Process monitoring using variational autoencoder for high-dimensional nonlinear processes. (August 2019)

• Record Type: Journal Article
• Authors: Lee, Seulki; Kwak, Mingu; Tsui, Kwok-Leung; Kim, Seoung Bum
• Is Part Of: Engineering applications of artificial intelligence. Volume 83(2019)
• Page Start: 13
• Page End: 27
• Publication Date: 2019-08
• Subjects: Statistical process monitoring -- Variational autoencoder -- High-dimensional process -- Nonlinear process -- Multivariate control chart; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.04.013
• Languages: English
• ISSNs: 0952-1976

Abstract

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. Highlights: We propose a variational autoencoder (VAE)-based process monitoring technique. VAE is a nonlinear feature extraction method that appropriate for T 2 charts. VAE chart can reduce both unwanted false alarms and misdetections in process control. VAE charts outperform the existing latent variable-based control charts.