TY - JOUR
T1 - Designing a parameter-free Kullback-Leibler information control chart for monitoring process mean shift
AU - Chang, Yu Ching
AU - Li, Ting Wei
AU - Mastrangelo, Christina
N1 - Funding Information:
The first author was supported by the Taiwan Ministry of Science and Technology under Grant no. MOST 103‐2410‐H‐006‐040.
Publisher Copyright:
© 2020 John Wiley & Sons Ltd.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/4
Y1 - 2021/4
N2 - This paper proposes a parameter-free Kullback-Leibler information control chart for monitoring sustained shifts in the process mean of a normally distributed process in phase II. Two plotted statistics are provided. One is based on our backward empirical sequential test, the other is based on the maximum log-likelihood ratio change point method. These two achieve similar performances for the control chart. The performance of the proposed chart is compared with those of the cumulative sum chart, the exponentially weighted moving average chart, and the generalized likelihood ratio (GLR) chart. The results show that our proposed chart and the GLR chart have similar performances. Both can detect a wide range of shifts in the process mean, and neither requires design parameters other than the control limits. The proposed chart outperforms GLR when the size of the shift is below 1.24 standard deviations, while GLR outperforms the proposed chart when the size of the shift is above 1.24 standard deviations.
AB - This paper proposes a parameter-free Kullback-Leibler information control chart for monitoring sustained shifts in the process mean of a normally distributed process in phase II. Two plotted statistics are provided. One is based on our backward empirical sequential test, the other is based on the maximum log-likelihood ratio change point method. These two achieve similar performances for the control chart. The performance of the proposed chart is compared with those of the cumulative sum chart, the exponentially weighted moving average chart, and the generalized likelihood ratio (GLR) chart. The results show that our proposed chart and the GLR chart have similar performances. Both can detect a wide range of shifts in the process mean, and neither requires design parameters other than the control limits. The proposed chart outperforms GLR when the size of the shift is below 1.24 standard deviations, while GLR outperforms the proposed chart when the size of the shift is above 1.24 standard deviations.
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U2 - 10.1002/qre.2779
DO - 10.1002/qre.2779
M3 - Article
AN - SCOPUS:85092492936
SN - 0748-8017
VL - 37
SP - 1017
EP - 1034
JO - Quality and Reliability Engineering International
JF - Quality and Reliability Engineering International
IS - 3
ER -