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Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things
Enabled by the increasing availability of sensor data monitored from production machinery, condition monitoring and predictive maintenance methods are key pillars for an efficient and robust manufacturing production cycle in the Industrial Internet of Things.
Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines
We demonstrate the effectiveness of the proposed method by employing two rotating machine datasets and the quality of the automatically learned features is compared with a set of handcrafted features by training an Isolation Forest model on either of these two sets.
Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series
Forecasting of time series in continuous systems becomes an increasingly relevant task due to recent developments in IoT and 5G.
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