Digital Twins with AI for Predictive Maintenance: A Secondary Data Synthesis

Abstract

Digital Twin (DT) technology, when combined with Artificial Intelligence (AI), has emerged as a transformative approach for predictive maintenance in modern industries. This paper synthesizes secondary evidence from academic literature, industry reports, and international standards (2018–2025) to examine how AI-enhanced digital twins are enabling predictive maintenance across manufacturing, energy, and transportation. Digital twins replicate physical assets virtually, while AI models analyze real-time sensor data to detect anomalies, forecast failures, and optimize maintenance schedules. Using structured analysis of ISO standards, Gartner, McKinsey, and IEEE literature, we map how digital twins integrate with IoT data streams, machine learning models, and maintenance workflows. The study identifies key enabling technologies (IoT, cloud computing, ML/DL), common architectural layers, and documented benefits, such as reduction in unplanned downtime (30–50%) and improved asset life cycles. A conceptual flowchart illustrates the AI–DT predictive maintenance loop, and a table compares sectoral adoption patterns. Findings highlight the convergence between AI analytics and DT simulations, enabling data-driven, condition-based maintenance strategies. Challenges remain in data interoperability, model updating, and cybersecurity. Future work should focus on standardized frameworks and cost-benefit models for broader adoption.