Digital Twin-Driven Machine Performance and Reliability: Replication, Prediction and Front Running Simulation

Dear Colleagues,

In the era of intelligent systems, Industry 4.0, and AI, machine performance and reliability are no longer retrospective metrics but dynamic, forward-looking capabilities. This Special Issue seeks original contributions that explore how Digital Twin technology—guided by the foundational framework developed by Dr. Michael Grieves—can be used to enhance the reliability of machines through replication of physical assets, predictive analytics, and Front Running Simulation (FRS).

Digital Twins, as defined by Grieves, serve as virtual counterparts of physical systems, continuously updated with real-time and historical data from their physical twins. This replication enables both descriptive and prescriptive insights. When coupled with advanced modeling, AI/ML, and physics-based simulations, Digital Twins become tools not only for understanding the current operational state but also for predicting failure modes and evaluating corrective actions before implementation.

Front Running Simulation expands the predictive role of Digital Twins by allowing decision-makers to simulate future scenarios, proactively determine optimal interventions, and thereby prevent reliability degradation before it occurs. This capability represents a paradigm shift from reactive maintenance to proactive, resource-optimized decision-making.

Topics of interest include (but are not limited to) the following:

• Frameworks for Digital Twin-enabled machine health monitoring and reliability;
• Applications of Front Running Simulation in industrial reliability management;
• Integration of AI/ML and physics-based modeling in predictive Digital Twins;
• Use of real-time data streams to improve fault detection and failure prediction;
• Case studies and industrial implementations of reliability-driven Digital Twins;
• Digital Twin architectures for cyber–physical system replication;
• Methodologies for validating and verifying predictive simulations;
• Economic and operational benefits of Digital Twin-based reliability management.

Prof. Dr. Michael Grieves
Guest Editor

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• factory health prediction
• digital twin-enabled machine
• industrial reliability management
• health monitoring
• reliability-driven digital Twins