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This study models an ultrasonic sensor for predicting water levels, a key step towards creating a digital twin. This approach enhances sensor predictability and supports Industry 4.0 integration.

Keywords:
digital twinhyperparameter optimizationkernel selectionsupport vector regressionvirtual sensor

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Area of Science:

  • Mechatronics Engineering
  • Sensor Systems Technology
  • Digital Twin Technology

Background:

  • The Pepperl+Fuchs 3RG6232-3JS00-PF ultrasonic sensor monitors water levels in the Festo MPS-PA Didactic System.
  • Accurate sensor modeling is crucial for developing digital twins and predictive maintenance strategies.
  • Transitioning to Industry 4.0 requires robust digital frameworks for physical systems.

Purpose of the Study:

  • To develop a novel predictive model for the Pepperl+Fuchs 3RG6232-3JS00-PF ultrasonic sensor.
  • To establish the initial phase of a digital twin for the physical sensor.
  • To enhance sensor response observation and life cycle forecasting for maintenance.

Main Methods:

  • Utilized the Festo MPS-PA Compact Didactic System for data acquisition (DAQ).
  • Employed support vector regression (SVR) for data preprocessing and model training.
  • Implemented hyperparameter optimization and predicted the rate of change (RoC) of water level for estimation.

Main Results:

  • Achieved a promising 6.99% error percentage in water level prediction.
  • Successfully modeled the sensor's output behavior.
  • Demonstrated a robust approach for sensor data modeling and digital twin creation.

Conclusions:

  • The developed sensor model is a foundational step towards a comprehensive digital twin of the Festo MPS-PA System.
  • This research advances the application of digital twins in mechatronics and sensor systems.
  • The approach enhances sensor system efficiency and predictability, aligning with Industry 4.0 principles.