Potanin Grant Supports AI-Based Equipment Diagnostics

One of the projects supported within our master's programme is a redesign of the English-language Power Equipment Diagnostics course for international students.

The course will be updated to teach students how to work with heterogeneous diagnostic data, including thermal images, partial-discharge signals, vibration measurements, online monitoring data and other sources.

Its central objective is not simply to introduce diagnostic methods, but to develop the ability to interpret data, understand the physics of equipment degradation and make operational decisions.

The revised course will also incorporate machine learning, computer vision, robotic diagnostics and digital twin technologies.

It will be used in UrFU's international master's programmes.