Technician 5.0: A Hybrid Framework Integrating Chatbot, Digital Twin, and Machine Learning for Human-Centric Predictive Maintenance in Industry 5.0

The evolution from Industry 4.0 to Industry 5.0 emphasizes the collaboration between humans and advanced technologies to create a human-centric, resilient, and sustainable industrial environment. Predictive maintenance plays a pivotal role in this transition by leveraging real-time data, artificial intelligence, and cyber-physical systems to enhance the overall equipment effectiveness and reduce downtime. This chapter proposes a novel triplet-based architecture for predictive maintenance tailored to continuous production systems in the mining industry. The architecture integrates three synergistic components: (i) machine learning for remaining useful life prediction, (ii) digital twins for system-level simulation and what-if analysis, and (iii) a conversational AI interface enabling intuitive interaction between operators and the intelligent system. A key contribution of this work lies in the operationalization of the Technician 5.0 concept to enable an augmented human actor capable of querying, interpreting, and acting upon AI-driven recommendations through natural language interfaces. The proposed system was validated using real sensor and fault from a jaw crusher, demonstrating its capacity to support multiscale diagnostics, simulation-based planning, and explainable maintenance decision-making. The chapter highlights how this integrated approach transforms predictive maintenance from a systematic proactive tool into a reactive, interactive, and human-aligned framework, aligned with Industry 5.0 principles. The proposed system offers practical pathways for deployment and future extension to production and quality domains.