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dc.contributor.authorRieger, T
dc.contributor.authorRegier, S
dc.contributor.authorStengel, I
dc.contributor.authorClarke, Nathan

Applying Deep Learning in the field of Industrial Internet of Things is a very active research field. The prediction of failures of machines and equipment in industrial environments before their possible occurrence is also a very popular topic, significantly because of its cost saving potential. Predictive Maintenance (PdM) applications can benefit from DL, especially because of the fact that high complex, non-linear and unlabeled (or partially labeled) data is the normal case. Especially with PdM applications being used in connected smart factories, low latency predictions are essential. Because of this real-time processing becomes more important. The aim of this paper is to provide a narrative review of the most current research covering trends and projects regarding the application of DL methods in IoT environments. Especially papers discussing the area of predictions and real-time processing with DL models are selected because of their potential use for PdM applications. The reviewed papers were selected by the authors based on a qualitative rather than a quantitative level.

dc.titleFast predictive maintenance in Industrial Internet of Things (IIoT) with Deep Learning (DL): A review
dc.typeConference Proceeding
plymouth.journalCEUR Workshop Proceedings
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering
plymouth.organisational-group|Plymouth|Faculty of Science and Engineering|School of Engineering, Computing and Mathematics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA
plymouth.organisational-group|Plymouth|Users by role
plymouth.organisational-group|Plymouth|Users by role|Academics
plymouth.organisational-group|Plymouth|REF 2021 Researchers by UoA|UoA11 Computer Science and Informatics

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