Fast predictive maintenance in Industrial Internet of Things (IIoT) with Deep Learning (DL): A review
dc.contributor.author | Rieger, T | |
dc.contributor.author | Regier, S | |
dc.contributor.author | Stengel, I | |
dc.contributor.author | Clarke, Nathan | |
dc.date.accessioned | 2023-05-22T15:24:38Z | |
dc.date.available | 2023-05-22T15:24:38Z | |
dc.date.issued | 2019-01-01 | |
dc.identifier.issn | 1613-0073 | |
dc.identifier.uri | https://pearl.plymouth.ac.uk/handle/10026.1/20914 | |
dc.description.abstract |
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.format.extent | 69-79 | |
dc.title | Fast predictive maintenance in Industrial Internet of Things (IIoT) with Deep Learning (DL): A review | |
dc.type | conference | |
dc.type | Conference Proceeding | |
plymouth.volume | 2348 | |
plymouth.publication-status | Published | |
plymouth.journal | CEUR Workshop Proceedings | |
plymouth.organisational-group | |Plymouth | |
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 | |
dcterms.dateAccepted | 2019-01-01 | |
dc.date.updated | 2023-05-22T15:24:37Z | |
dc.rights.embargodate | 2023-5-23 | |
dc.rights.embargoperiod | forever |