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Electrostatic Array Sensor Correlation with Known Wear and Contact Distress Mechanisms

  • Full or part time
  • Application Deadline
    Friday, January 04, 2019
  • Competition Funded PhD Project (UK Students Only)
    Competition Funded PhD Project (UK Students Only)

Project Description

This project is in the area of monitoring the performance of contacting surfaces within components of heavy machines using novel sensing systems (electrostatic sensor arrays ESA) and advanced machine learning techniques. We will study non-stationary signal processing methods and deep neural network models for inference. The project will involve novel algorithm development and experimental validation on specifically constructed rigs. Work is envisaged at Schaeffler testing labs and their adjunct laboratories to show benefits of multi-sensor approach to understanding distress of components/lubricant under test. The PhD would feed into better load capacity determination for bearings for example.

The studentship will be part of much larger £1m EPSRC funded programme looking at sensing and inference in tribological systems with the following research objectives:
(1) to miniaturise existing electrostatic sensor Condition Monitoring, nano stress devices and chemical sensing technology
(2) to produce them as arrays
(3) to embed electronics, and
(4) to use machine leaning to understand outputs and develop predictive tools from training data.

Student background: Good first degree in computer science or similar quantitative discipline. Good knowledge of statics and programming desirable.

If you wish to discuss any details of the project informally, please contact Professor Robert Wood FREng, nCATS research group, Email: , Tel: +44 (0) 2380 59 4881.

Funding Notes

This project is in competition with others for funding. This 3 year studentship covers UK tuition fees and provides an annual tax-free stipend at the standard EPSRC rate, which is £14,777 for 2018/19.

Applicants must be UK residents with no restrictions on how long they can stay in the UK and have lived here for at least 3 years prior to the start of the studentship. This residence cannot be mainly for the purpose of receiving full-time education.

How good is research at University of Southampton in General Engineering?

FTE Category A staff submitted: 192.23

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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