Intelligent Robots and Digital Twin Technology for Fault Diagnosis under Industry 5.0

Intelligent robots are central to modern industry, enabling precision manufacturing, flexible production, and human–robot collaboration. With the rise of Industry 5.0, increasing system complexity introduces significant challenges in ensuring reliability and safety. Traditional fault diagnosis methods struggle with high-dimensional, nonlinear data, while data-driven and AI-based approaches offer improved capabilities. Digital twin technology further enhances real-time monitoring and predictive maintenance through virtual–physical synchronization. However, challenges remain in multi-physics modeling, data fusion, model generalization, and ensuring real-time, secure, and scalable deployment in practical environments.This Research Topic aims to bring together cutting-edge research on intelligent robots and digital twin technologies for fault diagnosis within the framework of Industry 5.0. We seek contributions that address theoretical foundations, algorithmic developments, system integration, and real-world applications. Emphasis is placed on innovative methods that combine robotics, artificial intelligence, control theory, and digital twin modeling to enhance the reliability and autonomy of intelligent robotic systems.This Research Topic welcomes original research articles, reviews, and perspectives that advance the state of the art in intelligent robotic fault diagnosis. Contributions with rigorous validation through simulations, experiments, or industrial case studies are highly encouraged. Topics of interest include, but are not limited to:1. Design and modeling of digital twin systems for intelligent robots2. Data-driven and hybrid fault diagnosis methods for robotic systems3. Deep learning and transfer learning for fault detection and prognosis4. Multisensor data fusion and signal processing for robot health monitoring5. Real-time monitoring and predictive maintenance using digital twins6. Fault diagnosis under small-sample and imbalanced data conditions7. Intelligent control and fault-tolerant strategies for robotic systems8. Simulation-to-real transfer and deployment of diagnostic models9. Cybersecurity, reliability, and robustness in digital twin-enabled robots10. Industrial applications and case studies in smart manufacturing environments Topic editors: Quanbo Lu, CHAOMIN LUO, Bo Zhang, Zekun Zhou Source: https://www.frontiersin.org/research-topics/81680/intelligent-robots-and-digital-twin-technology-for-fault-diagnosis-under-industry-50