TY - JOUR
T1 - Hierarchical Equipment Health Index Framework
AU - Lee, Chia Yen
AU - Dong, Zhao Hong
N1 - Funding Information:
Manuscript received January 28, 2019; revised April 10, 2019 and June 4, 2019; accepted June 15, 2019. Date of publication June 27, 2019; date of current version August 2, 2019. This work was supported in part by the Ministry of Science and Technology, Taiwan, under Grant MOST106-2218-E-031-001. (Corresponding author: Chia-Yen Lee.) The authors are with the Institute of Manufacturing Information and Systems, National Cheng Kung University, Tainan 701, Taiwan (e-mail: cylee@mail.ncku.edu.tw; djh711429@gmail.com).
Publisher Copyright:
© 1988-2012 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - In the semiconductor manufacturing industry the development of single index assessing the equipment health condition is urgent, and thus the dashboard can be used for monitoring thousands of equipment. This paper proposes a novel equipment health index (EHI) framework and a Hotelling T-squared index (HTI) to monitor equipment and support preventive maintenance in real time in the semiconductor manufacturing industry. The EHI framework consists of data preprocessing, statistical process control, analytic hierarchy process, and a comprehensive single index representing the health condition of equipment. It considers different types of the univariate control charts for each status variable identification (SVID), and aggregates the scores corresponding to the control charts throughout the hierarchical structure. The HTI considers the correlation among the selected SVIDs and builds a multivariate index by using Hotelling T-squared statistic. An empirical study of Taiwan's leading semiconductor assembly manufacturer finds that both the EHI and the HTI supported monitoring thousands of pieces of equipment in real time. In practice, firms can rapidly troubleshoot the root causes of failures by the decomposition of EHI.
AB - In the semiconductor manufacturing industry the development of single index assessing the equipment health condition is urgent, and thus the dashboard can be used for monitoring thousands of equipment. This paper proposes a novel equipment health index (EHI) framework and a Hotelling T-squared index (HTI) to monitor equipment and support preventive maintenance in real time in the semiconductor manufacturing industry. The EHI framework consists of data preprocessing, statistical process control, analytic hierarchy process, and a comprehensive single index representing the health condition of equipment. It considers different types of the univariate control charts for each status variable identification (SVID), and aggregates the scores corresponding to the control charts throughout the hierarchical structure. The HTI considers the correlation among the selected SVIDs and builds a multivariate index by using Hotelling T-squared statistic. An empirical study of Taiwan's leading semiconductor assembly manufacturer finds that both the EHI and the HTI supported monitoring thousands of pieces of equipment in real time. In practice, firms can rapidly troubleshoot the root causes of failures by the decomposition of EHI.
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U2 - 10.1109/TSM.2019.2925362
DO - 10.1109/TSM.2019.2925362
M3 - Article
AN - SCOPUS:85068173806
SN - 0894-6507
VL - 32
SP - 267
EP - 276
JO - IEEE Transactions on Semiconductor Manufacturing
JF - IEEE Transactions on Semiconductor Manufacturing
IS - 3
M1 - 8747393
ER -