TY - JOUR
T1 - High-Speed Monitoring of Multidimensional Processes Using Bayesian Updates
AU - Kim, Sangahn
AU - Turkoz, Mehmet
AU - Baek, Jung Woo
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - The advent of modern data acquisition and computing techniques has enabled high-speed monitoring of high-dimensional processes. The short sampling interval makes the samples temporally correlated, even if there is no underlying autocorrelation among covariates. In this study, we introduce a new process monitoring scheme in a Bayesian framework. The key strategy of this study is to incorporate sequential observations into the estimation procedure for the parameters of interest to update the prior distribution. Based on the updated prior, we obtain the most appropriate estimation of the process parameters at each sampling epoch by maximizing the posterior probability. In addition, conventional statistical process control and monitoring methodologies suffer from the 'curse of dimensionality.' The closed form of the estimate developed in this study through Bayesian updates enables the proposed method to be effective for high-dimensional process monitoring. Various simulation studies demonstrate the superiority of the proposed scheme in the high-speed monitoring of high-dimensional processes. Moreover, a few sample paths of the estimated mean in a procedure of the proposed method are illustrated to provide practitioners with insights into the monitoring and control of the process. Finally, we provide a real-life application to illustrate the proposed method.
AB - The advent of modern data acquisition and computing techniques has enabled high-speed monitoring of high-dimensional processes. The short sampling interval makes the samples temporally correlated, even if there is no underlying autocorrelation among covariates. In this study, we introduce a new process monitoring scheme in a Bayesian framework. The key strategy of this study is to incorporate sequential observations into the estimation procedure for the parameters of interest to update the prior distribution. Based on the updated prior, we obtain the most appropriate estimation of the process parameters at each sampling epoch by maximizing the posterior probability. In addition, conventional statistical process control and monitoring methodologies suffer from the 'curse of dimensionality.' The closed form of the estimate developed in this study through Bayesian updates enables the proposed method to be effective for high-dimensional process monitoring. Various simulation studies demonstrate the superiority of the proposed scheme in the high-speed monitoring of high-dimensional processes. Moreover, a few sample paths of the estimated mean in a procedure of the proposed method are illustrated to provide practitioners with insights into the monitoring and control of the process. Finally, we provide a real-life application to illustrate the proposed method.
KW - Autocorrelated process
KW - Bayesian update
KW - high-dimensional process
KW - process mean monitoring
KW - statistical process control
UR - http://www.scopus.com/inward/record.url?scp=85139262316&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3206369
DO - 10.1109/ACCESS.2022.3206369
M3 - Article
AN - SCOPUS:85139262316
SN - 2169-3536
VL - 10
SP - 97450
EP - 97464
JO - IEEE Access
JF - IEEE Access
ER -