期刊
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
卷 83, 期 -, 页码 13-27出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2019.04.013
关键词
Statistical process monitoring; Variational autoencoder; High-dimensional process; Nonlinear process; Multivariate control chart
类别
资金
- Brain Korea PLUS, Basic Science Research Program through the National Research Foundation of Korea - Ministry of Science, ICT and Future Planning, South Korea [NRF-2016R1A2B1008994]
- Ministry of Trade, Industry & Energy, South Korea under Industrial Technology Innovation Program [R1623371]
- Institute for Information &communications Technology Promotion - Korea government [2018-0-00440]
In many industries, statistical process monitoring techniques play a key role in improving processes through variation reduction and defect prevention. Modern large-scale industrial processes require appropriate monitoring techniques that can efficiently address high-dimensional nonlinear processes. Such processes have been successfully monitored with several latent variable-based methods. However, because these monitoring methods use Hotelling's T-2 statistics in the reduced space, a normality assumption underlies the construction of these tools. This assumption has limited the use of latent variable-based monitoring charts in both nonlinear and nonnormal situations. In this study, we propose a variational autoencoder (VAE) as a monitoring method that can address both nonlinear and nonnormal situations in high-dimensional processes. VAE is appropriate for T-2 charts because it causes the reduced space to follow a multivariate normal distribution. The effectiveness and applicability of the proposed VAE-based chart were demonstrated through experiments on simulated data and real data from a thin-film-transistor liquid-crystal display process.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据