Abstract
During the last decades numerous innovations and inventions have been presented under the framework of Industry 4.0. The ultimate goal of the latest industrial revolution is the digitalization of modern manufacturing systems. Therefore, several techniques and technologies have been investigated by the academia and the industrial practitioners, such as Edge Computing. The above-mentioned paradigm adheres to the distributed computing paradigm, which is an analogous to the trend of decentralization of manufacturing systems. However, there are still several challenges to be addressed in the integration of such decentralized computing systems in real-life industries. Therefore, in this paper, the design and preliminary development of an Edge-computing platform is proposed, in order to distribute the computational load to the nodes and by extension to enable the utilization of machine learning techniques for the calculation of Remaining Useful Life (RUL) of critical machine tool components. Moreover, the proposed framework promotes the utilization of 5G cellular networks, in order to take advantage of the ultra-low latency and increased bandwidth offered by this technology.
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