ABSTRACT The concept of predictive maintenance in advanced manufacturing systems is crucial from the point of view of resource efficiency in the era of high competitiveness forced by energy transformation in the digital economy. Against the backdrop of sustainability and the opportunities a data cooperative offers, the combination of predictive maintenance, sustainable data use and data cooperatives could not only enable the private sector, but also the public sector to harmonise innovation, efficiency and sustainability to reach globally significant political goals such as the UN's 17 Sustainable Development Goals with the help of technology. The aim is to create social added value when understanding data as a resource, sharing it and using it specifically for sustainable change. In the subject literature and among application solutions, one can identify numerous forms of a taxonomy of various variables describing explained and explanatory variables. Aimed at improving the efficiency of production processes, taxonomy is of fundamental importance. It mainly affects the effectiveness of event prediction used to identification components based on taxonomy description which have influence for monitor the efficiency of production systems. The article provides a review of the literature on the predictive maintenance of production systems categorisation. The cognitive gap in the presented results identifies main thematic areas of predictive maintenance. The study was conducted on 54 articles from the Scopus database and 37 articles from the Web of Science database. Within the applied methodology the Prisma model was used.
Schachtner et al. (Sun,) studied this question.