  The application of machine learning techniques for anomaly detection in
particle accelerators has gained popularity in recent years. These efforts have
ranged from the analysis of quenches in radio frequency cavities and
superconducting magnets to anomalous beam position monitors, and even losses in
rings. Using machine learning for anomaly detection can be challenging owing to
the inherent imbalance in the amount of data collected during normal operations
as compared to during faults. Additionally, the data are not always labeled and
therefore supervised learning is not possible. Autoencoders, neural networks
that form a compressed representation and reconstruction of the input data, are
a useful tool for such situations. Here we explore the use of autoencoder
reconstruction analysis for the prediction of magnet faults in the Advanced
Photon Source (APS) storage ring at Argonne National Laboratory.
