Industry focused in data collection: how industry 4.0 is handled by big data

Abstract

The paper aims to organize and structure data collected and associated to technologies that powers the abroad concept of Industry 4.0. It starts with the historic evolution of industry, separated by date landmarks and approaches the last transition between 3.0 to 4.0. Apart from the differences between industry models, production data stats show a huge and important transformation in the amount of data related to manufacturing and how that knowledge is processed. The paper also aims to put on debate the lack of solutions regarding the knowledge extraction of data from machines and systems, needed for data analytics. Approaches with cyber-physical systems, machine learning, virtual environments, Industrial IoT 1 and augmented reality, in an industrial scale, are some of the strategies to power the reading and interpretation of data, in order to promote industrial efficiency. Real context industrial applications are taken into account in order to state the importance of collected data in the efficiency of a production process. Exploring technologies and concepts to improve digital twins systems, perception and perceived systems as well as maintenance processes are some of the explored implemented strategies that make Industry 4.0. Some possible strategies are presented, as well as the transition for Industry 5.0.