Measurement and Sensor Technology in Production Engineering

The Professorship of Measurement and Sensor Technology in Production Engineering focuses on developing intelligent sensing systems by integrating autonomous wireless sensor nodes, embedded electronics, and virtual sensing models enhanced by artificial intelligence. The research addresses the design of self-powered embedded sensor nodes capable of operating independently over extended periods. Power supply solutions include batteries or supercapacitors, which may be charged via energy harvesting systems or wireless charging, adaptable to both stationary and dynamic environments. These wireless sensor nodes incorporate low-power microcontrollers, multi-modal sensors, and efficient communication protocols, combined with on-node intelligence for real-time decision-making. AI models, such as lightweight neural networks and embedded machine learning, are deployed directly on the nodes to enable event detection, anomaly recognition, and adaptive sensing under strict energy and memory constraints. 

A major research direction focuses on designing AI-driven virtual sensors capable of estimating complex or unmeasurable physical states using limited yet informative sensor data. These models combine physics-based simulations with data-driven inference to replace costly sensors or access information that is difficult to measure directly. At higher processing levels, large language models (LLMs) and transformer-based architectures are employed to interpret sensor data streams, extract semantic insights, and support system configuration and adaptation. By embedding AI throughout the sensing hierarchy, the research aims to enable scalable and intelligent sensor networks with applications in manufacturing, structural health monitoring, smart infrastructure, and autonomous robotics. The overarching vision is to advance from passive sensing to systems capable of understanding, adapting, and interacting using human-interpretable, AI-augmented intelligence.