  Autoencoders are unsupervised models which have been used for detecting
anomalies in multi-sensor environments. A typical use includes training a
predictive model with data from sensors operating under normal conditions and
using the model to detect anomalies. Anomalies can come either from real
changes in the environment (real drift) or from faulty sensory devices (virtual
drift); however, the use of Autoencoders to distinguish between different
anomalies has not yet been considered. To this end, we first propose the
development of Bayesian Autoencoders to quantify epistemic and aleatoric
uncertainties. We then test the Bayesian Autoencoder using a real-world
industrial dataset for hydraulic condition monitoring. The system is injected
with noise and drifts, and we have found the epistemic uncertainty to be less
sensitive to sensor perturbations as compared to the reconstruction loss. By
observing the reconstructed signals with the uncertainties, we gain
interpretable insights, and these uncertainties offer a potential avenue for
distinguishing real and virtual drifts.
