  This paper provides the first comprehensive evaluation and analysis of modern
(deep-learning) unsupervised anomaly detection methods for chemical process
data. We focus on the Tennessee Eastman process dataset, which has been a
standard litmus test to benchmark anomaly detection methods for nearly three
decades. Our extensive study will facilitate choosing appropriate anomaly
detection methods in industrial applications.
