A Service Suite for Specifying Digital Twins for Industry 5.0

TL;DR

This paper introduces DT-Create, a suite of services for specifying digital twins to enhance decision-making in predictive maintenance through intelligent data processing, semantic enrichment, and self-adaptation.

Contribution

It presents a novel service suite for defining digital twins focused on predictive maintenance, integrating intelligent techniques, semantic processing, and self-adaptive features.

Findings

Feasibility demonstrated through case studies.

Effective data collection and processing for DTs.

Enhanced decision support with self-adaptation.

Abstract

One of the challenges of predictive maintenance is making decisions based on data in an agile and assertive way. Connected sensors and operational data favor intelligent processing techniques to enrich information and enable decision-making. Digital Twins (DTs) can be used to process information and support decision-making. 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 suite of services for specifying DTs, called DT-Create, focused on decision support in predictive maintenance. DT-Create suite is based on intelligent techniques, semantic data processing, and self-adaptation. This suite was developed using the Design Science Research (DSR) methodology through two development cycles and evaluated through case studies. The results demonstrate 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.