TY - JOUR
T1 - Multivariate control charts that combine the Hotelling T2 and classification algorithms
AU - Park, Sung Ho
AU - Kim, Seoung Bum
N1 - Funding Information:
This research was supported by the Brain Korea PLUS; the Basic Science Research Programme through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning [grant number NRF-2016R1A2B1008994]; the Ministry of Trade, Industry & Energy under Industrial Technology Innovation Programme [grant number R1623371].
Publisher Copyright:
© 2018, © Operational Research Society 2018.
PY - 2019/6/3
Y1 - 2019/6/3
N2 - Multivariate control charts, including Hotelling’s T2 chart, have been widely adopted for the multivariate processes found in many modern systems. However, traditional multivariate control charts assume that the in-control group is the only population that can be used to determine a decision boundary. However, this assumption has restricted the development of more efficient control chart techniques that can capitalise on available out-of-control information. In the present study, we propose a control chart that improves the sensitivity (i.e., detection accuracy) of a Hotelling’s T2 control chart by combining it with classification algorithms, while maintaining low false alarm rates. To the best of our knowledge, this is the first attempt to combine classification algorithms and control charts. Simulations and real case studies demonstrate the effectiveness and applicability of the proposed control chart.
AB - Multivariate control charts, including Hotelling’s T2 chart, have been widely adopted for the multivariate processes found in many modern systems. However, traditional multivariate control charts assume that the in-control group is the only population that can be used to determine a decision boundary. However, this assumption has restricted the development of more efficient control chart techniques that can capitalise on available out-of-control information. In the present study, we propose a control chart that improves the sensitivity (i.e., detection accuracy) of a Hotelling’s T2 control chart by combining it with classification algorithms, while maintaining low false alarm rates. To the best of our knowledge, this is the first attempt to combine classification algorithms and control charts. Simulations and real case studies demonstrate the effectiveness and applicability of the proposed control chart.
KW - Quality
KW - classification algorithm
KW - control
KW - multivariate control chart
KW - statistical process control
UR - http://www.scopus.com/inward/record.url?scp=85049040974&partnerID=8YFLogxK
U2 - 10.1080/01605682.2018.1468859
DO - 10.1080/01605682.2018.1468859
M3 - Article
AN - SCOPUS:85049040974
VL - 70
SP - 889
EP - 897
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
SN - 0160-5682
IS - 6
ER -