Digital Twins for predictive maintenance of Production and Machines: A Comprehensive Review

Title

Digital Twins for predictive maintenance of Production and Machines: A Comprehensive Review

Creator

Pal Pandian, P.; Sasianandham, K.; Katherrine, C.J.

Description

Digital Twin (DT) technology has matured from concept to practice across factories and critical assets, enabling new capabilities in condition-based and predictive maintenance, resilient production planning, and life-cycle decision-making. This review synthesizes current knowledge on DT usage for production and predictive machine maintenance, with concise notes on structural maintenance where SHM (structural health monitoring) increasingly adopts twin concepts. We first consolidate enabling architectures (standards, ontologies, F?FMI-based co-simulation, and hybrid modelling) and then critically survey applications spanning CNC cutting tools, bearings and gearboxes, robotic cells, and production lines. We highlight evidence that hybrid (physics + data-driven) twins reduce remaining useful life (RUL) prediction error compared to single-strategy approaches, improve energy-aware scheduling, and shorten diagnosis-to-action loops. Industrial deployments demonstrate up to 20-30% reduction in unplanned downtime when DT-enabled predictive maintenance is integrated into operational workflows Finally, we surface open challenges - data governance, model validation, uncertainty quantification, interoperability, and work-force adoption - and propose a practical roadmap to make DT predictive maintenance projects production-ready. 2025 IEEE.

Source

Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Digital Thread; Digital Twin; Edge computing; FMI/FMUs; Hybrid Modelling; Industry 4.0; Machine Learning; Ontologies; Predictive Maintenance; Production Systems; Structural Health Monitoring

Coverage

Pal Pandian P., Christ University, School of Engineering and Technology, Department of Mechanical and Automobile Engineering, Bangalore, 560074, India; Sasianandham K., Christ University, School of Engineering and Technology, Department of Mechanical and Automobile Engineering, Bangalore, 560074, India; Katherrine C.J., Christ University, School of Engineering and Technology, Department of Mechanical and Automobile Engineering, Bangalore, 560074, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833159676-7;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICCIST67338.2025.11438591

https://www.scopus.com/pages/publications/105037446173?origin=resultslist