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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30190
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dc.contributor.authorYan, Peng-
dc.contributor.authorAbdulkadir, Ahmed-
dc.contributor.authorLuley, Paul-Philipp-
dc.contributor.authorRosenthal, Matthias-
dc.contributor.authorSchatte, Gerrit A.-
dc.contributor.authorGrewe, Benjamin F.-
dc.contributor.authorStadelmann, Thilo-
dc.date.accessioned2024-03-12T13:37:32Z-
dc.date.available2024-03-12T13:37:32Z-
dc.date.issued2024-01-02-
dc.identifier.issn2169-3536de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/30190-
dc.description.abstractAutomating 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.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.relation.ispartofIEEE Accessde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectDeep transfer learningde_CH
dc.subjectTime series analysisde_CH
dc.subjectAnomaly detectionde_CH
dc.subjectManufacturing process monitoringde_CH
dc.subjectPredictive maintenancede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleA comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directionsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
zhaw.organisationalunitInstitute of Embedded Systems (InES)de_CH
dc.identifier.doi10.1109/ACCESS.2023.3349132de_CH
dc.identifier.doi10.21256/zhaw-30190-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end3789de_CH
zhaw.pages.start3768de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume12de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.funding.zhawDISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learningde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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.


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