Cross-domain digital twin architecture for predictive maintenance via machine learning and Large Language Models

Key Points

• Predictive maintenance achieved enhanced efficiency with a digital twin architecture, enabling proactive monitoring and issue resolution.
• Leveraging machine learning algorithms, the architecture processes vast amounts of real-time data effectively, highlighting critical system performance metrics.
• Assessment using large language models improves predictive capabilities, facilitating better decision-making for maintenance activities and operational reliability.
• This approach supports advanced maintenance strategies, illustrating the potential benefits of continued integration across diverse domains.