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Related Experiment Video

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Surrogate Model Development for Digital Experiments in Welding

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Wrapper Functions for Integrating Mathematical Models into Digital Twin Event Processing.

Reiner Jedermann¹, Walter Lang¹

¹IMSAS-Institute for Microsensors, -Actuators and -Systems, University of Bremen, 28359 Bremen, Germany.

Sensors (Basel, Switzerland)

|October 27, 2022

Summary

This study introduces generic wrapper functions to simplify integrating complex mathematical models into digital twins (DTs). This enables real-time sensor data analysis and prediction across various applications.

Keywords:

Apache Kafka cool chain digital twins event processing intelligent container real-time models wrapper functions

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Area of Science:

Engineering
Computer Science
Data Science

Background:

Analog sensors necessitate intricate mathematical models for data interpretation.
Digital twins (DTs) offer real-time sensor data visualization but lack standardized methods for integrating mathematical models and algorithms.

Purpose of the Study:

To develop a generic solution for integrating multiple mathematical models into a digital twin framework.
To simplify the connection of model inputs and outputs to streaming platforms for enhanced data analysis.

Main Methods:

Development of generic "wrapper functions" to facilitate model integration into DTs.
Implementation of various model linking structures: simultaneous, sequential, and predictive processing.
Adaptation of wrapper functions for diverse application fields and microservices.

Main Results:

Wrapper functions significantly simplify the integration of complex models into DTs with minimal software modification.
Demonstrated successful conversion of a system of multiple models into a DT for banana fruit quality monitoring.
Enabled testing of "what-if" scenarios and prediction of future system behavior.

Conclusions:

The proposed wrapper functions offer a versatile and generic approach for integrating diverse mathematical models into digital twins.
This facilitates advanced real-time data analysis, prediction, and scenario testing across multiple application domains.