Speaker
Description
Beam Position Monitors (BPMs) play a critical role in the commissioning and operation of fourth-generation synchrotron light sources, including the High Energy Photon Source (HEPS). By analyzing BPM data, a wide range of beam diagnostics can be performed, while the health and performance of the BPM system itself can also be evaluated. Based on calibration data measured in the laboratory, we have developed a new method for assessing BPM probe performance, and applied it to BPM data acquired during HEPS operation. This enables online monitoring of the BPM system’s performance status under real machine conditions. Furthermore, an autoencoder-based deep-learning approach has been implemented to detect anomalies in BPM electrode signals, allowing early identification of potential failures or performance degradation. These developments have been gradually integrated into the HEPS beam measurement system, and initial experimental results demonstrate their effectiveness and the potential for ensuring BPM data quality and supporting stable machine operation.
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