Digital Twins in Industrial Applications: Concepts, Mathematical Modeling, and Use Cases

TL;DR

This paper reviews the concepts, mathematical models, and industrial use cases of Digital Twins, emphasizing their role in predictive maintenance, fault diagnosis, and process optimization through advanced modeling techniques.

Contribution

It provides a comprehensive overview of the mathematical foundations and hybrid modeling approaches for Digital Twins in industrial contexts, including PINNs and implementation strategies.

Findings

Digital Twins enhance predictive maintenance and fault diagnosis.

Hybrid modeling techniques improve accuracy of industrial simulations.

The paper outlines future research directions in Digital Twin technology.

Abstract

Digital Twins (DTs) are virtual representations of physical systems synchronized in real time through Internet of Things (IoT) sensors and computational models. In industrial applications, DTs enable predictive maintenance, fault diagnosis, and process optimization. This paper explores the mathematical foundations of DTs, hybrid modeling techniques, including Physics Informed Neural Networks (PINNs), and their implementation in industrial scenarios. We present key applications, computational tools, and future research directions.