Research grant for young scientist of the Grant Fund

The main goal of the study is to develop an efficient real-time cluster computing-based system for monitoring and early diagnosis and prognosis of defects in energy conversion systems. This involves the development of new, advanced methods that overcome the limitations of traditional methods by applying machine learning, IoT solutions, and cloud computing. This signifies the next step in industrial equipment maintenance, which is only beginning to be addressed globally – moving from preventive maintenance to predictive maintenance, where faults are predicted before they actually occur.