Process monitoring using variational autoencoder for high-dimensional nonlinear processes

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....

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Veröffentlicht in:Engineering applications of artificial intelligence Jg. 83; S. 13 - 27
Hauptverfasser: Lee, Seulki, Kwak, Mingu, Tsui, Kwok-Leung, Kim, Seoung Bum
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.08.2019
Schlagworte:
High-dimensional process
Multivariate control chart
Nonlinear process
Statistical process monitoring
Variational autoencoder
Variational autoencoder
Statistical process monitoring
Multivariate control chart
High-dimensional process
Nonlinear process
ISSN:0952-1976, 1873-6769
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Zusammenfassung: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.
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2019.04.013