MACHINE VISION BASED REMOTE MONITORING SYSTEM FOR REMOTE PROCESS EQUIPMENT OF HYDROELECTRIC POWER STATION

Currently, there is a widespread digital transformation in many areas of the energy sector. This study proposes a new machine learning-based method for automatically detecting stable operating modes of hydroelectric power plant pumping equipment. The signal diagnostic accuracy rates are more than 90 %, which is critical for daily monitoring and maintenance of units. Modern operating conditions of hydroelectric power plants require the implementation of innovative technologies that contribute to improving their operational efficiency and safety. The proposed approach can help in planning optimal equipment maintenance based on stable operation data, and also has potential for other practical applications for predictive maintenance of various plant components.