Adaptive ‘Health’ Learning Algorithms for ‘Through-life’ Machine Condition Monitoring

   School of Energy and Electronic Engineering

  , Dr Hongjie Ma  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

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 Energy and Electronic 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

Project description

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 University of Portsmouth, winning over £3.5 million in funding over the last 5 years.

General admissions criteria

You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. 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.

Specific candidate requirements

Good Matlab or Python skills

How to Apply

We encourage you to contact Dr Edward Smart () 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. 

When applying please quote project code: SENE5631023.

Funding Notes

Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK students only).

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