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Computer Vision for Automated Asset Degradation Detection and Data Collection in Industry 4.0

School of Computing, Science and Engineering

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Dr J Bass No more applications being accepted Funded PhD Project (UK Students Only)

About the Project

The studentship is with University of Salford and Add Latent Ltd.

Academic Supervisor: Dr Julian Bass
Academic Co-Supervisor: Dr Tarek Gaber
Industrial Supervisor: Hossein Ghavimi
The studentship is fully funded and includes:

• A fee waiver
• A starting stipend of £15,824 p.a. for three and a half years
• All bench fees and consumable costs
• Funds specifically allocated for conference travel

Final date for applications: 31/08/2020

Interviews will be held on: 09/09/2020

The candidate must be in a position to register by 1/10/2020

Add Latent Ltd. have been collaborating with University of Salford for around 5 years and have successfully completed two Knowledge Transfer Partnerships. The company have already created an R&D capability using an agile software development process developed in partnership with the University of Salford. Add Latent Ltd. have an impressive client list of major companies in the energy sector. Their expertise in maintenance optimisation gives them unique access to industrial plant in the energy and utility sector.

This research project aims to:
• Develop and evaluate novel approaches to video detection of equipment degradation,
• Use novel computer vision techniques to blend video and equipment identification from a part database and
• Implement these novel approaches in software for integration into an existing cloud hosted software infrastructure.

These aims will be achieved through three main phases of work. First, establishing a laboratory-based experimental apparatus for testing and evaluating approaches to video detection of asset degradation. Then, you will create novel approaches to video detection of asset degradation. Finally, you will use video processing to recognise asset subsystems and overlay identification information derived from databases.
This project will Implement selected algorithms and approaches to video detection comprising 3D model rendering from video source data, registration of multiple video streams captured on different dates, equipment identification from video.

The successful candidate will work with the Add Latent team to learn about asset integrity and maintenance optimisation, while also developing expertise in a range of research methods through training in the university as a member of the Salford Software and Informatics Research Group.

The successful candidate will be expected to work with the senior management team including giving presentations to the CEO, R&D Manager, staff and other key stakeholders. They will also be expected to design and deliver training for staff on the implementation of the new software products and the work processes that will need to be adopted for product use.

You will hold a minimum of an upper second class honours degree in an area of computer science or software engineering or a closely related discipline, with evidence of completing an empirical research project as part of previous studies or work. A Masters degree with a relevant focus and membership of a professional body (such as BCS, the Chartered Institute for IT) would be an advantage, as would qualifications or experience in video processing, 3D model creation or image processing.

Experience of working with in the commercial digital technology sector would also be an advantage.

As part of your application please provide a CV and covering letter. The research proposal for this project is already designed. Your application should include a brief literature review related to this project, with an outline of what you see as the main challenges in completing the work. The review and reflection should be no more than 5 pages, single line spaced, 11 point, Arial font.

Informal enquiries may be made to Julian Bass by email: [Email Address Removed]

Curriculum vitae and supporting statement explaining their interest should be sent to [Email Address Removed]

Funding Notes

This studentship is only available to students with settled status in the UK, as classified by EPSRC eligibility. Please visit:
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