About the project
This project aims to develop data-driven asset criticality ranking and maintenance optimisation methods. By integrating machine learning with operational, condition-monitoring, and environmental data, it investigates multi-stress degradation, predicts asset failure risks, and prioritises critical assets to improve maintenance efficiency and power system reliability.
Effective asset management in electrical power systems is essential for ensuring operational reliability, minimising downtime, and optimising maintenance costs. However, traditional maintenance strategies often overlook the combined effects of environmental and operational stresses on asset degradation, limiting the accuracy of asset risk assessment and maintenance prioritisation.
This project aims to develop a data-driven framework for asset condition assessment, failure risk prediction, criticality ranking, and maintenance optimisation. Particular attention will be given to understanding how multiple stress factors influence asset health and long-term reliability.
The research will combine laboratory-based experiments with the analysis of available datasets, published research findings, and relevant environmental and operational information. Experimental investigations will provide insights into degradation mechanisms, while data analysis and modelling will support the assessment of asset performance and failure risks.
Expected outcomes include improved understanding of asset degradation mechanisms, more accurate risk assessment, systematic asset criticality ranking, and optimised maintenance strategies, supporting proactive decision-making and enhanced power system reliability.
You'll join an interdisciplinary and diverse academic team in the Electrical Power Engineering research group that will support you in expanding your transferrable skills, such as critical review of the literature, academic writing and publishing, as well as collaborating with your team members and organising activities. You'll acquire hands-on experience with testing facilities and operate state-of-the-art equipment in the Tony Davies High Voltage Lab (TDHVL) as part of your experimental research.
The School of Electronics and Computer Science is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
