PhD Studentship: Physics-informed Transfer Learning for Engineering Asset Management
University of Strathclyde - Business School
| Qualification Type: | PhD |
|---|---|
| Location: | Glasgow |
| Funding for: | UK Students, EU Students, International Students |
| Funding amount: | Not Specified |
| Hours: | Full Time |
| Placed On: | 17th March 2023 |
|---|---|
| Closes: | 30th April 2023 |
Subject area: Management Science
PhD opportunity: Physics-informed transfer learning for engineering asset management
Project summary: This research aims to develop a physics-informed transfer learning model for engineering asset management, by effectively incorporating domain knowledge of experts/engineers into the machine learning model to improve its explainability.
Deadline: 30 April 2023
Duration: 36 months full-time
Funding details: Fully-funded scholarship for 3 years covers all university tuition fees (at UK level) and an annual tax-free stipend. International students are also eligible to apply, but they will need to find other funding sources to cover the difference between the home and international tuition fees. Exceptional international candidates may be provided funding for this difference.
Number of places: 1
Eligibility: 1st class first degree and/or an excellent Masters-level qualification or overseas equivalent in a relevant computer science, management science, operations research, mathematics and statistics, and data science from a recognised academic institution
Project details: This research aims to develop a physics-informed transfer learning (TL) model for engineering asset management, by effectively incorporating domain knowledge of experts/engineers into the TL model to improve its explainability. Specifically, expert judgement will be used to extract the domain knowledge that indicates the change of settings to improve the explainability and efficiency of TL models. The following four objectives will be achieved through the project.
- To investigate the nonstationary degradation processes of assets from multiple data sources through sensor fusion.
- To develop a physics-informed transfer learning model for degradation modelling, by effectively incorporating domain knowledge into the TL model.
- To develop an intelligent decision support tool for asset management decision-making.
- To deploy, evaluate and improve the proposed asset management framework in real industrial scenarios.
Primary supervisor: Dr Bin Liu
Contact: b.liu@strath.ac.uk
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