A Service Suite for Specifying Digital Twins for Industry 5.0

2025-11-10

AI Summary

To address predictive maintenance requirements in Industry 5.0, this paper proposes DT-Create—a digital twin modeling service suite enabling real-time virtual mirroring of physical assets and dynamic decision support. Methodologically, it integrates machine learning with ontology-driven knowledge graphs to construct semantically enriched digital twins; introduces an adaptive mechanism that dynamically selects optimal prediction models based on data characteristics and supports online model updating and inference. Following the design science research paradigm, DT-Create unifies multi-source sensor data acquisition, semantic modeling, machine learning, and logical reasoning. Empirical validation demonstrates significant improvements in data interpretability, model adaptation efficiency, and decision autonomy, confirming its engineering feasibility. The core contribution is a novel adaptive twin modeling framework that orchestrates semantic representation, data processing, and model selection in tight synergy.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Knowledge Representation and Reasoning: Ontologies
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Developing service suite for specifying Digital Twins in Industry 5.0
• Enabling agile decision-making for predictive maintenance using sensor data
• Processing operational data through intelligent techniques and self-adaptation

Innovation

• Service suite for specifying Digital Twins
• Uses intelligent techniques and semantic processing
• Provides decision support and self-adaptation features