A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions

Yan, Peng; Abdulkadir, Ahmed; Luley, Paul-Philipp; Rosenthal, Matthias; Schatte, Gerrit A.; Grewe, Benjamin F.; Stadelmann, Thilo

doi:10.1109/ACCESS.2023.3349132

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30190

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions
Authors:	Yan, Peng Abdulkadir, Ahmed Luley, Paul-Philipp Rosenthal, Matthias Schatte, Gerrit A. Grewe, Benjamin F. Stadelmann, Thilo
et. al:	No
DOI:	10.1109/ACCESS.2023.3349132 10.21256/zhaw-30190
Published in:	IEEE Access
Volume(Issue):	12
Page(s):	3768
Pages to:	3789
Issue Date:	2-Jan-2024
Publisher / Ed. Institution:	IEEE
ISSN:	2169-3536
Language:	English
Subjects:	Deep transfer learning; Time series analysis; Anomaly detection; Manufacturing process monitoring; Predictive maintenance
Subject (DDC):	006: Special computer methods
Abstract:	Automating the monitoring of industrial processes has the potential to enhance efficiency and optimize quality by promptly detecting abnormal events and thus facilitating timely interventions. Deep learning, with its capacity to discern non-trivial patterns within large datasets, plays a pivotal role in this process. Standard deep learning methods are suitable to solve a specific task given a specific type of data. During training, deep learning demands large volumes of labeled data. However, due to the dynamic nature of the industrial processes and environment, it is impractical to acquire large-scale labeled data for standard deep learning training for every slightly different case anew. Deep transfer learning offers a solution to this problem. By leveraging knowledge from related tasks and accounting for variations in data distributions, the transfer learning framework solves new tasks with little or even no additional labeled data. The approach bypasses the need to retrain a model from scratch for every new setup and dramatically reduces the labeled data requirement. This survey first provides an in-depth review of deep transfer learning, examining the problem settings of transfer learning and classifying the prevailing deep transfer learning methods. Moreover, we delve into applications of deep transfer learning in the context of a broad spectrum of time series anomaly detection tasks prevalent in primary industrial domains, e.g., manufacturing process monitoring, predictive maintenance, energy management, and infrastructure facility monitoring. We discuss the challenges and limitations of deep transfer learning in industrial contexts and conclude the survey with practical directions and actionable suggestions to address the need to leverage diverse time series data for anomaly detection in an increasingly dynamic production environment.
URI:	https://digitalcollection.zhaw.ch/handle/11475/30190
Fulltext version:	Published version
License (according to publishing contract):	CC BY 4.0: Attribution 4.0 International
Departement:	School of Engineering
Organisational Unit:	Centre for Artificial Intelligence (CAI) Institute of Embedded Systems (InES)
Published as part of the ZHAW project:	DISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learning
Appears in collections:	Publikationen School of Engineering

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Yan, P., Abdulkadir, A., Luley, P.-P., Rosenthal, M., Schatte, G. A., Grewe, B. F., & Stadelmann, T. (2024). A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions. IEEE Access, 12, 3768–3789. https://doi.org/10.1109/ACCESS.2023.3349132

Yan, P. et al. (2024) ‘A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions’, IEEE Access, 12, pp. 3768–3789. Available at: https://doi.org/10.1109/ACCESS.2023.3349132.

P. Yan et al., “A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions,” IEEE Access, vol. 12, pp. 3768–3789, Jan. 2024, doi: 10.1109/ACCESS.2023.3349132.

YAN, Peng, Ahmed ABDULKADIR, Paul-Philipp LULEY, Matthias ROSENTHAL, Gerrit A. SCHATTE, Benjamin F. GREWE und Thilo STADELMANN, 2024. A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions. IEEE Access. 2 Januar 2024. Bd. 12, S. 3768–3789. DOI 10.1109/ACCESS.2023.3349132

Yan, Peng, Ahmed Abdulkadir, Paul-Philipp Luley, Matthias Rosenthal, Gerrit A. Schatte, Benjamin F. Grewe, and Thilo Stadelmann. 2024. “A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series : Methods, Applications, and Directions.” IEEE Access 12 (January): 3768–89. https://doi.org/10.1109/ACCESS.2023.3349132.

Yan, Peng, et al. “A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series : Methods, Applications, and Directions.” IEEE Access, vol. 12, Jan. 2024, pp. 3768–89, https://doi.org/10.1109/ACCESS.2023.3349132.