Funding

Self-funded

Project code

SEM10081026

Department

School of Electrical and Mechanical Engineering

Start dates

October, February and April

Application deadline

Applications accepted all year round

Applications are invited for a self-funded, 3-year full-time or 6-year part time PhD project.

The PhD will be based in the School of Electrical and Mechanical Engineering and will be supervised by Dr Edward Smart and Dr Hongjie Ma.

 

The work on this project could involve:

  • Access to real industrial data from a wide variety of sources (including faults)
  • Links to industrial partners, equipment and expertise
  • Opportunities to contribute to funded projects of national importance
  • Integrate and test these algorithms on test equipment

The relative condition of an electro-mechanical system (e.g. dairy filler machine, thermal printer, marine diesel engine) will vary throughout its life cycle. One class classification techniques have recently been applied to learn the condition of the system because they are better suited to learning in the absence of well distributed fault samples. 

 

Current research has shown that these techniques are well suited to detecting machine faults at a given point in time. However, the relative condition of the machine will change during its life-time. Ageing, maintenance actions and design changes will all affect how the machine operates and its baseline ‘normal’ condition. This means that over time, the accuracy of these techniques will be reduced and the algorithms are no longer fit for purpose. Recognising ‘ageing’ and adapting the algorithms to reflect it, so that ‘through-life’ monitoring can be provided for an asset is a key challenge.

 

We propose to investigate how we can use data to see how the condition of a machine changes over time and how we can train a learning algorithm to identify that the ‘normal’ condition of a machine is different so it can be retrained automatically.


The candidate will be supervised by members of the Innovative Industrial Research group. Formed in 2006, this is one of the most successful groups within the ºÚÁϳԹÏ, winning over £3.5 million in funding over the last 5 years.

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK  (UK and EU students only).

 

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

You'll need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a master’s degree in computer science or a related area. In exceptional cases, we may consider equivalent professional experience and/or Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Good Matlab or Python skills

How to apply

We encourage you to contact Dr Edward Smart (Edward.smart@port.ac.uk) to discuss your interest before you apply, quoting the project code below.

When you are ready to apply, please follow the 'Apply now' link on the Electronic Engineering PhD subject area page and select the link for the relevant intake. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV.  Our ‘How to Apply’ page offers further guidance on the PhD application process.  

If you want to be considered for this funded PhD opportunity you must quote project code SEM10081026 when applying.