Adaptive nonparametric control chart for time-varying and multimodal processes
•We propose a nonparametric control chart, which is based on a clustering algorithm.•Proposed chart can adaptively monitor the time-varying and multimodal processes.•Proposed chart traces the natural changes over time and keeps the chart up to date.•We examine the stability of proposed chart with a...
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| Published in | Journal of process control Vol. 37; pp. 34 - 45 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0959-1524 1873-2771 |
| DOI | 10.1016/j.jprocont.2015.11.005 |
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| Abstract | •We propose a nonparametric control chart, which is based on a clustering algorithm.•Proposed chart can adaptively monitor the time-varying and multimodal processes.•Proposed chart traces the natural changes over time and keeps the chart up to date.•We examine the stability of proposed chart with a case study from TFT-LCD process.
Multivariate statistical process control techniques have been widely used to improve processes by reducing variation and preventing defects. In modern manufacturing, because of the complexity and variability of processes, traditional multivariate control charts such as Hotelling's T2 cannot efficiently handle situations in which the patterns of process observations are nonlinear, multimodal, and time varying. In the present study, we propose a nonparametric control chart, which is capable of adaptively monitoring time-varying and multimodal processes. Experiments with simulated and real process data from a thin film transistor-liquid crystal display (TFT-LCD) demonstrate the effectiveness and accuracy of the proposed method. |
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| AbstractList | •We propose a nonparametric control chart, which is based on a clustering algorithm.•Proposed chart can adaptively monitor the time-varying and multimodal processes.•Proposed chart traces the natural changes over time and keeps the chart up to date.•We examine the stability of proposed chart with a case study from TFT-LCD process.
Multivariate statistical process control techniques have been widely used to improve processes by reducing variation and preventing defects. In modern manufacturing, because of the complexity and variability of processes, traditional multivariate control charts such as Hotelling's T2 cannot efficiently handle situations in which the patterns of process observations are nonlinear, multimodal, and time varying. In the present study, we propose a nonparametric control chart, which is capable of adaptively monitoring time-varying and multimodal processes. Experiments with simulated and real process data from a thin film transistor-liquid crystal display (TFT-LCD) demonstrate the effectiveness and accuracy of the proposed method. |
| Author | Yu, Jaehong Kim, Seoung Bum Kang, Ji Hoon |
| Author_xml | – sequence: 1 givenname: Ji Hoon orcidid: 0000-0003-3784-5958 surname: Kang fullname: Kang, Ji Hoon organization: Industrial Technology Laboratory, Algorithm Research Center, Samsung SDS, Sincheon-dong, Songpa-gu, Seoul 138-240, Republic of Korea – sequence: 2 givenname: Jaehong surname: Yu fullname: Yu, Jaehong organization: Department of Industrial Management Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Republic of Korea – sequence: 3 givenname: Seoung Bum surname: Kim fullname: Kim, Seoung Bum email: sbkim1@korea.ac.kr organization: Department of Industrial Management Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Republic of Korea |
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| Keywords | Time-varying process Data mining algorithm Clustering Multivariate control chart Multimodality False alarms |
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| SubjectTerms | Clustering Data mining algorithm False alarms Multimodality Multivariate control chart Time-varying process |
| Title | Adaptive nonparametric control chart for time-varying and multimodal processes |
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