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

15 Citations (Scopus)


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
Publication statusPublished - 2022 Feb 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) under Grant NRF-2021R1C1C1012031.

Publisher Copyright:
© 2021 IEEE

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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