Image-based process monitoring via adversarial autoencoder with applications to rolling defect detection

Hao Yan, Huai Ming Yeh, Nurettin Sergin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high-dimensionality and complex spatial structures. Recent advancements in unsupervised deep models such as generative adversarial networks (GAN) and adversarial autoencoders (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique. Based on this, we propose a monitoring statistic efficiently capturing the change of the data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.

Original languageEnglish (US)
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages311-316
Number of pages6
ISBN (Electronic)9781728103556
DOIs
StatePublished - Aug 2019
Externally publishedYes
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: Aug 22 2019Aug 26 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
CountryCanada
CityVancouver
Period8/22/198/26/19

Keywords

  • Adversarial Autoencoder
  • Deep Generative Models
  • Profile Monitoring
  • Statistical Process Control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Yan, H., Yeh, H. M., & Sergin, N. (2019). Image-based process monitoring via adversarial autoencoder with applications to rolling defect detection. In 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 (pp. 311-316). [8843313] (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2019.8843313