Speaker
Osama Mohsen
(Argonne National Laboratory)
Description
The performance of particle accelerators is critically dependent on the reliability of their power supplies, which can number in the thousands in many facilities. In this work, we present a method for monitoring temperature anomalies in power supplies using infrared (IR) imaging. By applying various machine learning algorithms to the IR imaging data, we develop a reliable anomaly detection system that can improve the uptime of accelerator facilities. This approach enables early detection of potential issues, facilitating predictive maintenance and enhancing overall operational efficiency.
Funding Agency
Work supported by the U. S. Department of Energy, Office of Science, under Contract No. DE-AC02-06CH11357.
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Author
Osama Mohsen
(Argonne National Laboratory)
Co-authors
Ihar Lobach
(Brookhaven National Laboratory)
Michael Borland
(Argonne National Laboratory)
Yine Sun
(Argonne National Laboratory)
