TY - JOUR
T1 - Time-adaptive support vector data description for nonstationary process monitoring
AU - Lee, Seulki
AU - Kim, Seoung Bum
N1 - Funding Information:
The authors would like to thank the editor and reviewers for their useful comments and suggestions, which were greatly help in improving the quality of the paper. This research was supported by Brain Korea PLUS, Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning ( NRF-2016R1A2B1008994 ), and the Ministry of Trade, Industry & Energy under Industrial Technology Innovation Program ( R1623371 ).
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/2
Y1 - 2018/2
N2 - Statistical process control techniques are widely used for quality control to monitor the stability of a process over time. In modern manufacturing systems with complex and variable processes, appropriate control chart techniques that can efficiently address nonnormal processes are required. Furthermore, in real manufacturing environments, process changes occur frequently because of various factors such as product and setpoint changes, catalyst degradation, seasonal variations, and sensor drift. However, conventional control chart schemes cannot necessarily accommodate all possible future conditions of a process because they are formulated based on information recorded in the early stages of the process. Several attempts have been made to accommodate process changes over time. In the present paper, we propose a time-adaptive support vector data description-based control chart that can address not only nonnormal in-control observations, but also time-varying processes. The effectiveness and applicability of the proposed chart was demonstrated through experiments with simulated data and real data from the metal frame process in mobile device manufacturing.
AB - Statistical process control techniques are widely used for quality control to monitor the stability of a process over time. In modern manufacturing systems with complex and variable processes, appropriate control chart techniques that can efficiently address nonnormal processes are required. Furthermore, in real manufacturing environments, process changes occur frequently because of various factors such as product and setpoint changes, catalyst degradation, seasonal variations, and sensor drift. However, conventional control chart schemes cannot necessarily accommodate all possible future conditions of a process because they are formulated based on information recorded in the early stages of the process. Several attempts have been made to accommodate process changes over time. In the present paper, we propose a time-adaptive support vector data description-based control chart that can address not only nonnormal in-control observations, but also time-varying processes. The effectiveness and applicability of the proposed chart was demonstrated through experiments with simulated data and real data from the metal frame process in mobile device manufacturing.
KW - Machine learning
KW - Multivariate control chart
KW - Nonstationary process
KW - Process control
KW - Support vector data description
KW - Time-varying process
UR - http://www.scopus.com/inward/record.url?scp=85033363759&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2017.10.016
DO - 10.1016/j.engappai.2017.10.016
M3 - Article
AN - SCOPUS:85033363759
VL - 68
SP - 18
EP - 31
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
SN - 0952-1976
ER -