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Journals / Journal of Metaverse / 2026 / Volume: 5 Issue: 1

Human-Centric IoT-Driven Digital Twins in Predictive Maintenance for Optimizing Industry 5.0

Özlem Sabuncu(YAKIN DOĞU ÜNİVERSİTESİ)0000-0002-9159-7791
Bülent Bilgehan(YAKIN DOĞU ÜNİVERSİTESİ)0000-0003-1615-6766
Pages
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Publication Date
2025-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

These improvements contribute to enhanced operational efficiency and better human-machine collaboration by minimizing unnecessary interventions and optimizing resource allocation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Predictive maintenance now heavily relies on digital twins and the Internet of Things (IoT), which allow industrial assets to be monitored and decisions made in real time.
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Key Findings & Data:However, adding human components to conventional optimization processes creates new difficulties as Industry 5.0 moves toward human-centric systems.
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Conclusion & Impact:These improvements contribute to enhanced operational efficiency and better human-machine collaboration by minimizing unnecessary interventions and optimizing resource allocation.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Industry 5.0 · Digital twin · IoT

Abstract

Predictive maintenance now heavily relies on digital twins and the Internet of Things (IoT), which allow industrial assets to be monitored and decisions made in real time. However, adding human components to conventional optimization processes creates new difficulties as Industry 5.0 moves toward human-centric systems. Existing frameworks frequently disregard human preferences, intuition, and safety considerations, which makes human operators distrustful and unwilling to accept them. To enable predictive maintenance, this paper presents a novel multi-objective optimization framework that incorporates human feedback into IoT-driven digital twins. The framework uses an enhanced particle swarm optimization (PSO) algorithm to reconcile competing goals, including maintaining operator safety, optimizing asset reliability, and minimizing maintenance costs. Furthermore, maintenance tasks are adaptively scheduled using built-in reinforcement learning (RL) and optimized model parameters are fine-tuned for improved predictive accuracy using Bayesian optimization. The latter is based on real-time operational data. In addition to promoting a safer working environment, the suggested approach shows a significant reduction in unplanned downtime and maintenance costs. This research contributes to the development of more resilient, adaptive, and collaborative industrial systems by aligning with the human-centric principles of Industry 5.0. The proposed model was tested using the maintenance duration and achieved an improvement of 10 to 100 hours. The model was further compared with the PSO algorithm, demonstrating its superiority with a 7.5% reduction in total maintenance cost and a 6.3% decrease in total downtime. These improvements contribute to enhanced operational efficiency and better human-machine collaboration by minimizing unnecessary interventions and optimizing resource allocation.

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Cite This Article

Sabuncu, Ö., Bilgehan, B. (2025). Human-Centric IoT-Driven Digital Twins in Predictive Maintenance for Optimizing Industry 5.0. Journal of Metaverse, Volume: 5 Issue: 1. https://doi.org/10.57019/jmv.1596909

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