
[13] T. Kourti and JF. MacGregor , "Multivariate SPC methods for process and product monitoring," J Qual Technol, 4rd ed vol 28 ,1996,pp. 409- 428.
[14] QJ. Nottingham , DF. Cook and CW. Zobel , "Visualization of multivariate data with radial plots using SAS," Comput Ind Eng, vol 41 ,2001, pp.17-35.
[15] STA. Niaki and B. Abbasi, "Fault diagnosis in multivariate control charts using artificial neural network," Qual Reliab Eng Int, 8th ed , vol 21 ,2005, pp. 825-840.
[16] RS. Guh , "On-line identification and quantification of mean shifts in bivariate processes using a neural network-based approach," Qual Reliab Eng Int, 3rd ed, vol 23,2007, pp.367-385.
[17] HB. Hwarng, "Detecting process mean shift in the presence of autocorrelation: a neural network based monitoring scheme," Int J Prod Res, 3rd ed, vol 42, 2004,pp.573-595.
[18] HB. Hwarng, "Simultaneous identification of mean shift and correlation change in AR(1) processes," Int J Prod Res ,9 th ed , vol 43 ,2005,pp.1761-1783.
[19] Hb. Hwarng, "Toward identifying the source of mean shifts in multivariate SPC: a neural network approach," Int J Prod Res ,vol 46 2008,pp.5531-5559.
[20] S. Bersimis , S. Psarakis and J. Panaretos , "Multivariate statistical process control chart: an overview," Qual Reliab Eng Int , 5th ed , vol 23 ,2007,pp.517-543.
[21] C. Lowry and W. Woodall, "C. Champ, S. Rigdon, A multivariate exponentially weighted moving average control chart," Technometrics, vol 34 , 1992,pp. 46-53.
[22] J. MacGregor and T. Harris, "The exponentially weighted moving variance," Journal Qual Technol, 1993,pp.106-118.
[23] RS. Guh and YR. Shiue, "An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts," Comp Indust Eng ,2nd ed , vol 55, 2008 ,pp.475-493.
[24] K. Nishina, "A Comparison of Control Charts From the Viewpoint of Change-Point Estimation," Qual Reliab Eng Int, vol 8 ,1992 , pp. 537- 541.
[25] JH. Sullivan and WH. Woodall, "Change-point detection of mean vector or covariance matrix shifts using multivariate individual observation," IIE Trans, 6 th ed , vol 32 ,2000, pp.537-549.
[26] F. Li , GC. Runger and E. Tun , "Supervised learning for changepoint detection," Int J Prod Res, vol 44, 2006, pp.2853-2868.
[27] DM. Hawkins and KD Zamba, "A multivariate change point model for statistical process control," Technometrics, 4th ed ,vol 48, 2006,pp.539- 549.
[28] K. Atashgar and R. Noorossana, "An integrating approach to root cause analysis of a bivariate mean vector with a linear trend disturbance," Int J Adv Manuf Technol, vol 25 ,2010, pp.407-420.
[29] F. Ahmadzadeh, "Change point detection with multivariate control charts by artificial neural network," Int J Adv Manuf Technol, 2010, pp.1-12.
[30] C.S. Cheng , "A multi-layered neural network model for detecting changes in the process mean," Comput Ind Eng , vol 28, 1995, pp. 51- 61.
[31] C. S. Cheng , "A neural network approach for the analysis of control chart patterns ," Int J Prod Res , 1997, pp.667-697.