Skip to content

A digital twin-driven framework for full lifecycle operation and maintenance of smart production lines

Jul 2026 · Digital Signal and Computer Communications · Vol 14294, pp. 142940K - 142940K-6 · 0 citations · 6 references
Engineering

TL;DR

A Digital Twin-Driven Full Lifecycle O and M Framework (DT-FLOM) for smart production lines, encompassing four phases: virtual commissioning, real-time operational monitoring, predictive maintenance and health management, and continuous performance optimization is proposed.

Abstract

The increasing complexity of smart production lines demands intelligent and lifecycle-aware approaches to operation and maintenance (O and M). Digital twin (DT) technology, which creates high-fidelity virtual replicas of physical systems, offers a promising paradigm for real-time monitoring, predictive maintenance, and continuous optimization. However, existing DT applications predominantly address isolated lifecycle phases without providing a unified framework. This paper proposes a Digital Twin-Driven Full Lifecycle O and M Framework (DT-FLOM) for smart production lines, encompassing four phases: virtual commissioning, real-time operational monitoring, predictive maintenance and health management, and continuous performance optimization. The framework is validated through retrospective analysis of publicly reported DT implementations in discrete manufacturing. This study provides a systematic reference for deploying DT-driven O and M strategies in Industry 4.0 smart production environments.

View source

Similar papers

Review Open access Jul 2026

Digital Twin Technology in Electronics Enabling Intelligent Design, Smart Manufacturing, and Predictive Maintenance

Digital Twin (DT) technology has emerged as a transformative paradigm in the electronics industry by enabling the creation of real-time virtual replicas of physical electronic systems, devices, and manufacturing processes. The integration of Internet of Things (IoT) sensors, artificial intelligence (AI), machine learni...

Malloju Dushyanthachary, Edla Chandu, N. Swaroop · 0 citations
Open access Sep 2026

Hybrid modelling–based digital twin for health monitoring in the naval auxiliary industry

This paper presents key insights from the TWIN NAVAUX project, which promotes the integration of digital twin technologies within this sector in the Galicia–North Portugal Euroregion, and outlines the essential building blocks required for successful DT deployment.

Alejandro Vidal-Abad, A. Munín-Doce, Javier Lamas-Vigo · 0 citations
Review Open access 2024

Digital Twin Systems for Next-Generation Product Engineering

Improvements in product quality, operational efficiency, maintenance accuracy, and cost reduction are demonstrated, highlighting Digital Twins as a key enabler of smart manufacturing and Industry 4.0.

Iyengar P. K. · 0 citations
Open access Sep 2026

A Unified Digital Twin Framework for Autonomous Lifecycle Management of Civil Infrastructure: Integrating Cyber–Physical Systems, AI-Driven Predictive Analytics, and Real-Time Optimization

A unified DT framework that couples a cyber-physical sensing and communication layer, an entropy-weighted multi-sensor fusion scheme, a hybrid physics-plus-machine-learning surrogate, extended Kalman assimilation for continuous model updating, and a constrained multi-objective optimisation layer that closes the loop fr...

Mahesh Kumar C. L, L. Pawar, K. M. et al. · 0 citations
Review Aug 2026

Digital twin architectures for industrial pumps: Integrating data perception, reduced-order modeling, and implementation paradigms

Comprehensive analysis indicates that the interdisciplinary integration of multisource data fusion and non-intrusive reduced-order modeling synergistically improves real-time responsiveness while preserving the physical fidelity of fluid-dynamics knowledge.

Wenjie Peng, Wen-Jie Wang, J. Pei et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.