Process monitoring using variational autoencoder for high-dimensional nonlinear processes - Lee, Seulki; Kwak, Mingu; Tsui, Kwok-Leung; Kim, Seoung Bum (Engineering applications of artificial intelligence, 2019)
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 T2 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 T2 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. •We propose a variational autoencoder (VAE)-based process monitoring technique.•VAE is a nonlinear feature extraction method that appropriate for T2 charts.•VAE chart can reduce both unwanted false alarms and misdetections in process control.•VAE charts outperform the existing latent variable-based control charts.