Anomaly detection using autoencoder reconstruction upon industrial motors

HIGHLIGHTS

• who: Sean Givnan and collaborators from the School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street have published the paper: Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors, in the Journal: Sensors 2022, 3166 of /2022/
• what: The authors propose a machine learning (ML) approach to model normal working operations and detect anomalies. Taking into account that anomalous data are a minority, as faults only occur sporadically, the approach taken in this paper focuses on anomaly detection rather than classification to predict when faults might occur. The approach utilises stacked autoencoders (SAEs . . .
  

   

  

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