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1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Machine learning-enhanced infrared imaging for temperature anomaly detection in power supplies

THPM103
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Magpie (TWTC)

Exhibiton Hall A _Magpie

TWTC

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

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.

Region represented America
Paper preparation format LaTeX

Author

Osama Mohsen (Argonne National Laboratory)

Co-authors

Michael Borland (Argonne National Laboratory) Yine Sun (Argonne National Laboratory)

Presentation materials

There are no materials yet.