Are you eager to contribute to innovative research at the forefront of AI in Power Electronics? Are you fascinated by the potential of AI to support Power Electronics design for reliability, and willing to take on the challenge of multidisciplinary research to advance AI methods for reliability modelling of PECs? The overarching goal of this project is to support PECs design for reliability by developing novel AI-based methods to model complex component degradation and failure interactions within PECs via interpretable and data-aware models tailored to deal with uncertainty and sparse data.
Reliability of Power Electronic Converters (PECs) has gained increasing attention due to the central role they play in mission-critical applications across various sectors, from renewable energy generation to e-mobility. Reliability modelling is paramount to support decision-making for their design, operation, control and maintenance. Ultimately, more reliable PECs carry a societal benefit by accelerating the transition to net-zero and thus a sustainable future.
The overarching aim of this research is to develop novel modelling techniques to support PECs reliability during design and operation, encompassing predictive capabilities tailored to deal with uncertainty and sparse data. You will explore the application of novel data-driven techniques, integrating physics-informed AI models that enhance acceptability and explainability. The focus is on the development of a systematic failure troubleshooting and predictive capability to map fault propagation patterns, eventually highlighting the weak points of the design, thus enabling more efficient distributions of design margins across PECs components. The research work will also involve laboratory experiments within the Power Electronic Lab for data collection and testing purposes.
You will design and lead the research project with the guidance of the supervisory team (dr.
Claudia Fecarotti, Prof. George Papafotiou and Prof. Geert-Jan van Houtum). You will publish the results in international journals and conferences to communicate with academia and other societal stakeholders.
Academic and Research EnvironmentIn this PhD project, you will conduct innovative multidisciplinary research at the intersection between reliability and industrial engineering, electrical engineering and AI, by becoming part of the Eindhoven Artificial Intelligent Systems Institute (EAISI), the Operations, Planning, Accounting and Control Group (OPAC) at the Department of Industrial Engineering & Innovation Science (IE&ES) and the Power Electronics Lab within the Department of Electrical Engineering at TU/e. You will be part of a challenging and diverse environment where you will interact with other PhD researchers working on AI and data-driven methods in various fields, including industrial and electrical engineering. You will learn, apply and improve diverse modelling techniques to support the creation of future-proof reliable power electronics.