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The objective of this project has been the development of a system capable of automating the early detection of leaks in heat collector pipes of concentrated solar power installations, redundantly and through the use of artificial intelligence technologies applied to image recognition, as well as IoT technologies.
This solution involves automating the detection by incorporating a set of elements into the installations that monitor the sensitive components of the entire plant, along with a system capable of processing the generated images and providing real-time and highly reliable alerts of leak production from the very moment they occur.
To carry out this project, we have worked with artificial intelligence technologies, specifically machine learning and deep learning, in combination with IoT technologies that have allowed us to collect both optical element images and information from sensors in the installations, which will act as redundancies or corroborators. Additionally, cloud computing technologies have been utilized for system training tasks. We have successfully connected the information acquisition systems (images and other sensors) with IoT technologies and created training systems in the cloud.
This project has been subsidized by Red.es through the 2020 Call for Grants on technological development based on artificial intelligence and other digital enabling technologies, within the framework of the strategic action on digital economy and society of the State R&D&i Program aimed at addressing societal challenges and the State R&D&i Business Leadership Program (C007/20-ED).