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Machine-Learning Models on the Edge to reduce Data Volume in Wide-Area Networks between various Production Sites

The availability of vast amounts of data in automated production systems reveals the potential for data-driven improvements. Jointly using this data across different sites or even across different companies will further increase the validity of data-driven models. However, the throughput in wide area networks is limited, limiting the large-scale transmission of data. Therefore, this paper proposes a data reduction approach to reduce network load based on regression and time series models directly on the shop floor. The machine-learning models are used to predict the signals of the automated production system to prevent the transmission of extensive raw data. It is shown that the approach reduces the network load significantly while still ensuring the fulfillment of the real-time control tasks of the programmable logic controller at any time. Thereby, the reduction of the data is dependent on the error of the reconstructed data that can be tolerated.