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PhD Studentship in Electrical and Electronic Engineering: Predictive Maintenance in Marine Diesel Engines with Machine Learning-Based Systems


School of Engineering

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Dr D Balsamo , Dr R Shafik No more applications being accepted Funded PhD Project (European/UK Students Only)

About the Project

Number of awards
1

Start date and duration
October 2020 for 3 years.


Overview
Rapid changes in the marine industry due to the introduction and advancements within telecommunications are predicted, and it has been suggested that the vast majority of commercial vessels will be broadband capable within the very near future. Increased data transfer rates are expected to lead to a change in the industry’s approach to, and desired for, optimised Prognostics and Health Management (PHM) systems for fault detection, isolation and prediction of marine diesel engines.

An optimised stand-alone PHM solution for small to medium-sized marine diesel engines could provide improved energy efficiency, which could result in cost and environmental benefits. If the state of health or condition of a system, subsystem or component is known, condition-based maintenance can be carried out, and system design optimisation can be achieved thereby reducing the total cost of ownership and efficiency of the system.

The focus of the proposed project is to design and develop IoT solutions for machine learning-based predictive maintenance with medium-sized marine diesel engines. The design of such systems will rely on vibration and acoustic sensors as primary data sources, and machine learning models for predictive maintenance and fault. These systems’ aim is to detect future engine failures and help scheduling maintenance in advance.

Furthermore, this project will also explore the utilisation of energy harvesting solutions which can potentially lead to the development of a battery-free predictive maintenance system.

The supervision team for the PhD is Dr Domenico Balsamo and Dr Rishad Shafik, experts in hardware and software design for embedded systems and IoT. Royston Diesel Power Company.

Sponsor
‌European Regional Development Fund (ERDF) and Royston Diesel Power.

EU Regional Development Fund Landscape

Name of supervisor(s)
Dr Domenico Balsamo (https://bit.ly/2XXgOMd) and Dr Rishad Shafik (https://bit.ly/370BI19)

Eligibility Criteria
You should have, or expect to achieve, at least a 2:1 Honours degree, or international equivalent in Electrical and Electronic Engineering or closely related discipline.

The award is available to UK/EU applicants only.

How to apply
You must apply through the University’s online postgraduate application system (https://bit.ly/3dyZGCS).

.You will need to:

• insert the programme code 8060F in the programme of study section
• select ‘PhD Electrical and Electronic Engineering (full time) - Electrical & Electronic Engineering’ as the programme of study
• insert the studentship code ENG069 in the studentship/partnership reference field
• attach a covering letter and CV. The covering letter must state the title of the studentship, quote reference code ENG069 and state how your interests and experience relate to the project
• attach degree transcripts and certificates and, if English is not your first language, a copy of your English language qualifications.

Contact
For further information, please contact Dr Balsamo (https://bit.ly/2XXgOMd).

Funding Notes

100% of UK/EU tuition fees paid and annual living expenses of £15,285.
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