Collecting and pre-processing data for Industry 4.0 implementation using hydraulic press

Abstract

More and more activities are being undertaken to implement the Industry 4.0 concept in industrial practice. One of the biggest challenges is the digitization of existing industrial systems and heavy industry operations, where there is huge potential for optimizing and managing these processes more efficiently, but this requires collecting large amounts of data, understanding, and evaluating it so that we can add value back based on it. This paper focuses on the collection, local pre-processing of data, and its subsequent transfer to the cloud from an industrial hydraulic press to create a comprehensive dataset that forms the basis for further digitization of the operation. The novelty lies mainly in the process of data collection and pre-processing in the framework of edge computing of large amounts of data. In the data pre-processing, data normalization methods are applied, which allow the data to be logically sorted, tagged, and linked, which also allows the data to be efficiently compressed, thus, dynamically creating a complex dataset for later use in the process digitization.