  Digitalization leads to data transparency for production systems that we can
benefit from with data-driven analysis methods like neural networks. For
example, automated anomaly detection enables saving resources and optimizing
the production. We study using rarely occurring information about labeled
anomalies into Variational Autoencoder neural network structures to overcome
information deficits of supervised and unsupervised approaches. This method
outperforms all other models in terms of accuracy, precision, and recall. We
evaluate the following methods: Principal Component Analysis, Isolation Forest,
Classifying Neural Networks, and Variational Autoencoders on seven time series
datasets to find the best performing detection methods. We extend this idea to
include more infrequently occurring meta information about production
processes. This use of sparse labels, both of anomalies or production data,
allows to harness any additional information available for increasing anomaly
detection performance.
