Data-Driven Physics-Informed Reliability Prediction of Power Electronics for Net Zero Applications
Develop a novel physics-informed, data-driven model to predict power electronics reliability in net-zero applications. Integrate multi-modal data for real-time health monitoring to enable early failure detection and improve system sustainability.
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Project Description
Project Overview
Power electronics convert power in net-zero applications like electrified transportation and renewable energy but often fail due to high electrothermal stress. This project, in partnership with Toshiba Europe, develops a physics-informed, data-driven reliability model for these components, combining experimental and industry data.
What You Will Do
You will develop hybrid modelling and robust statistical learning methods using lifecycle datasets to predict reliability. You will extend the approach to real-time multi-modal health monitoring integrating electrothermal and operational data streams. The project explores sustainability-aware prognostics and multi-modal data fusion for early failure warning.
Expected Outcomes
The project aims to deliver new knowledge of reliability modeling, create datasets and prototypes, and validate solutions in operational environments. Research findings will be shared internationally, with engagement through IEEE PELS and industrial collaborations, including a placement with Toshiba Europe.
Why This Matters
Understanding failure mechanisms and accurate lifetime prediction will reduce power electronics failure, increasing reliability and sustainability of net-zero energy systems critical for global climate targets.
Entry Requirements
Eligibility
Supervisor Profile
Dr Sheng Wang is a researcher at the University of Glasgow's ASC Division specializing in artificial intelligence applications in power electronics reliability and prognostics. His work focuses on integrating data-driven methods with physics-based models to enhance system lifetime and resilience. He collaborates closely with industry to translate research into practical solutions.