.png){kind=icon}
返回
A missing manufacturing process data imputation framework for nonlinear dynamic soft sensor modeling and its application
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2023.121428.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Data-driven soft sensors have been widely used in manufacturing processes for product quality prediction. However, in engineering practice, traditional linear soft sensors are considered to be insufficient when the manufacturing processes present high dimensionality as well as strong nonlinearity and dynamics. Moreover, due to the economic and technical limitations, missing data problem is widespread, which affects the generalization and accuracy of soft sensors. To overcome these issues, a missing manufacturing process data imputation framework is presented for nonlinear dynamic soft sensor modeling with the purpose of quality prediction. Specifically, the generate adversarial imputation network is introduced for data imputation, and the loss functions of discriminator and generator are reasonably designed. Subsequently, a mix gated unit combining bidirectional gated recurrent unit with bidirectional minimal gated unit is proposed for nonlinear dynamic soft sensor modeling aiming at improving the performance of quality prediction. Finally, the efficiency and flexibility of the proposed framework are demonstrated through a representative manufacturing process, the hot rolling process, in which the percentages of missing data are simulated from 20% to 80%. The ideal performance indicators, root mean square error and mean absolute error, have been obtained. It can be shown that the proposed framework can provide better prediction accuracy than the competitive methods in each scenario.
Keyword:
Data imputation
Soft sensor modeling
Quality prediction
Generate adversarial imputation network
Mix gated unit
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
暂无机构信息
引用论文
Latent-Enhanced Variational Adversarial Active Learning Assisted Soft Sensor潜在增强变分对抗主动学习辅助软测量.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏A bi-objective k-nearest-neighbors-based imputation method for multilevel data一种基于双目标k近邻的多级数据插补方法
OAAI
OAAI分享收藏Data-Driven Modeling Based on Two-Stream λ Gated Recurrent Unit Network With Soft Sensor Application
PREAI
分享收藏
查看更多内容