  Unsupervised anomaly detection (AD) is a major topic in the field of
Cyber-Physical Production Systems (CPPSs). A closely related concern is
dimensionality reduction (DR) which is: 1) often used as a preprocessing step
in an AD solution, 2) a sort of AD, if a measure of observation conformity to
the learned data manifold is provided.
  We argue that the two aspects can be complementary in a CPPS anomaly
detection solution. In this work, we focus on the nonlinear autoencoder (AE) as
a DR/AD approach. The contribution of this work is: 1) we examine the
suitability of AE reconstruction error as an AD decision criterion in CPPS
data. 2) we analyze its relation to a potential second-phase AD approach in the
AE latent space 3) we evaluate the performance of the approach on three
real-world datasets. Moreover, the approach outperforms state-of-the-art
techniques, alongside a relatively simple and straightforward application.
