Abstract
In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residualsthe difference between the actual value and the fitted valuewith the traditional SPC approach. However, this residuals-based approach requires two steps: (1) finding the residuals; and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful information about the targeted process. The main purpose of this article is to examine the feasibility of using one-class classification-based control charts to handle multivariate and autocorrelated processes. The article uses simulated data to present an analysis and comparison of one-class classification-based control charts and the traditional Hotelling's T2 chart.
| Original language | English |
|---|---|
| Pages (from-to) | 461-474 |
| Number of pages | 14 |
| Journal | Communications in Statistics: Simulation and Computation |
| Volume | 39 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2010 Mar 1 |
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Keywords
- Autocorrelated multivariate process
- Data mining algorithm
- One-class classification algorithm
- Statistical process control
ASJC Scopus subject areas
- Modelling and Simulation
- Statistics and Probability
Cite this
One-class classification-based control charts for monitoring autocorrelated multivariate processes. / Kim, Seoung Bum; Jitpitaklert, Weerawat; Sukchotrat, Thuntee.
In: Communications in Statistics: Simulation and Computation, Vol. 39, No. 3, 01.03.2010, p. 461-474.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - One-class classification-based control charts for monitoring autocorrelated multivariate processes
AU - Kim, Seoung Bum
AU - Jitpitaklert, Weerawat
AU - Sukchotrat, Thuntee
PY - 2010/3/1
Y1 - 2010/3/1
N2 - In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residualsthe difference between the actual value and the fitted valuewith the traditional SPC approach. However, this residuals-based approach requires two steps: (1) finding the residuals; and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful information about the targeted process. The main purpose of this article is to examine the feasibility of using one-class classification-based control charts to handle multivariate and autocorrelated processes. The article uses simulated data to present an analysis and comparison of one-class classification-based control charts and the traditional Hotelling's T2 chart.
AB - In recent years, statistical process control (SPC) of multivariate and autocorrelated processes has received a great deal of attention. Modern manufacturing/service systems with more advanced technology and higher production rates can generate complex processes in which consecutive observations are dependent and each variable is correlated. These processes obviously violate the assumption of the independence of each observation that underlies traditional SPC and thus deteriorate the performance of its traditional tools. The popular way to address this issue is to monitor the residualsthe difference between the actual value and the fitted valuewith the traditional SPC approach. However, this residuals-based approach requires two steps: (1) finding the residuals; and (2) monitoring the process. Also, an accurate prediction model is necessary to obtain the uncorrelated residuals. Furthermore, these residuals are not the original values of the observations and consequently may have lost some useful information about the targeted process. The main purpose of this article is to examine the feasibility of using one-class classification-based control charts to handle multivariate and autocorrelated processes. The article uses simulated data to present an analysis and comparison of one-class classification-based control charts and the traditional Hotelling's T2 chart.
KW - Autocorrelated multivariate process
KW - Data mining algorithm
KW - One-class classification algorithm
KW - Statistical process control
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UR - http://www.scopus.com/inward/citedby.url?scp=77349113379&partnerID=8YFLogxK
U2 - 10.1080/03610910903480826
DO - 10.1080/03610910903480826
M3 - Article
AN - SCOPUS:77349113379
VL - 39
SP - 461
EP - 474
JO - Communications in Statistics Part B: Simulation and Computation
JF - Communications in Statistics Part B: Simulation and Computation
SN - 0361-0918
IS - 3
ER -