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February 2, 2026Open Access

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

Key Points

To develop a system for monitoring temperature anomalies in power supplies using infrared imaging and machine learning.
Utilized infrared imaging for temperature monitoring of power supplies.
Applied various machine learning algorithms to the imaging data.
Designed an anomaly detection system to identify critical temperature changes.
Achieved reliable detection of temperature anomalies in power supplies.
Enabled early identification of potential issues before they escalate.
Improved operational efficiency and uptime of accelerator facilities.

Abstract

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.

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

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