A Review of Digital Twinning for Rotating Machinery

Area of Science:

• Engineering
• Computer Science
• Manufacturing Technology

Background:

• Industry 4.0 and 5.0 emphasize smart manufacturing and human-centric industrial processes.
• Digital Twins (DTs) are virtual representations of physical assets, crucial for modern industrial applications.
• Understanding DTs' role in transmission and industrial machinery is vital for optimizing performance and lifecycle.

Purpose of the Study:

• To review definitions, modalities, applications, and performance of digital twins in transmission and industrial machinery.
• To contextualize DTs within Industry 4.0 and Industry 5.0 frameworks.
• To analyze the integration of sensors, machine learning, and data-driven methods for DTs.

Main Methods:

• Literature review of digital twin concepts and applications in industrial machinery.
• Analysis of DT adoption, performance metrics, and validation methods.
• Synthesis of research on integrating operational data, sensors, and AI/ML for DTs.

Main Results:

• Multiple definitions and interpretations of DTs exist within the industrial machinery domain.
• DTs show significant adoption and performance benefits for rotating and industrial machinery throughout their lifecycle.
• Effective integration of sensors and advanced data-driven methods (ML/DL) enhances DT capabilities.

Conclusions:

• Digital twins are pivotal for optimizing lifetime design, manufacturing, and decision-making in industrial machinery.
• Even incomplete or limited digital twins offer substantial value.
• The review highlights the critical role of data integration and advanced analytics in maximizing DT benefits.