3 Sensing the real world
This chapter covers
- The role of sensors in updating a digital twin
- Building a complete sensing system for real-world applications
- Processing sensor data
- Managing sensors at scale
Digital twins depend on maintaining an accurate, real-time connection to reality. While 3D models show you what a system looked like when designed, a true digital twin evolves continuously with its physical counterpart through systematic sensing of the physical world. Sensors bridge the physical and digital worlds, continuously capturing changing conditions such as temperature fluctuations, equipment vibrations, fluid flows, and human activities and converting them into data streams that keep digital twins synchronized with reality.
What distinguishes a digital twin from traditional monitoring systems is both purpose and scale. Traditional systems might check temperature hourly to prevent overheating, while digital twins need comprehensive data streams that enable predictive capabilities through data density for machine learning, simulation capabilities using rich historical data for scenario modeling and what-if analysis, real-time synchronization with continuous updates keeping the digital model current, and contextual understanding through multiple data streams that reveal system relationships. As digital twins mature from descriptive through predictive to autonomous capabilities, demands for rich, timely data increase dramatically.