Proceedings of the 5th International Symposium on Knowledge Acquisition and Modeling

Self-related Process Residual Control Chart Based on Neural Network

Authors
Yu Janli, Han Yang, Miao Manxiang, Huang Hongqi
Corresponding Author
Yu Janli
Available Online June 2015.
DOI
https://doi.org/10.2991/kam-15.2015.11How to use a DOI?
Keywords
neural network; self-related process; control chart.
Abstract

The output data of the modern complex product manufacturing process shows the high correlation, leading to the output of the process deviates from the design target or increasing of false alarms when traditional Control Charts monitors the process. A kind of self-related process of residual control chart, which is based on the Neural network, is using Neural network to establish time series prediction model of the self-related process and apply this model to predict the output of the self-related process. Forming residual control chart with the output of the self-related process and the Neural network prediction residual to eliminate correlation of time series of the self-related process and implement statistical quality control of self-related process.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 5th International Symposium on Knowledge Acquisition and Modeling
Series
Advances in Intelligent Systems Research
Publication Date
June 2015
ISBN
978-94-62520-87-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/kam-15.2015.11How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yu Janli
AU  - Han Yang
AU  - Miao Manxiang
AU  - Huang Hongqi
PY  - 2015/06
DA  - 2015/06
TI  - Self-related Process Residual Control Chart Based on Neural Network
BT  - Proceedings of the 5th International Symposium on Knowledge Acquisition and Modeling
PB  - Atlantis Press
SP  - 41
EP  - 43
SN  - 1951-6851
UR  - https://doi.org/10.2991/kam-15.2015.11
DO  - https://doi.org/10.2991/kam-15.2015.11
ID  - Janli2015/06
ER  -