High voltage cable systems comprise several interacting components, including cables, joints, terminations, and electrical connectors. The service conditions vary with load, ambient and environmental conditions, installation environment, and operating history. Ageing and degradation processes may therefore develop over long timeframes and involve coupled electrical, thermal, mechanical and material effects which also depend on the local environment of the installed parts. The direct measurement of such processes during operation is often limited and available measurement and failure data is generally sparse. The application of different physics-based approaches allows for the generation of a more robust prognostics framework, where uncertainties are considered when approaching the overall component population.
Thesis Description
The thesis will develop a prognostics framework for a selected component of cable system. Physics-based models are combined with available failure statistics and expert knowledge to predict a components remaining useful life and estimate uncertainty of the given prediction.
Main activities:
• Study and identify relevant degradation mechanisms (e.g., Joule heating, changes in the material properties, thermal and electrical aging) and their physical description.
• Evaluate currently existing models, transfer the models to new use cases and add missing degradation models.
• Evaluate replacement of computationally expensive models (e.g., finite-element methods) with reduced-order models, Gaussian processes or neural-network based models to reduce computational costs.
• Integrate available data from literature and measurements and validate models against existing data. Conclude where models are applicable and discuss limitations of both models and methodology.
Expected outcomes:
• A modular python framework for physics-based prognostics for a selected part of a cable system
• Comparison of different approaches to estimate the remaining useful life (e.g., finite-element method, neural-network, reduced-order models) and verification with real-life data
• Recommendation on model application and suggestion for model improvements and possible condition monitoring approaches
Qualifications and requirements
• Master student in Engineering Physics, Material Science, Mathematics or related fields.
• Solid understanding of physics, e.g., heat transfer, electromagnetics and materials.
• Interest and experience in using python for data science and machine learning applications.
• High-level fluency in English, both spoken and written
Contact and application
NKT is committed to fostering a diverse organization and a culture where people from different backgrounds can thrive and are inspired to perform at their best. We believe that a diverse organization enables sustainable performance, and that an inclusive and welcoming culture makes for a better place to work.
This thesis will be done at NKT HV Cables AB, Technology Consulting in Västerås, Sweden. The thesis correspond to 30 hp at full speed and is expected to be run from January 2027.
Please submit your application, including your CV and cover letter, by November 30, 2026.
If you have any questions regarding the position, you are very welcome to contact Recruiting Manager, Giampaolo Martufi, Giampaolo.Martufi@nkt.com or Senior Scientist, Christophe Polek, Christoph.Polek@nkt.com.
Please note that due to the GDPR regulations we cannot accept any applications via e-mail.
Welcome with your application!