A Systematic Review of Digital Twin-Driven Predictive Maintenance in Industrial Engineering: Taxonomy, Architectural Elements, and Future Research Directions
🤖 AI Summary
Rising complexity in industrial systems undermines the reliability and safety assurance capabilities of conventional maintenance paradigms.
Method: This study proposes a digital twin–driven predictive maintenance (PdM) framework, featuring a hierarchical technical architecture and an AI-empowered industrial application taxonomy. It integrates IoT sensing, machine learning, and real-time big data analytics to enable sensor-driven adaptive virtual twin modeling and dynamic performance optimization.
Contribution: First, it systematically traces the evolutionary trajectory of digital twins in PdM—from static one-to-one mapping to AI-enabled self-learning models—and clarifies middleware requirements and representative deployment patterns. Second, it identifies critical enabling technologies and implementation pathways for trustworthy intelligent industrial ecosystems. Third, it highlights key future research directions, including model interpretability, edge-cloud collaboration, and cross-domain interoperability. The framework bridges theoretical advancement with practical industrial applicability, offering a scalable foundation for next-generation smart maintenance systems.
Application Category
📝 Abstract
With the increasing complexity of industrial systems, there is a pressing need for predictive maintenance to avoid costly downtime and disastrous outcomes that could be life-threatening in certain domains. With the growing popularity of the Internet of Things, Artificial Intelligence, machine learning, and real-time big data analytics, there is a unique opportunity for efficient predictive maintenance to forecast equipment failures for real-time intervention and optimize maintenance actions, as traditional reactive and preventive maintenance practices are often inadequate to meet the requirements for the industry to provide quality-of-services of operations. Central to this evolution is digital twin technology, an adaptive virtual replica that continuously monitors and integrates sensor data to simulate and improve asset performance. Despite remarkable progress in digital twin implementations, such as considering DT in predictive maintenance for industrial engineering. This paper aims to address this void. We perform a retrospective analysis of the temporal evolution of the digital twin in predictive maintenance for industrial engineering to capture the applications, middleware, and technological requirements that led to the development of the digital twin from its inception to the AI-enabled digital twin and its self-learning models. We provide a layered architecture of the digital twin technology, as well as a taxonomy of the technology-enabled industrial engineering applications systems, middleware, and the used Artificial Intelligence algorithms. We provide insights into these systems for the realization of a trustworthy and efficient smart digital-twin industrial engineering ecosystem. We discuss future research directions in digital twin for predictive maintenance in industrial engineering.
Problem
Research questions and friction points this paper is trying to address.
Systematically reviewing digital twin applications in industrial predictive maintenance
Analyzing architectural elements and taxonomy of digital twin technology
Identifying future research directions for digital twin-driven maintenance systems
Innovation
Methods, ideas, or system contributions that make the work stand out.
Digital twin creates adaptive virtual replicas
AI enables self-learning predictive maintenance models
Layered architecture integrates real-time sensor data
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