UK industries are facing a growing problem - a lack of experts! Multiple sectors of the UK's economy, especially in Engineering, are increasingly dependent on older workers, leaving employers exposed to a massive need for skilled staff when they retire. While the UK attempts to provide more quality vocational training to young people so they can replace skilled older workers when they retire, there remains years of knowledge gap to be filled. Hence, a technological solution becomes increasingly attractive - i.e. assisting humans with "Virtual Expert" (VE) systems and complementing them while they acquire experience. Many UK companies in industry have a range of automation and digitalisation challenges, such as automatic remote condition monitoring tools and engine test automation, which this project seeks to address. The main concept behind this new project is to build and train an Evolutionary Virtual Expert System (EVES) to assist current and future industrial fault diagnostic engineers. These "virtual apprentices" (diagnostic agents, including knowledge-based rules, signal processing algorithms and model-based approaches) will be trained by human experts, through coaching, examining and refining processes. After a number of subject matter tests, the successful "virtual apprentices" are promoted to become VEs and their weightings (rankings) will be updated using a genetic algorithm. Over generations of evolution, EVES will be able to find a suitable population of VEs (rules/algorithms/models), and produce a heuristically best decision through a weighted voting process, with reasoning mechanisms and possible solutions made transparent to users. EVES integrates the strengths of precision, learning ability, adaptability and knowledge representation from all the VEs that conform to the population, aiming to provide an automated and digitalised fault diagnostic system, to match or possibly outperform human experts working without such support. The EVES project will have a big impact on areas of industrial application. This proposal is timely, as the proportion of experts in UK industries are getting older, while at the same time more modern technologies involve longer learning curves for young people. To be ready for the industries of the future, these VEs, when fully trained, will provide critical support for existing experts, and also act as good trainers for the younger workers. As the future generation is based on high technologies, good virtual assistants and virtual trainers will become increasingly important. The proposal is important, as the structure of EVES is widely applicable to all industrial sectors, for example, from fault diagnostics of machines and plants, to remote condition monitoring for railway applications, agriculture precision, water quality monitoring, and even to diagnostics for human health.

It is now widely accepted that if current private car use trends continue then urban road networks will become increasingly unable to cope with the demand for travel, with existing traffic management techniques unable to achieve desired levels of both sustainability and safety. While much research effort has been directed towards this issue there has been a dichotomy between supply side solutions (for example flow responsive traffic signals) and demand side solutions (such as encouraging high occupancy vehicles and public transport use). The ultimate merging of these two approaches would result in signal priority being given based on an environmentally friendly vehicle occupancy scale (from hybrid/electric public transport at one end to single occupancy large engine cars at the other) with clear sustainability, economic and environmental benefits. The required real-time data sources and technologies to achieve this are only now beginning to be created however and forward looking research is now essential to shape the characteristics of these data sources and quantify the benefits which they facilitate. Since the introduction of demand responsive traffic signal control in the 1970s, urban traffic control (UTC) systems have attempted to optimise traffic signal stage lengths and stage orders based on real time traffic detector data. While much research has been carried out since this time to improve the optimality of the underlying algorithms however, the initial data source of inductive loop or above ground (e.g. infrared) detectors have remained fundamental to the operation of the system. In order to give the maximum opportunity for a set of traffic signals to react to approaching traffic, the detectors used to provide the input data for each arm of the junction are generally located as far upstream as possible often the exit stream from the upstream junctions. While this reliance on upstream detectors gives the greatest warning of approaching traffic it also means that the UTC system must make estimations of the stop line arrival times of vehicles, suffering from errors related to platoon dispersion and indeed the variable speed nature of urban driving. The development of GPS/Galileo technologies for individual vehicle positioning, accompanied by advances in wireless communications technologies however provides increasing opportunity to establish the position of vehicles not just at a single upstream detector location, but continuously along the approaching arm. This would provide the UTC systems with significant increased detail in relation to real-time traffic demand, allowing for more detailed stage adjustments and a transformation from the current discrete decision approach to one of continuous response to approaching demands.The focus of this research is therefore the creation of traffic signal control algorithms based on the real-time positions of individual vehicles and, through the creation of a simulation test bed, the quantification of the benefits in relation to the reductions (compared to existing signal control methods) in both delays and emissions that such an algorithm could achieve, a critical step towards achieving an environmentally and economically sustainable road transport system.

Rapid and transformative advances in power electronic systems are currently taking place following technological breakthroughs in wide-bandgap (WBG) power semiconductor devices. The enhancements in switching speed and operating temperature, and reduction in losses offered by these devices will impact all sectors of low-carbon industry, leading to a new generation of robust, compact, highly efficient and intelligent power conversion solutions. WBG devices are becoming the device of choice in a growing number of power electronic converters used to interface with and control electrical machines in a range of applications including transportation systems (aerospace, automotive, railway and marine propulsion) and renewable energy (e.g. wind power generators). However, the use of WBG devices produces fast-fronted voltage transients with voltage rise-time (dv/dt) in excess of 10~30kV/us which are at least an order of magnitude greater than those seen in conventional Silicon based converters. These voltage transients are expected to significantly reduce the lifetime of the insulation of the connected machines, and hence their reliability or availability. This, in turn, will have serious economic and safety impacts on WBG converter-fed electrical drives in all applications, including safety critical transportation systems. The project aims to advance our scientific understanding of the impact of WBG devices on machine insulation systems and to make recommendations that will support the design and test of machines with an optimised power density and lifetime when used with a WBG converter. This will be achieved by quantifying the negative impact of fast voltage transients when applied to machine insulation systems, by identifying mitigating strategies that are assessed at the device and systems level and by demonstrating solutions that can support the insulation health monitoring of the WBG converter-fed machine, with support from a range of industrial partners in automotive, aerospace, renewable energy and industrial drives sectors.

The Innovative Manufacturing and Construction Research Centre (IMCRC) will undertake a wide variety of work in the Manufacturing, Construction and product design areas. The work will be contained within 5 programmes:1. Transforming Organisations / Providing individuals, organisations, sectors and regions with the dynamic and innovative capability to thrive in a complex and uncertain future2. High Value Assets / Delivering tools, techniques and designs to maximise the through-life value of high capital cost, long life physical assets3. Healthy & Secure Future / Meeting the growing need for products & environments that promote health, safety and security4. Next Generation Technologies / The future materials, processes, production and information systems to deliver products to the customer5. Customised Products / The design and optimisation techniques to deliver customer specific products.Academics within the Loughborough IMCRC have an internationally leading track record in these areas and a history of strong collaborations to gear IMCRC capabilities with the complementary strengths of external groups.Innovative activities are increasingly distributed across the value chain. The impressive scope of the IMCRC helps us mirror this industrial reality, and enhances knowledge transfer. This advantage of the size and diversity of activities within the IMCRC compared with other smaller UK centres gives the Loughborough IMCRC a leading role in this technology and value chain integration area. Loughborough IMCRC as by far the biggest IMRC (in terms of number of academics, researchers and in funding) can take a more holistic approach and has the skills to generate, identify and integrate expertise from elsewhere as required. Therefore, a large proportion of the Centre funding (approximately 50%) will be allocated to Integration projects or Grand Challenges that cover a spectrum of expertise.The Centre covers a wide range of activities from Concept to Creation.The activities of the Centre will take place in collaboration with the world's best researchers in the UK and abroad. The academics within the Centre will be organised into 3 Research Units so that they can be co-ordinated effectively and can cooperate on Programmes.

Project Notch will aim to assess the best way of using existing and new technologies in order to enable the community in a new regeneration development in Nottingham to take more control of their energy generation and usage for both residential and light commercial users. The Siemens Ecosystem will include very small innovative companies as well as the University and the Council and will aim to establish the necessary steps for a successful demonstrator. The feasibility is innovative in attempting to integrate new and old technologies to make regeneration developments in cities carbon neutral. The ability to analyse all the data will enable predictive analytics enebling the communities to make better decisions on generation and demand locally and prioritise as the community requires.