学术文献库

Accelerators produce too many signals for a small operations team to monitor in real time. In addition, many of these signals are only interpretable by subject matter experts with years of experience. As a result, changes in accelerator performance can require time-intensive consultations with experts to identify the underlying problem. Herein, we focus on a particular anomaly detection task for radio-frequency (RF) stations at the Linac Coherent Light Source (LCLS). The existing RF station diagnostics are bandwidth limited, resulting in slow, unreliable signals. As a result, anomaly detection is currently a manual process. We propose a beam-based method, identifying changes in the accelerator status using shot-to-shot data from the beam position monitoring system; by comparing the beam-based anomalies to data from RF stations, we identify the source of the change. We find that our proposed method can be fully automated while identifying more events with fewer false positives than the RF station diagnostics alone. Our automated fault identification system has been used to create a new data set for investigating the interaction between the RF stations and accelerator performance.