An Active Reference Reset Method Adapting Distribution Shift for Robust System Anomaly Detection

Seungwan Seo, Heejeong Choi, Pilsung Kang

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

Abstract

In order to ensure the operation reliability of process systems in various industries, faults and unplanned shutdowns must be detected early. Traditionally, they have been detected based on the domain knowledge of industrial engineers. However, data-based methods have been developed with recent advances in machine-learning algorithms. These methods can detect signs of shutdowns and set the alarms off. However, they can not account for data distributions that have undergone changes after shutdowns. To handle this challenge, we propose a robust system anomaly detection algorithm using the distribution-based active reference set reset method. The experimental results on real-world industry data show that the proposed method could detect abnormal signs early at various thresholds.

Original languageEnglish
Title of host publicationICTC 2022 - 13th International Conference on Information and Communication Technology Convergence
Subtitle of host publicationAccelerating Digital Transformation with ICT Innovation
PublisherIEEE Computer Society
Pages515-518
Number of pages4
ISBN (Electronic)9781665499392
DOIs
Publication statusPublished - 2022
Event13th International Conference on Information and Communication Technology Convergence, ICTC 2022 - Jeju Island, Korea, Republic of
Duration: 2022 Oct 192022 Oct 21

Publication series

NameInternational Conference on ICT Convergence
Volume2022-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference13th International Conference on Information and Communication Technology Convergence, ICTC 2022
Country/TerritoryKorea, Republic of
CityJeju Island
Period22/10/1922/10/21

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2C2005455). This work was also supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2021-0-00034, Clustering technologies of fragmented data for time-based data analysis).

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Anomaly detection
  • Manufacturing system
  • Predictive maintenance

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

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