Diagnosing the Cause and its Timing of Changes in Multivariate Process Mean Vector from Quality Control Charts using Artificial Neural Network
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.
[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.
[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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:52236", author = "Farzaneh Ahmadzadeh", title = "Diagnosing the Cause and its Timing of Changes in Multivariate Process Mean Vector from Quality Control Charts using Artificial Neural Network", 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", volume = "5", number = "6", pages = "973-6", }
