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Predictive maintenance through advanced data exploitation

Southampton Business School

Location:  Highfield Campus
Closing Date:  Monday 15 July 2019
Reference:  1142219TR

Supervisors:

Prof Dr Christophe Mues

Prof Dr Houduo Qi

Project description:

Based on advanced machine learning concepts, we want to develop predictive maintenance tools for fleets of vehicles. The industrial project sponsor provides a data analytics capability for an advanced Health and Usage Monitoring System whereby sensors record hundreds of parameters at a frequency of 1Hz and thousands of fault codes and user notifications. To provide an indication of the scale of the largest dataset available for this platform the number of rows now exceeds 11 billion —very much a big data environment. 

This broad project can be taken into a variety of directions, to be decided jointly with the project student and the industrial sponsor.  Examples include:

  • optimising asset use via battery health monitoring, oil health monitoring, fuel consumption, power management system monitoring, and other vehicle health indicators.

  • Condition based maintenance – Instead of having monotonous routine maintenance procedures at high costs with low value added to the asset, can we prove the value of condition based maintenance using available data from this given platform?

  • Asset prognostics – Working towards accurately knowing the Health of an asset at all times through data monitoring and striving to predict when failures are likely to arise based on probability algorithms. Questions to answer: What technology is required? What additional data is required? What procedures are required? What programming mechanisms and techniques are required to deliver this proposal?

  • Decision based cost model simulations – The creation of models to accurately predict costs throughout an assets in-service life. By the application of business intelligence, can simulations be set up to predict the change to forecasted costs by inputting data-based decisions – these simulations will provide the evidence in choosing the ‘right’ course of action for an asset.

DIAMOND: from Data and Intelligence via Modelling to Decisions

This project is part of the Southampton DIAMOND initiative of industrially funded PhD projects in Operational Research, Data Science, and mathematical modeling. This year, eight funded studentships are available within DIAMOND.

CORMSIS, the Centre for Operational Research, Management Science, and Information Systems

You will be part of the vibrant research environment of CORMSIS, the Centre for Operational Research, Management Science, and Information Systems at the University of Southampton. CORMSIS has an established breadth and depth in Operational Research unrivalled in the UK. Our research centre applies advanced mathematical and analytical modelling to help people and organisations make better decisions. CORMSIS is the largest Operational Research group in the UK, spanning Mathematical Sciences and Southampton Business School. Among the many areas of expertise, it has extensive breadth and depth of experience in mathematical modelling and optimisation, but covers the whole spectrum of current OR/MS/IS from mathematical optimisation through business analytics and simulation to qualitative research in problem structuring.. In the QS World Rankings by Subject 2019, Operational Research and Statistics at the University of Southampton are placed at 48th in the world and 7th in the UK.
(http://www.southampton.ac.uk/cormsis/)

How to apply:

Scholarships will be awarded on a competitive basis. Applicants should have or expect to obtain the equivalent of a UK first class or upper second class honours degree (and preferably a master’s degree) in mathematics, computer science, engineering or other relevant discipline. The studentship provides a maintenance grant at the Research Council UK rate and tuition fees at the UK/EU rate. Applications should include a cover letter, CV, detailed academic transcripts and the contact details for at least two academic referees.



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