Data Analytics and Resource Efficiency
“Optimising resources through data analytics to drive efficiencies”
Big Data and IoT
Making sense of Big Data through predictive analytics and providing insights
Energy Recovery and Management
Developing tools and strategies to reduce energy consumption to an optimal level
Developing intelligent systems that reduce human effort and improve efficiency and safety
Autonomous transport and ride sharing economy for the manufacturing industry
Intelligent multi-use of resource and waste in an urban landscape
Through automation in local clouds, the Arrowhead framework allows networks to be decentralised and communicate using common standards. The project, funded by Artemis, is an IoT platform built for collaboration. The UK consortium consists of HSSMI, WMG, FDS and Ford. HSSMI provide a smart energy monitoring solution capable of detecting machine states and cycles; WMG and FDS provide engineering software and visualisation tools; and Ford – the end user requirements. Case studies include using these services in automotive manufacturing to measure, reduce and make decisions based on real-time energy monitoring data.
Partners: 80+ partners from 15 European countries
The project vision is to enable full economic sustainability of the production systems based on intelligent modular plug-and-produce equipment. This will be achieved through: 1) embedding plug-and-produce capabilities into machines and devices, 2) enabling vertical and horizontal connectivity between plug-and-produce automation components as well as higher level control and business functions, and 3) creating an easily extendable and adaptable manufacturing operating system (MOS).
Partners: Afag, Asys, Electrolux, Elrest, Ford Motor Company, Fortiss, Inotec, Introsys, KTH, Linköping University, Loughborough University, Masmec, SenseAir, Uninova, We Plus
Dynamic Resource Monitor (DRM)
The Dynamic Resource Monitor is an end-to-end hardware and software solution for remote monitoring of energy consumption and environmental conditions of machinery in a production line. Its core features include visualisation of both live and historic average views of power usage and machine status at individual, line and factory levels. The DRM’s unique feature is an algorithm which can identify and determine the cycle-time of a machine without the need for pre-calibration.
HandS App is an initiative to make reporting health and safety issues more natural, fast and most of all effective. Current workflows in most organisation are often time consuming and ineffective. Through continued reporting from all members of staff, by making it really easy for them, it is hoped that such an application will eventually save lives – particularly in hazardous working environments.
Multi-agent negotiation strategies for dynamic energy reduction in manufacturing (Melanie Zimmer)
Melanie’s PhD project is part of openMOS, which focuses on developing a common and openly accessible plug-and-produce (P&P) system. She will in particular look into multi-agent negotiation strategies to provide dynamic energy optimisation techniques.
Academic partner: Loughborough University, Dr Niels Lohse & Dr Pedro Ferreira