Realistic sensor simulations for the digital twin

Abstract

Digital twins use actual sensor data to replicate the current state of a plant in a virtual model. They can be used to evaluate the current state, predict future behavior, and thus allow to refine control or optimize operation, enable predictive maintenance as well as detection of anomalies and failures.The model of a digital twin includes models of the components, behaviors and dynamics of a system. With the ability to simulate real scenarios, such models can therefore also be used before a plant is actually implemented, e.g., to predict the actual performance, identify potential issues for the implementation and to develop optimal operation strategy and algorithms. Furthermore, interfaces may be defined, implemented, and tested with such models allowing fast and easy commissioning of the physical implementation.Accurate digital twins therefore also need to include realistic sensor models, considering adverse effects that impact their output signals. The proposed work presents approaches for accurate sensor simulations allowing researchers and industries to assess sensor performance, optimize algorithms, and evaluate system-level integration. We address Frequency Modulated Continuous Wave (FMCW) radar sensors and time-of-flight cameras as examples for far-field sensors and capacitive sensors as an example for near-field sensors. The approaches can be transferred to other sensors, e.g., ultrasound sensors, LiDAR sensors and inductive or magnetic sensors so that a wide range of industrial sensors can be covered.The proposed simulations are benchmarked with different tests, including real-world experiments and compared with the corresponding real sensors.