TY - GEN
T1 - A Study on Evaluation of Stability in Process Mean Using Bayesian Updating
AU - Takemoto, Yasuhiko
AU - Arizono, Ikuo
N1 - Funding Information:
This work was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 17K01266: “The investigation of data visualization and its application to production and operation management”.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - It is common that a manufacturing process is unstable at the beginning of operation. Then, a process condition is brought into being stable with the lapse of time. The evaluation of stability is very important to process management in order to shift to mass production. In this study, we propose a method of evaluating the stability in a process. In a detail, we investigate a conjugate distribution of the process mean based on Bayesian theory first. In particular, we consider the difference between both of the prior and posterior probability distributions in the process mean to be a criterion for the stability of a process. Hence, we evaluate the difference between the both distributions using information theory. Then, a numerical example in the method of evaluating the stability in a process is shown.
AB - It is common that a manufacturing process is unstable at the beginning of operation. Then, a process condition is brought into being stable with the lapse of time. The evaluation of stability is very important to process management in order to shift to mass production. In this study, we propose a method of evaluating the stability in a process. In a detail, we investigate a conjugate distribution of the process mean based on Bayesian theory first. In particular, we consider the difference between both of the prior and posterior probability distributions in the process mean to be a criterion for the stability of a process. Hence, we evaluate the difference between the both distributions using information theory. Then, a numerical example in the method of evaluating the stability in a process is shown.
KW - Kullback-Leibler divergence
KW - Prior and posterior distribution
KW - Statistical process control
KW - component; Baysian statistics
UR - http://www.scopus.com/inward/record.url?scp=85093972159&partnerID=8YFLogxK
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U2 - 10.1109/APARM49247.2020.9209578
DO - 10.1109/APARM49247.2020.9209578
M3 - Conference contribution
AN - SCOPUS:85093972159
T3 - 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020
BT - 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling, APARM 2020
Y2 - 20 August 2020 through 23 August 2020
ER -