Bayesian Autoencoders for Drift Detection in Industrial Environments

@article{Yong2020BayesianAF,
  title={Bayesian Autoencoders for Drift Detection in Industrial Environments},
  author={Bang Xiang Yong and Yasmin Fathy and Alexandra Brintrup},
  journal={2020 IEEE International Workshop on Metrology for Industry 4.0 \& IoT},
  year={2020},
  pages={627-631}
}
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… 

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