Abstract
Control charts have been widely used to improve manufacturing processes by reducing variations and defects. In particular, multivariate control charts have been effectively applied with monitoring processes that contain many correlated variables. Most existing multivariate control charts are vulnerable to misclassification errors that originate because of the hypothesis tests. In particular, these often cause the generation of a large number of false alarms. In this paper, we propose a procedure to reduce false alarms by combining a multivariate control chart and data mining algorithms. Simulation and real case studies demonstrate that the proposed method effectively reduces the false alarm rate.
| Original language | English |
|---|---|
| Pages (from-to) | 21-29 |
| Number of pages | 9 |
| Journal | Journal of Process Control |
| Volume | 35 |
| DOIs | |
| Publication status | Published - 2015 Nov 10 |
Bibliographical note
Funding Information:We thank the editor and referees for their constructive comments and suggestions, which greatly improved the quality of the paper. This research was supported by Brain Korea PLUS and Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning ( 2013007724 ).
Publisher Copyright:
© 2015 Elsevier Ltd. All rights reserved.
Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.
Keywords
- Classification algorithm
- False alarms
- Multivariate control charts
- TFT-LCD manufacturing process
ASJC Scopus subject areas
- Control and Systems Engineering
- Modelling and Simulation
- Computer Science Applications
- Industrial and Manufacturing Engineering