ORCID
- Clarke, Nathan: 0000-0002-3595-3800
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.
Publication Date
2019-01-01
Publication Title
CEUR Workshop Proceedings
Volume
2348
First Page
69
Last Page
79
ISSN
1613-0073
Embargo Period
2023-05-23
Organisational Unit
School of Engineering, Computing and Mathematics
Recommended Citation
Rieger, T., Regier, S., Stengel, I., & Clarke, N. (2019) 'Fast predictive maintenance in Industrial Internet of Things (IIoT) with Deep Learning (DL): A review', CEUR Workshop Proceedings, 2348, pp. 69-79. Retrieved from https://pearl.plymouth.ac.uk/secam-research/906
