An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial Internet of Things

作者：Pease Sarogini Grace^*; Trueman Russell; Davies Callum; Gro**erg Jude; Yau Kai Hin; Kaur Navjot; Conway Paul; West Andrew

来源：Future Generation Computer Systems-The International Journal of eScience, 2018, 79: 815-829.

DOI：10.1016/j.future.2017.09.026

• Wireless networks
• Real-time systems
• Energy efficiency
• Energy management
• Process planning

Energy waste significantly contributes to increased costs in the automotive manufacturing industry, which is subject to energy usage restrictions and taxation from national and international policy makers and restrictions and charges from national energy providers. For example, the UK Climate Change Levy, charged to businesses at 0.554 p/kWh equates to 7.28% of a manufacturing business's energy bill based on an average total usage rate of 7.61 p/kWh. Internet of Things (IoT) energy monitoring systems are being developed, however, there has been limited consideration of services for efficient energy-use and minimisation of production costs in industry. This paper presents the design, development and validation of a novel, adaptive Cyber-Physical Toolset to optimise cumulative plant energy consumption through characterisation and prediction of the active and reactive power of three-phase industrial machine processes. Extensive validation has been conducted in automotive manufacture production lines with industrial three-phase Hurco VM1 computer numerical control (CNC) machines.

• 出版日期2018-2