Process monitoring using variational autoencoder for high-dimensional nonlinear processes

Seulki Lee, Mingu Kwak, Kwok Leung Tsui, Seoung Bum Kim

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)13-27
Number of pages15
JournalEngineering Applications of Artificial Intelligence
Volume83
DOIs
Publication statusPublished - 2019 Aug 1

Keywords

  • High-dimensional process
  • Multivariate control chart
  • Nonlinear process
  • Statistical process monitoring
  • Variational autoencoder

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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