DT-Create – suíte de serviços para especificação de Digital Twins na Indústria 5.0

Abstract

Predictive maintenance in Industry 4.0 and Industry 5.0 scenarios demands intelligent solutions that guarantee the sustainability of the industry in terms of its efficiency, minimizing costs associated with corrective maintenance. One of the challenges of predictive maintenance is making decisions based on data in an agile and assertive way. Connected sensors and equipment produce operational data that favors the use of intelligent processing techniques to enrich information and enable decision-making. Digital Twins (DTs) are a real-time representation of physical machines and generate data that predictive maintenance can use to make assertive and quick decisions. The main contribution of this work is the specification of a suíte of services for specifying DTs, DT-Create, focused on decision support in predictive maintenance. DT-Create is based on the use of intelligent techniques, semantic data processing, and self-adaptation. DT-Create was developed using the Design Science Research (DSR) methodology, through two development cycles and evaluated in two case studies, the first in the textile industry and the second in the metallurgical industry. The results demonstrated the feasibility of using DT-Create in specifying DTs considering the following aspects: (i) collection, storage and intelligent processing of data generated by sensors, (ii) enrichment of information through machine learning and ontologies, (iii) use of intelligent techniques to select predictive models that adhere to the available data set, and (iv) decision support and self-adaptation based on ontological processing.