Uncertainty aware anomaly detection to predict errant beam pulses in the SNS accelerator
This work addresses anomaly detection for high-power particle accelerators to enhance operational reliability, but it appears incremental as it applies an existing method to a specific domain.
The paper tackled the problem of predicting errant beam pulses in the SNS accelerator to prevent damage, using an uncertainty-aware Siamese neural network on data from a single monitoring device, with results showing it can improve accelerator operations.
High-power particle accelerators are complex machines with thousands of pieces of equipmentthat are frequently running at the cutting edge of technology. In order to improve the day-to-dayoperations and maximize the delivery of the science, new analytical techniques are being exploredfor anomaly detection, classification, and prognostications. As such, we describe the applicationof an uncertainty aware Machine Learning method, the Siamese neural network model, to predictupcoming errant beam pulses using the data from a single monitoring device. By predicting theupcoming failure, we can stop the accelerator before damage occurs. We describe the acceleratoroperation, related Machine Learning research, the prediction performance required to abort beamwhile maintaining operations, the monitoring device and its data, and the Siamese method andits results. These results show that the researched method can be applied to improve acceleratoroperations.