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Details of Grant 

EPSRC Reference: EP/R029741/1
Title: Evolutionary Virtual Expert System
Principal Investigator: Zhang, Dr Y
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Siemens plc (UK)
Department: Lincoln School of Engineering
Organisation: University of Lincoln
Scheme: New Investigator Award
Starts: 01 July 2018 Ends: 21 August 2019 Value (£): 96,359
EPSRC Research Topic Classifications:
Artificial Intelligence Structural Engineering
EPSRC Industrial Sector Classifications:
Energy
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Feb 2018 Engineering Prioritisation Panel Meeting 7 and 8 February 2018 Announced
Summary on Grant Application Form
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.

Key Findings
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Potential use in non-academic contexts
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Impacts
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Summary
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Further Information:  
Organisation Website: http://www.lincoln.ac.uk