Digital Twin Technology for Predictive Maintenance inIndustrial Engineering
Keywords:
Digital Twin, Predictive Maintenance, Industrial Engineering, Cyber–Physical Systems, Iot, Condition Monitoring, Smart ManufacturingAbstract
Digital Twin Technology (DTT) has emerged as a transformative approach for predictive maintenance in industrial engineering by enabling real-time synchronization between physical assets and their virtual counterparts. By integrating sensor data, simulation models, and analytics, digital twins allow early fault detection, performance optimization, and lifecycle management of industrial systems. This paper explores the architecture, enabling technologies, maintenance strategies, evaluation metrics, and challenges of digital twin–based predictive maintenance. A structured analysis highlights its advantages over traditional maintenance approaches and discusses future research directions for scalable and intelligent industrial applications.
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Copyright (c) 2026 Arjun Raj, Mohamed Rifaz (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
