A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions-Reference-Cited by-同舟云学术

A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions

Published:2024 Issue: Volume:12 Page:3768-3789
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yan Peng¹^ORCID,Abdulkadir Ahmed¹,Luley Paul-Philipp¹^ORCID,Rosenthal Matthias²^ORCID,Schatte Gerrit A.³^ORCID,Grewe Benjamin F.⁴,Stadelmann Thilo¹^ORCID

Affiliation:

1. Centre for Artificial Intelligence, ZHAW School of Engineering, Winterthur, Switzerland

2. Institute of Embedded Systems, ZHAW School of Engineering, Winterthur, Switzerland

3. Innovation Lab, Kistler Instrumente AG, Winterthur, Switzerland

4. Faculty of Science, University of Zurich, Zürich, Switzerland

Funder

Innosuisse - Schweizerische Agentur f?r Innovationsf?rderung

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10379639.pdf?arnumber=10379639

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