Erik Johannes Husom
ABSTRACT
We present an automated Machine Learning (ML) tool designed as a continual learning pipeline to adapt to evolving data streams in the Industrial Internet of Things (IIoT). This tool creates ML experiences, starting with training a neural network model. It then iteratively refines this model using fresh data while judiciously replaying pertinent historical data segments. When applied to IIoT sensor data, our tool ensures sustained ML performance amid evolving data dynamics while preventing the undue accumulation of obsolete sensor data. We have successfully assessed our tool across three industrial datasets and affirm its efficacy in dynamic knowledge retention and adaptation.
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- Sagar Sen, Simon Myklebust Nielsen, Erik Johannes Husom, Arda Goknil, Simeon Tverdal, and Leonardo Sastoque Pinilla. 2023. Replay-driven continual learning for the industrial internet of things. In CAIN’23. IEEE, 43–55.Google Scholar
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