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

Abstract

Unlike conventional cellular systems, the fifth generation (5G) and beyond includes intrinsic support for vertical industries with diverse service requirements. Industrial process automation with autonomous fault detection and prediction, optimised operations and proactive control can be considered as one of the key verticals of 5G and beyond. Such applications enable equipping industrial plants with a reasoning sixth sense for optimised operations and fault avoidance. In this direction, we introduce an inter-disciplinary approach integrating wireless sensor networks with machine learning-enabled industrial plants to build a step towards developing this sixth sense technology, i.e., the reasoning ability. 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 in a wireless communication network with sensors and actuators to enable the sixth sense technology with guaranteed service load requirements. 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.