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Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations
2021
Sensors
Development of predictive maintenance (PdM) solutions is one of the key aspects of Industry 4.0. In recent years, more attention has been paid to data-driven techniques, which use machine learning to monitor the health of an industrial asset. The major issue in the implementation of PdM models is a lack of good quality labelled data. In the paper we present how unsupervised learning using a variational autoencoder may be used to monitor the wear of rolls in a hot strip mill, a part of a
doi:10.3390/s22010291
pmid:35009832
pmcid:PMC8749861
fatcat:c7pvxmofsvaeplisntm4csc7zi