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An Inter-disciplinary Modelling Approach in Industrial 5G/6G and Machine Learning Era

Mohamed, Abdelrahim, Ruan, Hang, Abdelwahab, Mohamed, Dorneanu, Bogdan, Xiao, Pei, Arellano-Garcia, Harvey, Gao, Yang and Tafazolli, Rahim An Inter-disciplinary Modelling Approach in Industrial 5G/6G and Machine Learning Era In: IEEE International Conference on Communications (ICC) 2020, 7-11 June 2020, Dublin, Ireland.

An Inter-disciplinary Modelling Approach in Industrial 5G 6G and Machine Learning Era.pdf - Accepted version Manuscript

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Recently, the fifth-generation (5G) cellular system has been standardised. As opposed to legacy cellular systems geared towards broadband services, the 5G system identifies key use cases for ultra-reliable and low latency communications (URLLC) and massive machine-type communications (mMTC). These intrinsic 5G capabilities enable promising sensor-based vertical applications and services such as industrial process automation. The latter includes autonomous fault detection and prediction, optimised operations and proactive control. Such applications enable equipping industrial plants with a sixth sense (6S) for optimised operations and fault avoidance. In this direction, we introduce an inter-disciplinary approach integrating wireless sensor networks with machine learningenabled industrial plants to build a step towards developing this 6S technology. We develop a modular-based system that can be adapted to the vertical-specific elements. Without loss of generalisation, exemplary use cases are developed and presented including a fault detection/prediction scheme, and a sensor density-based boundary between orthogonal and non-orthogonal transmissions. The proposed schemes and modelling approach are implemented in a real chemical plant for testing purposes, and a high fault detection and prediction accuracy is achieved coupled with optimised sensor density analysis.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Dorneanu, Bogdan
Arellano-Garcia, Harvey
Funders : U.K. Engineering and Physical Sciences Research Council
Grant Title : EPSRC Grant
Uncontrolled Keywords : 5G; big data; deep learning; machine learning; mMTC; network slicing; predictive analysis; URLLC.
Depositing User : James Marshall
Date Deposited : 04 Mar 2020 15:34
Last Modified : 07 Jun 2020 02:08

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