Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes

Kyojin Jang, Seokyoung Hong, Minsu Kim, Jonggeol Na, Il Moon

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Deep learning has recently emerged as a promising method for nonlinear process monitoring. However, ensuring that the features from process variables have representative information of the high-dimensional process data remains a challenge. In this study, we propose an adversarial autoencoder (AAE) based process monitoring system. AAE which combines the advantages of a variational autoencoder and a generative adversarial network enables the generation of features that follow the designed prior distribution. By employing the AAE model, features that have informative manifolds of the original data are obtained. These features are used for constructing and monitoring statistics and improve the stability and reliability of fault detection. Extracted features help calculate the degree of abnormalities in process variables more robustly and indicate the type of fault information they imply. Finally, our proposed method is testified using the Tennessee Eastman benchmark process in terms of fault detection rate, false alarm rate, and fault detection delays.

Original languageEnglish
Pages (from-to)827-834
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Issue number2
StatePublished - 1 Feb 2022

Bibliographical note

Publisher Copyright:
© 2021 IEEE


  • Adversarial autoencoder (AAE)
  • Tennessee Eastman (TE) process
  • data-driven method
  • dimensionality reduction
  • fault detection
  • process monitoring


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