Resilient VAE: Unsupervised Anomaly Detection at the SLAC Linac Coherent
  Light Source
Ryan Humble William Colocho Finn O'Shea Daniel Ratner Eric Darve
Significant advances in utilizing deep learning for anomaly detection have
been made in recent years. However, these methods largely assume the existence
of a normal training set (i.e., uncontaminated by anomalies) or even a
completely labeled training set. In many complex engineering systems, such as
particle accelerators, labels are sparse and expensive; in order to perform
anomaly detection in these cases, we must drop these assumptions and utilize a
completely unsupervised method. This paper introduces the Resilient Variational
Autoencoder (ResVAE), a deep generative model specifically designed for anomaly
detection. ResVAE exhibits resilience to anomalies present in the training data
and provides feature-level anomaly attribution. During the training process,
ResVAE learns the anomaly probability for each sample as well as each
individual feature, utilizing these probabilities to effectively disregard
anomalous examples in the training data. We apply our proposed method to detect
anomalies in the accelerator status at the SLAC Linac Coherent Light Source
(LCLS). By utilizing shot-to-shot data from the beam position monitoring
system, we demonstrate the exceptional capability of ResVAE in identifying
various types of anomalies that are visible in the accelerator.